Effect of objective function on multi-objective inverse planning of radiation therapy

International Nuclear Information System (INIS)

Li Guoli; Wu Yican; Song Gang; Wang Shifang

2006-01-01

There are two kinds of objective functions in radiotherapy inverse planning: dose distribution-based and Dose-Volume Histogram (DVH)-based functions. The treatment planning in our days is still a trial and error process because the multi-objective problem is solved by transforming it into a single objective problem using a specific set of weights for each object. This work investigates the problem of objective function setting based on Pareto multi-optimization theory, and compares the effect on multi-objective inverse planning of those two kinds of objective functions including calculation time, converge speed, etc. The basis of objective function setting on inverse planning is discussed. (authors)

Multichannel Dynamic Fourier-Transform IR Spectrometer

Science.gov (United States)

Balashov, A. A.; Vaguine, V. A.; Golyak, Il. S.; Morozov, A. N.; Khorokhorin, A. I.

2017-09-01

A design of a multichannel continuous scan Fourier-transform IR spectrometer for simultaneous recording and analysis of the spectral characteristics of several objects is proposed. For implementing the design, a multi-probe fiber is used, constructed from several optical fibers connected into a single optical connector and attached at the output of the interferometer. The Fourier-transform spectrometer is used as a signal modulator. Each fiber is individually mated with an investigated sample and a dedicated radiation detector. For the developed system, the radiation intensity of the spectrometer is calculated from the condition of the minimum spectral resolution and parameters of the optical fibers. Using the proposed design, emission spectra of a gas-discharge neon lamp have been recorded using a single fiber 1 mm in diameter with a numerical aperture NA = 0.22.

Wireless Sensor Network Optimization: Multi-Objective Paradigm.

Science.gov (United States)

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-07-20

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks.

Wireless Sensor Network Optimization: Multi-Objective Paradigm

Science.gov (United States)

Iqbal, Muhammad; Naeem, Muhammad; Anpalagan, Alagan; Ahmed, Ashfaq; Azam, Muhammad

2015-01-01

Optimization problems relating to wireless sensor network planning, design, deployment and operation often give rise to multi-objective optimization formulations where multiple desirable objectives compete with each other and the decision maker has to select one of the tradeoff solutions. These multiple objectives may or may not conflict with each other. Keeping in view the nature of the application, the sensing scenario and input/output of the problem, the type of optimization problem changes. To address different nature of optimization problems relating to wireless sensor network design, deployment, operation, planing and placement, there exist a plethora of optimization solution types. We review and analyze different desirable objectives to show whether they conflict with each other, support each other or they are design dependent. We also present a generic multi-objective optimization problem relating to wireless sensor network which consists of input variables, required output, objectives and constraints. A list of constraints is also presented to give an overview of different constraints which are considered while formulating the optimization problems in wireless sensor networks. Keeping in view the multi facet coverage of this article relating to multi-objective optimization, this will open up new avenues of research in the area of multi-objective optimization relating to wireless sensor networks. PMID:26205271

A multi-passage spectrometer for charge-state separation at MLLTRAP

Energy Technology Data Exchange (ETDEWEB)

Weber, Christine; Gartzke, Eva; Habs, Dietrich; Krug, Kevin; Szerypo, Jerzy; Thirolf, Peter [Fakultaet fuer Physik, LMU - Muenchen (Germany); Kolhinen, Veli [Department of Physics, University of Jyvaeskylae (Finland)

2010-07-01

MLLTRAP is a Penning trap mass spectrometer facility which is currently being commissioned at the Maier-Leibnitz Tandem Accelerator Laboratory in Garching. Here, atomic mass values are determined by comparison of cyclotron frequencies, {omega}{sub c}=qB/m, of stored ions with mass m and charge q in a strong magnetic field B, relative to those of well-known ion species. One of the future goals of MLLTRAP is to utilize highly-charged ions for an improvement in the achievable mass accuracy {delta}m/m. For this purpose, singly-charged ions will have to be injected into a charge-breeding device, such as an EBIT, and transferred back towards the Penning traps, while being q/A selected. A multi-passage-spectrometer (MPS) is being built to fulfill these tasks. It consists of a fast-ramping, round-pole dipole magnet with a four-way electrostatic mirror system. In this presentation, the planned MLLTRAP setup focussing on the q/A-selection with the MPS system is presented.

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

Directory of Open Access Journals (Sweden)

Kaveh Khalili-Damghani

2017-07-01

Full Text Available In this paper a multi-period multi-product multi-objective aggregate production planning (APP model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method.

Multi-objective optimization of linear multi-state multiple sliding window system

International Nuclear Information System (INIS)

Konak, Abdullah; Kulturel-Konak, Sadan; Levitin, Gregory

2012-01-01

This paper considers the optimal element sequencing in a linear multi-state multiple sliding window system that consists of n linearly ordered multi-state elements. Each multi-state element can have different states: from complete failure up to perfect functioning. A performance rate is associated with each state. The failure of type i in the system occurs if for any i (1≤i≤I) the cumulative performance of any r i consecutive elements is lower than w i . The element sequence strongly affects the probability of any type of system failure. The sequence that minimizes the probability of certain type of failure can provide high probability of other types of failures. Therefore the optimization problem for the multiple sliding window system is essentially multi-objective. The paper formulates and solves the multi-objective optimization problem for the multiple sliding window systems. A multi-objective Genetic Algorithm is used as the optimization engine. Illustrative examples are presented.

Multi-Objective Nonlinear Model Predictive Control: Lexicographic Method

OpenAIRE

Zheng, Tao; Wu, Gang; Liu, Guang-Hong; Ling, Qing

2010-01-01

In this chapter, to avoid the disadvantages of weight coefficients in multi-objective dynamic optimization, lexicographic (completely stratified) and partially stratified frameworks of multi-objective controller are proposed. The lexicographic framework is absolutely prioritydriven and the partially stratified framework is a modification of it, they both can solve the multi-objective control problem with the concept of priority for objective’s relative importance, while the latter one is mo...

Multi-objective Transmission Planning Paper

DEFF Research Database (Denmark)

Xu, Zhao; Dong, Zhao Yang; Wong, Kit Po

2009-01-01

This paper describes a transmission expansion planning method based on multi-objective optimization (MOOP). The method starts with constructing a candidate pool of feasible expansion plans, followed by selection of the best candidates through MOOP, of which multiple objectives are tackled...

Portable x-ray fluorescence spectrometer for Works of art

International Nuclear Information System (INIS)

Mendoza, A.; Griesser, A.

2001-01-01

X-ray fluorescence is an analytical technique of prier importance in archaeometry, for restoration and art history investigation; it is because of non-destructive and multi-elemental character of the analysis simplicity and high speed of operation, ability to produce immediate analytical results for the objects, which can neither be sampled nor removed to the laboratory Recent advances in X-ray tubes, X-ray detectors and electronic provided an opportunity to produce portable high resolution XRF spectrometers characterized by a good reliability and analytical performance; in this paper a prototype portable XRF spectrometer based on a small size, low power X-ray tube and a thermometrically cooled Si-Pin detector is described. The spectrometer provides a possibility for direct and secondary target excitation geometry use of proper secondary target and filter and size adjustment of the primary photon bean by using a set of different beam collimators; the portable XRF spectrometer was successfully applied to study art objects in the Art History Museum in Vienna, including such objects as old master paintings bronze and brass alloys of antique as well as Renaissance objects and silver/copper coins produced at different locations. Quantitative and Quantitative analysis were amedee depending of the curator questions and discussed from the point of view of art History. The importance of the results for restoration and authentification of the art objects is also emphasized

Fuzzy Multi-objective Linear Programming Approach

Directory of Open Access Journals (Sweden)

Amna Rehmat

2007-07-01

Full Text Available Traveling salesman problem (TSP is one of the challenging real-life problems, attracting researchers of many fields including Artificial Intelligence, Operations Research, and Algorithm Design and Analysis. The problem has been well studied till now under different headings and has been solved with different approaches including genetic algorithms and linear programming. Conventional linear programming is designed to deal with crisp parameters, but information about real life systems is often available in the form of vague descriptions. Fuzzy methods are designed to handle vague terms, and are most suited to finding optimal solutions to problems with vague parameters. Fuzzy multi-objective linear programming, an amalgamation of fuzzy logic and multi-objective linear programming, deals with flexible aspiration levels or goals and fuzzy constraints with acceptable deviations. In this paper, a methodology, for solving a TSP with imprecise parameters, is deployed using fuzzy multi-objective linear programming. An example of TSP with multiple objectives and vague parameters is discussed.

Interactive Approach for Multi-Level Multi-Objective Fractional Programming Problems with Fuzzy Parameters

Directory of Open Access Journals (Sweden)

M.S. Osman

2018-03-01

Full Text Available In this paper, an interactive approach for solving multi-level multi-objective fractional programming (ML-MOFP problems with fuzzy parameters is presented. The proposed interactive approach makes an extended work of Shi and Xia (1997. In the first phase, the numerical crisp model of the ML-MOFP problem has been developed at a confidence level without changing the fuzzy gist of the problem. Then, the linear model for the ML-MOFP problem is formulated. In the second phase, the interactive approach simplifies the linear multi-level multi-objective model by converting it into separate multi-objective programming problems. Also, each separate multi-objective programming problem of the linear model is solved by the ∊-constraint method and the concept of satisfactoriness. Finally, illustrative examples and comparisons with the previous approaches are utilized to evince the feasibility of the proposed approach.

Multi-Objective Optimization of Hybrid Renewable Energy System Using an Enhanced Multi-Objective Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Mengjun Ming

2017-05-01

Full Text Available Due to the scarcity of conventional energy resources and the greenhouse effect, renewable energies have gained more attention. This paper proposes methods for multi-objective optimal design of hybrid renewable energy system (HRES in both isolated-island and grid-connected modes. In each mode, the optimal design aims to find suitable configurations of photovoltaic (PV panels, wind turbines, batteries and diesel generators in HRES such that the system cost and the fuel emission are minimized, and the system reliability/renewable ability (corresponding to different modes is maximized. To effectively solve this multi-objective problem (MOP, the multi-objective evolutionary algorithm based on decomposition (MOEA/D using localized penalty-based boundary intersection (LPBI method is proposed. The algorithm denoted as MOEA/D-LPBI is demonstrated to outperform its competitors on the HRES model as well as a set of benchmarks. Moreover, it effectively obtains a good approximation of Pareto optimal HRES configurations. By further considering a decision maker’s preference, the most satisfied configuration of the HRES can be identified.

Multi Camera Multi Object Tracking using Block Search over Epipolar Geometry

Directory of Open Access Journals (Sweden)

Saman Sargolzaei

2000-01-01

Full Text Available We present strategy for multi-objects tracking in multi camera environment for the surveillance and security application where tracking multitude subjects are of utmost importance in a crowded scene. Our technique assumes partially overlapped multi-camera setup where cameras share common view from different angle to assess positions and activities of subjects under suspicion. To establish spatial correspondence between camera views we employ an epipolar geometry technique. We propose an overlapped block search method to find the interested pattern (target in new frames. Color pattern update scheme has been considered to further optimize the efficiency of the object tracking when object pattern changes due to object motion in the field of views of the cameras. Evaluation of our approach is presented with the results on PETS2007 dataset..

Structural damage detection-oriented multi-type sensor placement with multi-objective optimization

Science.gov (United States)

Lin, Jian-Fu; Xu, You-Lin; Law, Siu-Seong

2018-05-01

A structural damage detection-oriented multi-type sensor placement method with multi-objective optimization is developed in this study. The multi-type response covariance sensitivity-based damage detection method is first introduced. Two objective functions for optimal sensor placement are then introduced in terms of the response covariance sensitivity and the response independence. The multi-objective optimization problem is formed by using the two objective functions, and the non-dominated sorting genetic algorithm (NSGA)-II is adopted to find the solution for the optimal multi-type sensor placement to achieve the best structural damage detection. The proposed method is finally applied to a nine-bay three-dimensional frame structure. Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection. The restriction on the number of each type of sensors in the optimization can reduce the searching space in the optimization to make the proposed method more effective. Moreover, how to select a most optimal sensor placement from the Pareto solutions via the utility function and the knee point method is demonstrated in the case study.

Use of UAV-Borne Spectrometer for Land Cover Classification

Directory of Open Access Journals (Sweden)

Sowmya Natesan

2018-04-01

Full Text Available Unmanned aerial vehicles (UAV are being used for low altitude remote sensing for thematic land classification using visible light and multi-spectral sensors. The objective of this work was to investigate the use of UAV equipped with a compact spectrometer for land cover classification. The UAV platform used was a DJI Flamewheel F550 hexacopter equipped with GPS and Inertial Measurement Unit (IMU navigation sensors, and a Raspberry Pi processor and camera module. The spectrometer used was the FLAME-NIR, a near-infrared spectrometer for hyperspectral measurements. RGB images and spectrometer data were captured simultaneously. As spectrometer data do not provide continuous terrain coverage, the locations of their ground elliptical footprints were determined from the bundle adjustment solution of the captured images. For each of the spectrometer ground ellipses, the land cover signature at the footprint location was determined to enable the characterization, identification, and classification of land cover elements. To attain a continuous land cover classification map, spatial interpolation was carried out from the irregularly distributed labeled spectrometer points. The accuracy of the classification was assessed using spatial intersection with the object-based image classification performed using the RGB images. Results show that in homogeneous land cover, like water, the accuracy of classification is 78% and in mixed classes, like grass, trees and manmade features, the average accuracy is 50%, thus, indicating the contribution of hyperspectral measurements of low altitude UAV-borne spectrometers to improve land cover classification.

Benchmarks for dynamic multi-objective optimisation

CSIR Research Space (South Africa)

Helbig, M

2013-06-01

Full Text Available When algorithms solve dynamic multi-objective optimisation problems (DMOOPs), benchmark functions should be used to determine whether the algorithm can overcome specific difficulties that can occur in real-world problems. However, for dynamic multi...

Multi-Grid detector for neutron spectroscopy: results obtained on time-of-flight spectrometer CNCS

Science.gov (United States)

Anastasopoulos, M.; Bebb, R.; Berry, K.; Birch, J.; Bryś, T.; Buffet, J.-C.; Clergeau, J.-F.; Deen, P. P.; Ehlers, G.; van Esch, P.; Everett, S. M.; Guerard, B.; Hall-Wilton, R.; Herwig, K.; Hultman, L.; Höglund, C.; Iruretagoiena, I.; Issa, F.; Jensen, J.; Khaplanov, A.; Kirstein, O.; Lopez Higuera, I.; Piscitelli, F.; Robinson, L.; Schmidt, S.; Stefanescu, I.

2017-04-01

The Multi-Grid detector technology has evolved from the proof-of-principle and characterisation stages. Here we report on the performance of the Multi-Grid detector, the MG.CNCS prototype, which has been installed and tested at the Cold Neutron Chopper Spectrometer, CNCS at SNS. This has allowed a side-by-side comparison to the performance of 3He detectors on an operational instrument. The demonstrator has an active area of 0.2 m2. It is specifically tailored to the specifications of CNCS. The detector was installed in June 2016 and has operated since then, collecting neutron scattering data in parallel to the He-3 detectors of CNCS. In this paper, we present a comprehensive analysis of this data, in particular on instrument energy resolution, rate capability, background and relative efficiency. Stability, gamma-ray and fast neutron sensitivity have also been investigated. The effect of scattering in the detector components has been measured and provides input to comparison for Monte Carlo simulations. All data is presented in comparison to that measured by the 3He detectors simultaneously, showing that all features recorded by one detector are also recorded by the other. The energy resolution matches closely. We find that the Multi-Grid is able to match the data collected by 3He, and see an indication of a considerable advantage in the count rate capability. Based on these results, we are confident that the Multi-Grid detector will be capable of producing high quality scientific data on chopper spectrometers utilising the unprecedented neutron flux of the ESS.

Power magnetic devices a multi-objective design approach

CERN Document Server

Sudhoff, Scott D

2014-01-01

Presents a multi-objective design approach to the many power magnetic devices in use today Power Magnetic Devices: A Multi-Objective Design Approach addresses the design of power magnetic devices-including inductors, transformers, electromagnets, and rotating electric machinery-using a structured design approach based on formal single- and multi-objective optimization. The book opens with a discussion of evolutionary-computing-based optimization. Magnetic analysis techniques useful to the design of all the devices considered in the book are then set forth. This material is then used for ind

Distribution Network Expansion Planning Based on Multi-objective PSO Algorithm

DEFF Research Database (Denmark)

Zhang, Chunyu; Ding, Yi; Wu, Qiuwei

2013-01-01

This paper presents a novel approach for electrical distribution network expansion planning using multi-objective particle swarm optimization (PSO). The optimization objectives are: investment and operation cost, energy losses cost, and power congestion cost. A two-phase multi-objective PSO...... algorithm was proposed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity of Pareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach and the improved multi-objective PSO have been verified...

Smart Multi-Frequency Bioelectrical Impedance Spectrometer for BIA and BIVA Applications.

Science.gov (United States)

Harder, Rene; Diedrich, Andre; Whitfield, Jonathan S; Buchowski, Macie S; Pietsch, John B; Baudenbacher, Franz J

2016-08-01

Bioelectrical impedance analysis (BIA) is a noninvasive and commonly used method for the assessment of body composition including body water. We designed a small, portable and wireless multi-frequency impedance spectrometer based on the 12 bit impedance network analyzer AD5933 and a precision wide-band constant current source for tetrapolar whole body impedance measurements. The impedance spectrometer communicates via Bluetooth with mobile devices (smart phone or tablet computer) that provide user interface for patient management and data visualization. The export of patient measurement results into a clinical research database facilitates the aggregation of bioelectrical impedance analysis and biolectrical impedance vector analysis (BIVA) data across multiple subjects and/or studies. The performance of the spectrometer was evaluated using a passive tissue equivalent circuit model as well as a comparison of body composition changes assessed with bioelectrical impedance and dual-energy X-ray absorptiometry (DXA) in healthy volunteers. Our results show an absolute error of 1% for resistance and 5% for reactance measurements in the frequency range of 3 kHz to 150 kHz. A linear regression of BIA and DXA fat mass estimations showed a strong correlation (r(2)=0.985) between measures with a maximum absolute error of 6.5%. The simplicity of BIA measurements, a cost effective design and the simple visual representation of impedance data enables patients to compare and determine body composition during the time course of a specific treatment plan in a clinical or home environment.

Objective Crystal Spectrometer (OXS) for the Spectrum-X-γ satellite

DEFF Research Database (Denmark)

Christensen, Finn Erland; Byrnak, B.P.; Hornstrup, Allan

1990-01-01

The status of the Objective Crystal Spectrometer (OXS) to be flown on the Soviet Spectrum-X-Gamma satellite together with the X-ray investigation of two of the three natural crystals (LiF(220), Ge(111) and RAP(001) which are chosen as the baseline option are presented. An important result...... on the LiF(220) and Ge(111) surfaces is presented. This design allows simultaneous spectroscopy in two energy bands each centered on cosmically interesting line emission regions. X-ray reflectivity measurements demonstrate that the crystal surface can be made sufficiently smooth for the application...

Multi-objective congestion management by modified augmented ε-constraint method

International Nuclear Information System (INIS)

Esmaili, Masoud; Shayanfar, Heidar Ali; Amjady, Nima

2011-01-01

Congestion management is a vital part of power system operations in recent deregulated electricity markets. However, after relieving congestion, power systems may be operated with a reduced voltage or transient stability margin because of hitting security limits or increasing the contribution of risky participants. Therefore, power system stability margins should be considered within the congestion management framework. The multi-objective congestion management provides not only more security but also more flexibility than single-objective methods. In this paper, a multi-objective congestion management framework is presented while simultaneously optimizing the competing objective functions of congestion management cost, voltage security, and dynamic security. The proposed multi-objective framework, called modified augmented ε-constraint method, is based on the augmented ε-constraint technique hybridized by the weighting method. The proposed framework generates candidate solutions for the multi-objective problem including only efficient Pareto surface enhancing the competitiveness and economic effectiveness of the power market. Besides, the relative importance of the objective functions is explicitly modeled in the proposed framework. Results of testing the proposed multi-objective congestion management method on the New-England test system are presented and compared with those of the previous single objective and multi-objective techniques in detail. These comparisons confirm the efficiency of the developed method. (author)

Multi-objective compared to single-objective optimization with application to model validation and uncertainty quantification

Energy Technology Data Exchange (ETDEWEB)

Schulze-Riegert, R.; Krosche, M.; Stekolschikov, K. [Scandpower Petroleum Technology GmbH, Hamburg (Germany); Fahimuddin, A. [Technische Univ. Braunschweig (Germany)

2007-09-13

History Matching in Reservoir Simulation, well location and production optimization etc. is generally a multi-objective optimization problem. The problem statement of history matching for a realistic field case includes many field and well measurements in time and type, e.g. pressure measurements, fluid rates, events such as water and gas break-throughs, etc. Uncertainty parameters modified as part of the history matching process have varying impact on the improvement of the match criteria. Competing match criteria often reduce the likelihood of finding an acceptable history match. It is an engineering challenge in manual history matching processes to identify competing objectives and to implement the changes required in the simulation model. In production optimization or scenario optimization the focus on one key optimization criterion such as NPV limits the identification of alternatives and potential opportunities, since multiple objectives are summarized in a predefined global objective formulation. Previous works primarily focus on a specific optimization method. Few works actually concentrate on the objective formulation and multi-objective optimization schemes have not yet been applied to reservoir simulations. This paper presents a multi-objective optimization approach applicable to reservoir simulation. It addresses the problem of multi-objective criteria in a history matching study and presents analysis techniques identifying competing match criteria. A Pareto-Optimizer is discussed and the implementation of that multi-objective optimization scheme is applied to a case study. Results are compared to a single-objective optimization method. (orig.)

Aerodynamic multi-objective integrated optimization based on principal component analysis

Directory of Open Access Journals (Sweden)

Jiangtao HUANG

2017-08-01

Full Text Available Based on improved multi-objective particle swarm optimization (MOPSO algorithm with principal component analysis (PCA methodology, an efficient high-dimension multi-objective optimization method is proposed, which, as the purpose of this paper, aims to improve the convergence of Pareto front in multi-objective optimization design. The mathematical efficiency, the physical reasonableness and the reliability in dealing with redundant objectives of PCA are verified by typical DTLZ5 test function and multi-objective correlation analysis of supercritical airfoil, and the proposed method is integrated into aircraft multi-disciplinary design (AMDEsign platform, which contains aerodynamics, stealth and structure weight analysis and optimization module. Then the proposed method is used for the multi-point integrated aerodynamic optimization of a wide-body passenger aircraft, in which the redundant objectives identified by PCA are transformed to optimization constraints, and several design methods are compared. The design results illustrate that the strategy used in this paper is sufficient and multi-point design requirements of the passenger aircraft are reached. The visualization level of non-dominant Pareto set is improved by effectively reducing the dimension without losing the primary feature of the problem.

Multi-objective optimization using genetic algorithms: A tutorial

International Nuclear Information System (INIS)

Konak, Abdullah; Coit, David W.; Smith, Alice E.

2006-01-01

Multi-objective formulations are realistic models for many complex engineering optimization problems. In many real-life problems, objectives under consideration conflict with each other, and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. A reasonable solution to a multi-objective problem is to investigate a set of solutions, each of which satisfies the objectives at an acceptable level without being dominated by any other solution. In this paper, an overview and tutorial is presented describing genetic algorithms (GA) developed specifically for problems with multiple objectives. They differ primarily from traditional GA by using specialized fitness functions and introducing methods to promote solution diversity

Multi-criteria objective based climate change impact assessment for multi-purpose multi-reservoir systems

Science.gov (United States)

Müller, Ruben; Schütze, Niels

2014-05-01

Water resources systems with reservoirs are expected to be sensitive to climate change. Assessment studies that analyze the impact of climate change on the performance of reservoirs can be divided in two groups: (1) Studies that simulate the operation under projected inflows with the current set of operational rules. Due to non adapted operational rules the future performance of these reservoirs can be underestimated and the impact overestimated. (2) Studies that optimize the operational rules for best adaption of the system to the projected conditions before the assessment of the impact. The latter allows for estimating more realistically future performance and adaption strategies based on new operation rules are available if required. Multi-purpose reservoirs serve various, often conflicting functions. If all functions cannot be served simultaneously at a maximum level, an effective compromise between multiple objectives of the reservoir operation has to be provided. Yet under climate change the historically preferenced compromise may no longer be the most suitable compromise in the future. Therefore a multi-objective based climate change impact assessment approach for multi-purpose multi-reservoir systems is proposed in the study. Projected inflows are provided in a first step using a physically based rainfall-runoff model. In a second step, a time series model is applied to generate long-term inflow time series. Finally, the long-term inflow series are used as driving variables for a simulation-based multi-objective optimization of the reservoir system in order to derive optimal operation rules. As a result, the adapted Pareto-optimal set of diverse best compromise solutions can be presented to the decision maker in order to assist him in assessing climate change adaption measures with respect to the future performance of the multi-purpose reservoir system. The approach is tested on a multi-purpose multi-reservoir system in a mountainous catchment in Germany. A

Multi-objective differential evolution with adaptive Cauchy mutation for short-term multi-objective optimal hydro-thermal scheduling

Energy Technology Data Exchange (ETDEWEB)

Qin Hui [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Zhou Jianzhong, E-mail: jz.zhou@hust.edu.c [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China); Lu Youlin; Wang Ying; Zhang Yongchuan [College of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074 (China)

2010-04-15

A new multi-objective optimization method based on differential evolution with adaptive Cauchy mutation (MODE-ACM) is presented to solve short-term multi-objective optimal hydro-thermal scheduling (MOOHS) problem. Besides fuel cost, the pollutant gas emission is also optimized as an objective. The water transport delay between connected reservoirs and the effect of valve-point loading of thermal units are also taken into account in the presented problem formulation. The proposed algorithm adopts an elitist archive to retain non-dominated solutions obtained during the evolutionary process. It modifies the DE's operators to make it suit for multi-objective optimization (MOO) problems and improve its performance. Furthermore, to avoid premature convergence, an adaptive Cauchy mutation is proposed to preserve the diversity of population. An effective constraints handling method is utilized to handle the complex equality and inequality constraints. The effectiveness of the proposed algorithm is tested on a hydro-thermal system consisting of four cascaded hydro plants and three thermal units. The results obtained by MODE-ACM are compared with several previous studies. It is found that the results obtained by MODE-ACM are superior in terms of fuel cost as well as emission output, consuming a shorter time. Thus it can be a viable alternative to generate optimal trade-offs for short-term MOOHS problem.

Multi-objective optimization of inverse planning for accurate radiotherapy

International Nuclear Information System (INIS)

Cao Ruifen; Pei Xi; Cheng Mengyun; Li Gui; Hu Liqin; Wu Yican; Jing Jia; Li Guoli

2011-01-01

The multi-objective optimization of inverse planning based on the Pareto solution set, according to the multi-objective character of inverse planning in accurate radiotherapy, was studied in this paper. Firstly, the clinical requirements of a treatment plan were transformed into a multi-objective optimization problem with multiple constraints. Then, the fast and elitist multi-objective Non-dominated Sorting Genetic Algorithm (NSGA-II) was introduced to optimize the problem. A clinical example was tested using this method. The results show that an obtained set of non-dominated solutions were uniformly distributed and the corresponding dose distribution of each solution not only approached the expected dose distribution, but also met the dose-volume constraints. It was indicated that the clinical requirements were better satisfied using the method and the planner could select the optimal treatment plan from the non-dominated solution set. (authors)

Convex hull ranking algorithm for multi-objective evolutionary algorithms

NARCIS (Netherlands)

Davoodi Monfrared, M.; Mohades, A.; Rezaei, J.

2012-01-01

Due to many applications of multi-objective evolutionary algorithms in real world optimization problems, several studies have been done to improve these algorithms in recent years. Since most multi-objective evolutionary algorithms are based on the non-dominated principle, and their complexity

Enhanced Multi-Objective Energy Optimization by a Signaling Method

OpenAIRE

Soares, João; Borges, Nuno; Vale, Zita; Oliveira, P.B.

2016-01-01

In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2) emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO), multi-objective particle swarm optimization (MOPSO) and non-dominated sorting genetic algorithm II (NSGA-II). The performance of these methods with the use of multi-dimensi...

MOPSO-based multi-objective TSO planning considering uncertainties

DEFF Research Database (Denmark)

Wang, Qi; Zhang, Chunyu; Ding, Yi

2014-01-01

factors, i.e. load growth, generation capacity and line faults, and aims to enhance the transmission system via the multi-objective TSO planning (MOTP) approach. The proposed MOTP approach optimizes three objectives simultaneously, namely the probabilistic available transfer capability (PATC), investment...... cost and power outage cost. A two-phase MOPSO algorithm is employed to solve this optimization problem, which can accelerate the convergence and guarantee the diversity ofPareto-optimal front set as well. The feasibility and effectiveness of both the proposed multi-objective planning approach...

A procedure for multi-objective optimization of tire design parameters

Directory of Open Access Journals (Sweden)

Nikola Korunović

2015-04-01

Full Text Available The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zones inside the tire. It consists of four main stages: pre-analysis, design of experiment, mathematical modeling and multi-objective optimization. Advantage of the proposed procedure is reflected in the fact that multi-objective optimization is based on the Pareto concept, which enables design engineers to obtain a complete set of optimization solutions and choose a suitable tire design. Furthermore, modeling of the relationships between tire design parameters and objective functions based on multiple regression analysis minimizes computational and modeling effort. The adequacy of the proposed tire design multi-objective optimization procedure has been validated by performing experimental trials based on finite element method.

Connected Component Model for Multi-Object Tracking.

Science.gov (United States)

He, Zhenyu; Li, Xin; You, Xinge; Tao, Dacheng; Tang, Yuan Yan

2016-08-01

In multi-object tracking, it is critical to explore the data associations by exploiting the temporal information from a sequence of frames rather than the information from the adjacent two frames. Since straightforwardly obtaining data associations from multi-frames is an NP-hard multi-dimensional assignment (MDA) problem, most existing methods solve this MDA problem by either developing complicated approximate algorithms, or simplifying MDA as a 2D assignment problem based upon the information extracted only from adjacent frames. In this paper, we show that the relation between associations of two observations is the equivalence relation in the data association problem, based on the spatial-temporal constraint that the trajectories of different objects must be disjoint. Therefore, the MDA problem can be equivalently divided into independent subproblems by equivalence partitioning. In contrast to existing works for solving the MDA problem, we develop a connected component model (CCM) by exploiting the constraints of the data association and the equivalence relation on the constraints. Based upon CCM, we can efficiently obtain the global solution of the MDA problem for multi-object tracking by optimizing a sequence of independent data association subproblems. Experiments on challenging public data sets demonstrate that our algorithm outperforms the state-of-the-art approaches.

New approach for solving intuitionistic fuzzy multi-objective ...

Indian Academy of Sciences (India)

SANKAR KUMAR ROY

2018-02-07

Feb 7, 2018 ... Transportation problem; multi-objective decision making; intuitionistic fuzzy programming; interval programming ... MOTP under multi-choice environment using utility func- ... theory is an intuitionistic fuzzy set (IFS), which was.

Reduction of determinate errors in mass bias-corrected isotope ratios measured using a multi-collector plasma mass spectrometer

International Nuclear Information System (INIS)

Doherty, W.

2015-01-01

A nebulizer-centric instrument response function model of the plasma mass spectrometer was combined with a signal drift model, and the result was used to identify the causes of the non-spectroscopic determinate errors remaining in mass bias-corrected Pb isotope ratios (Tl as internal standard) measured using a multi-collector plasma mass spectrometer. Model calculations, confirmed by measurement, show that the detectable time-dependent errors are a result of the combined effect of signal drift and differences in the coordinates of the Pb and Tl response function maxima (horizontal offset effect). If there are no horizontal offsets, then the mass bias-corrected isotope ratios are approximately constant in time. In the absence of signal drift, the response surface curvature and horizontal offset effects are responsible for proportional errors in the mass bias-corrected isotope ratios. The proportional errors will be different for different analyte isotope ratios and different at every instrument operating point. Consequently, mass bias coefficients calculated using different isotope ratios are not necessarily equal. The error analysis based on the combined model provides strong justification for recommending a three step correction procedure (mass bias correction, drift correction and a proportional error correction, in that order) for isotope ratio measurements using a multi-collector plasma mass spectrometer

Multiple utility constrained multi-objective programs using Bayesian theory

Science.gov (United States)

Abbasian, Pooneh; Mahdavi-Amiri, Nezam; Fazlollahtabar, Hamed

2018-03-01

A utility function is an important tool for representing a DM's preference. We adjoin utility functions to multi-objective optimization problems. In current studies, usually one utility function is used for each objective function. Situations may arise for a goal to have multiple utility functions. Here, we consider a constrained multi-objective problem with each objective having multiple utility functions. We induce the probability of the utilities for each objective function using Bayesian theory. Illustrative examples considering dependence and independence of variables are worked through to demonstrate the usefulness of the proposed model.

Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units

Directory of Open Access Journals (Sweden)

Jinjiao Hou

2018-05-01

Full Text Available This paper proposes a multi-objective optimization method for the start-up strategy of pumped storage units (PSU for the first time. In the multi-objective optimization method, the speed rise time and the overshoot during the process of the start-up are taken as the objectives. A precise simulation platform is built for simulating the transient process of start-up, and for calculating the objectives based on the process. The Multi-objective Particle Swarm Optimization algorithm (MOPSO is adopted to optimize the widely applied start-up strategies based on one-stage direct guide vane control (DGVC, and two-stage DGVC. Based on the Pareto Front obtained, a multi-objective decision-making method based on the relative objective proximity is used to sort the solutions in the Pareto Front. Start-up strategy optimization for a PSU of a pumped storage power station in Jiangxi Province in China is conducted in experiments. The results show that: (1 compared with the single objective optimization, the proposed multi-objective optimization of start-up strategy not only greatly shortens the speed rise time and the speed overshoot, but also makes the speed curve quickly stabilize; (2 multi-objective optimization of strategy based on two-stage DGVC achieves better solution for a quick and smooth start-up of PSU than that of the strategy based on one-stage DGVC.

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

Science.gov (United States)

Trianni, Vito; López-Ibáñez, Manuel

2015-01-01

The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics.

Directory of Open Access Journals (Sweden)

Vito Trianni

Full Text Available The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled. However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

A multi-object spectral imaging instrument

International Nuclear Information System (INIS)

Gibson, G M; Dienerowitz, M; Kelleher, P A; Harvey, A R; Padgett, M J

2013-01-01

We have developed a snapshot spectral imaging system which fits onto the side camera port of a commercial inverted microscope. The system provides spectra, in real time, from multiple points randomly selected on the microscope image. Light from the selected points in the sample is directed from the side port imaging arm using a digital micromirror device to a spectrometer arm based on a dispersing prism and CCD camera. A multi-line laser source is used to calibrate the pixel positions on the CCD for wavelength. A CMOS camera on the front port of the microscope allows the full image of the sample to be displayed and can also be used for particle tracking, providing spectra of multiple particles moving in the sample. We demonstrate the system by recording the spectra of multiple fluorescent beads in aqueous solution and from multiple points along a microscope sample channel containing a mixture of red and blue dye. (paper)

Multi-Label Object Categorization Using Histograms of Global Relations

DEFF Research Database (Denmark)

Mustafa, Wail; Xiong, Hanchen; Kraft, Dirk

2015-01-01

In this paper, we present an object categorization system capable of assigning multiple and related categories for novel objects using multi-label learning. In this system, objects are described using global geometric relations of 3D features. We propose using the Joint SVM method for learning......). The experiments are carried out on a dataset of 100 objects belonging to 13 visual and action-related categories. The results indicate that multi-label methods are able to identify the relation between the dependent categories and hence perform categorization accordingly. It is also found that extracting...

Multi-objective optimization in computer networks using metaheuristics

CERN Document Server

Donoso, Yezid

2007-01-01

Metaheuristics are widely used to solve important practical combinatorial optimization problems. Many new multicast applications emerging from the Internet-such as TV over the Internet, radio over the Internet, and multipoint video streaming-require reduced bandwidth consumption, end-to-end delay, and packet loss ratio. It is necessary to design and to provide for these kinds of applications as well as for those resources necessary for functionality. Multi-Objective Optimization in Computer Networks Using Metaheuristics provides a solution to the multi-objective problem in routing computer networks. It analyzes layer 3 (IP), layer 2 (MPLS), and layer 1 (GMPLS and wireless functions). In particular, it assesses basic optimization concepts, as well as several techniques and algorithms for the search of minimals; examines the basic multi-objective optimization concepts and the way to solve them through traditional techniques and through several metaheuristics; and demonstrates how to analytically model the compu...

Multi-objective convex programming problem arising in multivariate ...

African Journals Online (AJOL)

user

Multi-objective convex programming problem arising in ... However, although the consideration of multiple objectives may seem a novel concept, virtually any nontrivial ..... Solving multiobjective programming problems by discrete optimization.

Recent advances in evolutionary multi-objective optimization

CERN Document Server

Datta, Rituparna; Gupta, Abhishek

2017-01-01

This book covers the most recent advances in the field of evolutionary multiobjective optimization. With the aim of drawing the attention of up-andcoming scientists towards exciting prospects at the forefront of computational intelligence, the authors have made an effort to ensure that the ideas conveyed herein are accessible to the widest audience. The book begins with a summary of the basic concepts in multi-objective optimization. This is followed by brief discussions on various algorithms that have been proposed over the years for solving such problems, ranging from classical (mathematical) approaches to sophisticated evolutionary ones that are capable of seamlessly tackling practical challenges such as non-convexity, multi-modality, the presence of multiple constraints, etc. Thereafter, some of the key emerging aspects that are likely to shape future research directions in the field are presented. These include:< optimization in dynamic environments, multi-objective bilevel programming, handling high ...

Searching for the Pareto frontier in multi-objective protein design.

Science.gov (United States)

Nanda, Vikas; Belure, Sandeep V; Shir, Ofer M

2017-08-01

The goal of protein engineering and design is to identify sequences that adopt three-dimensional structures of desired function. Often, this is treated as a single-objective optimization problem, identifying the sequence-structure solution with the lowest computed free energy of folding. However, many design problems are multi-state, multi-specificity, or otherwise require concurrent optimization of multiple objectives. There may be tradeoffs among objectives, where improving one feature requires compromising another. The challenge lies in determining solutions that are part of the Pareto optimal set-designs where no further improvement can be achieved in any of the objectives without degrading one of the others. Pareto optimality problems are found in all areas of study, from economics to engineering to biology, and computational methods have been developed specifically to identify the Pareto frontier. We review progress in multi-objective protein design, the development of Pareto optimization methods, and present a specific case study using multi-objective optimization methods to model the tradeoff between three parameters, stability, specificity, and complexity, of a set of interacting synthetic collagen peptides.

Performance and robustness of a multi-user, multi-spectrometer system for INAA

International Nuclear Information System (INIS)

Peter Bode; Menno Blaauw

2012-01-01

The laboratory for instrumental neutron activation analysis at the Reactor Institute Delft, Delft University of Technology uses a network of 3 gamma-ray spectrometers with well-type detectors and 2 gamma-ray spectrometers with coaxial detectors, all equipped with modern sample changers, as well as 2 spectrometers with coaxial detectors at the two fast rabbit systems. A wide variety of samples is processed through the system, all at specific optimized (and thus different) analytical protocols, and using different combination of the spectrometer systems. The gamma-ray spectra are analyzed by several qualified operators. The laboratory therefore needs to anticipate on the occurrence of random and systematic inconsistencies in the results (such as bias, non-linearity or wrong assignments due to spectral interferences) resulting from differences in operator performance, selection of analytical protocol and experimental conditions. This has been accomplished by taking advantage of the systematic processing of internal quality control samples such as certified reference materials and blanks in each test run. The data from these internal quality control analyses have been stored in a databank since 1991, and are now used to assess the various method performance indicators as indicators for the method's robustness. (author)

The development for small scale soft X-ray spectrometer

International Nuclear Information System (INIS)

Sun Kexu; Jiang Shaoen; Yi Rongqing; Cui Yanli

2004-12-01

For the development of small-scale soft X-ray spectrometer, first, some small-scale soft X-ray detection elements are developed, it is included GaAs irradiated with neutron, GaAs irradiated with proton, multi-layer mirror, plane mirror and small scale X-ray diode et al. Soft X-ray spectrometers built of multi-layer mirror-GaAs (with neutron irradiation), and plane mirror-small-scale XRD, and plane mirror-GaAs (with proton irradiation) are prepared. These spectrometers are examined in Shen Guang-II laser facility, and some external estimation are given. (authors)

Echelle grating multi-order imaging spectrometer utilizing a catadioptric lens

Science.gov (United States)

Chrisp, Michael P; Bowers, Joel M

2014-05-27

A cryogenically cooled imaging spectrometer that includes a spectrometer housing having a first side and a second side opposite the first side. An entrance slit is on the first side of the spectrometer housing and directs light to a cross-disperser grating. An echelle immersions grating and a catadioptric lens are positioned in the housing to receive the light. A cryogenically cooled detector is located in the housing on the second side of the spectrometer housing. Light from the entrance slit is directed to the cross-disperser grating. The light is directed from the cross-disperser grating to the echelle immersions grating. The light is directed from the echelle immersions grating to the cryogenically cooled detector on the second side of the spectrometer housing.

EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.

Science.gov (United States)

Castro Martins, Thiago; Sales Guerra Tsuzuki, Marcos

2015-01-01

Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.

Modified Thomson spectrometer design for high energy, multi-species ion sources

International Nuclear Information System (INIS)

Gwynne, D.; Kar, S.; Doria, D.; Ahmed, H.; Hanton, F.; Cerchez, M.; Swantusch, M.; Willi, O.; Fernandez, J.; Gray, R. J.; MacLellan, D. A.; McKenna, P.; Green, J. S.; Neely, D.; Najmudin, Z.; Streeter, M.; Ruiz, J. A.; Schiavi, A.; Zepf, M.; Borghesi, M.

2014-01-01

A modification to the standard Thomson parabola spectrometer is discussed, which is designed to measure high energy (tens of MeV/nucleon), broad bandwidth spectra of multi-species ions accelerated by intense laser plasma interactions. It is proposed to implement a pair of extended, trapezoidal shaped electric plates, which will not only resolve ion traces at high energies, but will also retain the lower energy part of the spectrum. While a longer (along the axis of the undeflected ion beam direction) electric plate design provides effective charge state separation at the high energy end of the spectrum, the proposed new trapezoidal shape will enable the low energy ions to reach the detector, which would have been clipped or blocked by simply extending the rectangular plates to enhance the electrostatic deflection

The Dynamic Multi-objective Multi-vehicle Covering Tour Problem

Science.gov (United States)

2013-06-01

144 [38] Coello, Carlos A. Coello, Gary B Lamont, and David A Van Veldhuizen . Evolutionary Algorithms for Solving Multi-Objective Problems. Springer...Traveling Repairperson Problem (DTRP) Policies Proposed by Bertsimas and Van Ryzin. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.3...queuing theory perspective. Table 3.2: DTRP Policies Proposed by Bertsimas and Van Ryzin. Name Description First Come First Serve (FCFS) vehicles

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

Energy Technology Data Exchange (ETDEWEB)

Kumar, Ranjan [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: ranjan.k@ks3.ecs.kyoto-u.ac.jp; Izui, Kazuhiro [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: izui@prec.kyoto-u.ac.jp; Yoshimura, Masataka [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: yoshimura@prec.kyoto-u.ac.jp; Nishiwaki, Shinji [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: shinji@prec.kyoto-u.ac.jp

2009-04-15

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.

Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

International Nuclear Information System (INIS)

Kumar, Ranjan; Izui, Kazuhiro; Yoshimura, Masataka; Nishiwaki, Shinji

2009-01-01

Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets

Interactive Preference Learning of Utility Functions for Multi-Objective Optimization

OpenAIRE

Dewancker, Ian; McCourt, Michael; Ainsworth, Samuel

2016-01-01

Real-world engineering systems are typically compared and contrasted using multiple metrics. For practical machine learning systems, performance tuning is often more nuanced than minimizing a single expected loss objective, and it may be more realistically discussed as a multi-objective optimization problem. We propose a novel generative model for scalar-valued utility functions to capture human preferences in a multi-objective optimization setting. We also outline an interactive active learn...

Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.

Science.gov (United States)

Jiménez, Fernando; Sánchez, Gracia; Juárez, José M

2014-03-01

This paper presents a novel rule-based fuzzy classification methodology for survival/mortality prediction in severe burnt patients. Due to the ethical aspects involved in this medical scenario, physicians tend not to accept a computer-based evaluation unless they understand why and how such a recommendation is given. Therefore, any fuzzy classifier model must be both accurate and interpretable. The proposed methodology is a three-step process: (1) multi-objective constrained optimization of a patient's data set, using Pareto-based elitist multi-objective evolutionary algorithms to maximize accuracy and minimize the complexity (number of rules) of classifiers, subject to interpretability constraints; this step produces a set of alternative (Pareto) classifiers; (2) linguistic labeling, which assigns a linguistic label to each fuzzy set of the classifiers; this step is essential to the interpretability of the classifiers; (3) decision making, whereby a classifier is chosen, if it is satisfactory, according to the preferences of the decision maker. If no classifier is satisfactory for the decision maker, the process starts again in step (1) with a different input parameter set. The performance of three multi-objective evolutionary algorithms, niched pre-selection multi-objective algorithm, elitist Pareto-based multi-objective evolutionary algorithm for diversity reinforcement (ENORA) and the non-dominated sorting genetic algorithm (NSGA-II), was tested using a patient's data set from an intensive care burn unit and a standard machine learning data set from an standard machine learning repository. The results are compared using the hypervolume multi-objective metric. Besides, the results have been compared with other non-evolutionary techniques and validated with a multi-objective cross-validation technique. Our proposal improves the classification rate obtained by other non-evolutionary techniques (decision trees, artificial neural networks, Naive Bayes, and case

A Bayesian alternative for multi-objective ecohydrological model specification

Science.gov (United States)

Tang, Yating; Marshall, Lucy; Sharma, Ashish; Ajami, Hoori

2018-01-01

Recent studies have identified the importance of vegetation processes in terrestrial hydrologic systems. Process-based ecohydrological models combine hydrological, physical, biochemical and ecological processes of the catchments, and as such are generally more complex and parametric than conceptual hydrological models. Thus, appropriate calibration objectives and model uncertainty analysis are essential for ecohydrological modeling. In recent years, Bayesian inference has become one of the most popular tools for quantifying the uncertainties in hydrological modeling with the development of Markov chain Monte Carlo (MCMC) techniques. The Bayesian approach offers an appealing alternative to traditional multi-objective hydrologic model calibrations by defining proper prior distributions that can be considered analogous to the ad-hoc weighting often prescribed in multi-objective calibration. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological modeling framework based on a traditional Pareto-based model calibration technique. In our study, a Pareto-based multi-objective optimization and a formal Bayesian framework are implemented in a conceptual ecohydrological model that combines a hydrological model (HYMOD) and a modified Bucket Grassland Model (BGM). Simulations focused on one objective (streamflow/LAI) and multiple objectives (streamflow and LAI) with different emphasis defined via the prior distribution of the model error parameters. Results show more reliable outputs for both predicted streamflow and LAI using Bayesian multi-objective calibration with specified prior distributions for error parameters based on results from the Pareto front in the ecohydrological modeling. The methodology implemented here provides insight into the usefulness of multiobjective Bayesian calibration for ecohydrologic systems and the importance of appropriate prior

Optimization of multi-objective micro-grid based on improved particle swarm optimization algorithm

Science.gov (United States)

Zhang, Jian; Gan, Yang

2018-04-01

The paper presents a multi-objective optimal configuration model for independent micro-grid with the aim of economy and environmental protection. The Pareto solution set can be obtained by solving the multi-objective optimization configuration model of micro-grid with the improved particle swarm algorithm. The feasibility of the improved particle swarm optimization algorithm for multi-objective model is verified, which provides an important reference for multi-objective optimization of independent micro-grid.

Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

Science.gov (United States)

Cao, Jia; Yan, Zheng; He, Guangyu

2016-06-01

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

An improved fast and elitist multi-objective genetic algorithm-ANSGA-II for multi-objective optimization of inverse radiotherapy treatment planning

International Nuclear Information System (INIS)

Cao Ruifen; Li Guoli; Song Gang; Zhao Pan; Lin Hui; Wu Aidong; Huang Chenyu; Wu Yican

2007-01-01

Objective: To provide a fast and effective multi-objective optimization algorithm for inverse radiotherapy treatment planning system. Methods: Non-dominated Sorting Genetic Algorithm-NSGA-II is a representative of multi-objective evolutionary optimization algorithms and excels the others. The paper produces ANSGA-II that makes use of advantage of NSGA-II, and uses adaptive crossover and mutation to improve its flexibility; according the character of inverse radiotherapy treatment planning, the paper uses the pre-known knowledge to generate individuals of every generation in the course of optimization, which enhances the convergent speed and improves efficiency. Results: The example of optimizing average dose of a sheet of CT, including PTV, OAR, NT, proves the algorithm could find satisfied solutions in several minutes. Conclusions: The algorithm could provide clinic inverse radiotherapy treatment planning system with selection of optimization algorithms. (authors)

Scalable and practical multi-objective distribution network expansion planning

NARCIS (Netherlands)

Luong, N.H.; Grond, M.O.W.; Poutré, La J.A.; Bosman, P.A.N.

2015-01-01

We formulate the distribution network expansion planning (DNEP) problem as a multi-objective optimization (MOO) problem with different objectives that distribution network operators (DNOs) would typically like to consider during decision making processes for expanding their networks. Objectives are

OBJECT-SPACE MULTI-IMAGE MATCHING OF MOBILE-MAPPING-SYSTEM IMAGE SEQUENCES

Directory of Open Access Journals (Sweden)

Y. C. Chen

2012-07-01

Full Text Available This paper proposes an object-space multi-image matching procedure of terrestrial MMS (Mobile Mapping System image sequences to determine the coordinates of an object point automatically and reliably. This image matching procedure can be applied to find conjugate points of MMS image sequences efficiently. Conventional area-based image matching methods are not reliable to deliver accurate matching results for this application due to image scale variations, viewing angle variations, and object occlusions. In order to deal with these three matching problems, an object space multi-image matching is proposed. A modified NCC (Normalized Cross Correlation coefficient is proposed to measure the similarity of image patches. A modified multi-window matching procedure will also be introduced to solve the problem of object occlusion. A coarse-to-fine procedure with a combination of object-space multi-image matching and multi-window matching is adopted. The proposed procedure has been implemented for the purpose of matching terrestrial MMS image sequences. The ratio of correct matches of this experiment was about 80 %. By providing an approximate conjugate point in an overlapping image manually, most of the incorrect matches could be fixed properly and the ratio of correct matches was improved up to 98 %.

Multi-objective evacuation routing optimization for toxic cloud releases

International Nuclear Information System (INIS)

Gai, Wen-mei; Deng, Yun-feng; Jiang, Zhong-an; Li, Jing; Du, Yan

2017-01-01

This paper develops a model for assessing the risks associated with the evacuation process in response to potential chemical accidents, based on which a multi-objective evacuation routing model for toxic cloud releases is proposed taking into account that the travel speed on each arc will be affected by disaster extension. The objectives of the evacuation routing model are to minimize travel time and individual evacuation risk along a path respectively. Two heuristic algorithms are proposed to solve the multi-objective evacuation routing model. Simulation results show the effectiveness and feasibility of the model and algorithms presented in this paper. And, the methodology with appropriate modification is suitable for supporting decisions in assessing emergency route selection in other cases (fires, nuclear accidents). - Highlights: • A model for assessing and visualizing the risks is developed. • A multi-objective evacuation routing model is proposed for toxic cloud releases. • A modified Dijkstra algorithm is designed to obtain an solution of the model. • Two heuristic algorithms have been developed as the optimization tool.

Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System

KAUST Repository

Charara, Ali; Ltaief, Hatem; Gratadour, Damien; Keyes, David E.; Sevin, Arnaud; Abdelfattah, Ahmad; Gendron, Eric; Morel, Carine; Vidal, Fabrice

2014-01-01

called MOSAIC has been proposed to perform multi-object spectroscopy using the Multi-Object Adaptive Optics (MOAO) technique. The core implementation of the simulation lies in the intensive computation of a tomographic reconstruct or (TR), which is used

A multi-objective decision framework for lifecycle investment

NARCIS (Netherlands)

Timmermans, S.H.J.T.; Schumacher, J.M.; Ponds, E.H.M.

2017-01-01

In this paper we propose a multi-objective decision framework for lifecycle investment choice. Instead of optimizing individual strategies with respect to a single-valued objective, we suggest evaluation of classes of strategies in terms of the quality of the tradeoffs that they provide. The

Multi-objective possibilistic model for portfolio selection with transaction cost

Science.gov (United States)

Jana, P.; Roy, T. K.; Mazumder, S. K.

2009-06-01

In this paper, we introduce the possibilistic mean value and variance of continuous distribution, rather than probability distributions. We propose a multi-objective Portfolio based model and added another entropy objective function to generate a well diversified asset portfolio within optimal asset allocation. For quantifying any potential return and risk, portfolio liquidity is taken into account and a multi-objective non-linear programming model for portfolio rebalancing with transaction cost is proposed. The models are illustrated with numerical examples.

An Evolutionary Approach for Bilevel Multi-objective Problems

Science.gov (United States)

Deb, Kalyanmoy; Sinha, Ankur

Evolutionary multi-objective optimization (EMO) algorithms have been extensively applied to find multiple near Pareto-optimal solutions over the past 15 years or so. However, EMO algorithms for solving bilevel multi-objective optimization problems have not received adequate attention yet. These problems appear in many applications in practice and involve two levels, each comprising of multiple conflicting objectives. These problems require every feasible upper-level solution to satisfy optimality of a lower-level optimization problem, thereby making them difficult to solve. In this paper, we discuss a recently proposed bilevel EMO procedure and show its working principle on a couple of test problems and on a business decision-making problem. This paper should motivate other EMO researchers to engage more into this important optimization task of practical importance.

Conflicting Multi-Objective Compatible Optimization Control

OpenAIRE

Xu, Lihong; Hu, Qingsong; Hu, Haigen; Goodman, Erik

2010-01-01

Based on ideas developed in addressing practical greenhouse environmental control, we propose a new multi-objective compatible control method. Several detailed algorithms are proposed to meet the requirements of different kinds of problem: 1) A two-layer MOCC framework is presented for problems with a precise model; 2) To deal with situations

Standalone vertex finding in the ATLAS muon spectrometer

DEFF Research Database (Denmark)

Aad, A.; Abajyan, T.; Abbott, B.

2014-01-01

A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths. The perf......A dedicated reconstruction algorithm to find decay vertices in the ATLAS muon spectrometer is presented. The algorithm searches the region just upstream of or inside the muon spectrometer volume for multi-particle vertices that originate from the decay of particles with long decay paths....... The performance of the algorithm is evaluated using both a sample of simulated Higgs boson events, in which the Higgs boson decays to long-lived neutral particles that in turn decay to bbar b final states, and pp collision data at √s = 7 TeV collected with the ATLAS detector at the LHC during 2011....

Uncertain and multi-objective programming models for crop planting structure optimization

Directory of Open Access Journals (Sweden)

Mo LI,Ping GUO,Liudong ZHANG,Chenglong ZHANG

2016-03-01

Full Text Available Crop planting structure optimization is a significant way to increase agricultural economic benefits and improve agricultural water management. The complexities of fluctuating stream conditions, varying economic profits, and uncertainties and errors in estimated modeling parameters, as well as the complexities among economic, social, natural resources and environmental aspects, have led to the necessity of developing optimization models for crop planting structure which consider uncertainty and multi-objectives elements. In this study, three single-objective programming models under uncertainty for crop planting structure optimization were developed, including an interval linear programming model, an inexact fuzzy chance-constrained programming (IFCCP model and an inexact fuzzy linear programming (IFLP model. Each of the three models takes grayness into account. Moreover, the IFCCP model considers fuzzy uncertainty of parameters/variables and stochastic characteristics of constraints, while the IFLP model takes into account the fuzzy uncertainty of both constraints and objective functions. To satisfy the sustainable development of crop planting structure planning, a fuzzy-optimization-theory-based fuzzy linear multi-objective programming model was developed, which is capable of reflecting both uncertainties and multi-objective. In addition, a multi-objective fractional programming model for crop structure optimization was also developed to quantitatively express the multi-objective in one optimization model with the numerator representing maximum economic benefits and the denominator representing minimum crop planting area allocation. These models better reflect actual situations, considering the uncertainties and multi-objectives of crop planting structure optimization systems. The five models developed were then applied to a real case study in Minqin County, north-west China. The advantages, the applicable conditions and the solution methods

Multi-mode optical fibers for connecting space-based spectrometers

Science.gov (United States)

Roberts, W. T.; Lindenmisth, C. A.; Bender, S.; Miller, E. A.; Motts, E.; Ott, M.; LaRocca, F.; Thomes, J.

2017-11-01

significantly smaller, less massive and less robust. Large core multi-mode optical fibers are often used to accommodate the optical connection of the two separated portions of such instrumentation. In some cases, significant throughput efficiency improvement can be realized by judiciously orienting the strands of multi-fiber cable, close-bunching them to accommodate a tight focus of the optical system on the optical side of the connection, and splaying them out linearly along a spectrometer slit on the other end. For such instrumentation to work effectively in identifying elements and molecules, and especially to produce accurate quantitative results, the spectral throughput of the optical fiber connection must be consistent over varying temperatures, over the range of motion of the optical head (and it's implied optical cable stresses), and over angle-aperture invariant of the total system. While the first two of these conditions have been demonstrated[4], spectral observations of the latter present a cause for concern, and may have an impact on future design of fiber-connected LIBS and Raman spectroscopy instruments. In short, we have observed that the shape of the spectral efficiency curve of a large multi-mode core optical fiber changes as a function of input angle.

Multi-objective optimal power flow with FACTS devices

International Nuclear Information System (INIS)

Basu, M.

2011-01-01

This paper presents multi-objective differential evolution to optimize cost of generation, emission and active power transmission loss of flexible ac transmission systems (FACTS) device-equipped power systems. In the proposed approach, optimal power flow problem is formulated as a multi-objective optimization problem. FACTS devices considered include thyristor controlled series capacitor (TCSC) and thyristor controlled phase shifter (TCPS). The proposed approach has been examined and tested on the modified IEEE 30-bus and 57-bus test systems. The results obtained from the proposed approach have been compared with those obtained from nondominated sorting genetic algorithm-II, strength pareto evolutionary algorithm 2 and pareto differential evolution.

Multi-objective engineering design using preferences

Science.gov (United States)

Sanchis, J.; Martinez, M.; Blasco, X.

2008-03-01

System design is a complex task when design parameters have to satisy a number of specifications and objectives which often conflict with those of others. This challenging problem is called multi-objective optimization (MOO). The most common approximation consists in optimizing a single cost index with a weighted sum of objectives. However, once weights are chosen the solution does not guarantee the best compromise among specifications, because there is an infinite number of solutions. A new approach can be stated, based on the designer's experience regarding the required specifications and the associated problems. This valuable information can be translated into preferences for design objectives, and will lead the search process to the best solution in terms of these preferences. This article presents a new method, which enumerates these a priori objective preferences. As a result, a single objective is built automatically and no weight selection need be performed. Problems occuring because of the multimodal nature of the generated single cost index are managed with genetic algorithms (GAs).

PARETO OPTIMAL SOLUTIONS FOR MULTI-OBJECTIVE GENERALIZED ASSIGNMENT PROBLEM

Directory of Open Access Journals (Sweden)

S. Prakash

2012-01-01

Full Text Available

ENGLISH ABSTRACT: The Multi-Objective Generalized Assignment Problem (MGAP with two objectives, where one objective is linear and the other one is non-linear, has been considered, with the constraints that a job is assigned to only one worker – though he may be assigned more than one job, depending upon the time available to him. An algorithm is proposed to find the set of Pareto optimal solutions of the problem, determining assignments of jobs to workers with two objectives without setting priorities for them. The two objectives are to minimise the total cost of the assignment and to reduce the time taken to complete all the jobs.

AFRIKAANSE OPSOMMING: ‘n Multi-doelwit veralgemeende toekenningsprobleem (“multi-objective generalised assignment problem – MGAP” met twee doelwitte, waar die een lineêr en die ander nielineêr is nie, word bestudeer, met die randvoorwaarde dat ‘n taak slegs toegedeel word aan een werker – alhoewel meer as een taak aan hom toegedeel kan word sou die tyd beskikbaar wees. ‘n Algoritme word voorgestel om die stel Pareto-optimale oplossings te vind wat die taaktoedelings aan werkers onderhewig aan die twee doelwitte doen sonder dat prioriteite toegeken word. Die twee doelwitte is om die totale koste van die opdrag te minimiseer en om die tyd te verminder om al die take te voltooi.

Joint Conditional Random Field Filter for Multi-Object Tracking

Directory of Open Access Journals (Sweden)

Luo Ronghua

2011-03-01

Full Text Available Object tracking can improve the performance of mobile robot especially in populated dynamic environments. A novel joint conditional random field Filter (JCRFF based on conditional random field with hierarchical structure is proposed for multi-object tracking by abstracting the data associations between objects and measurements to be a sequence of labels. Since the conditional random field makes no assumptions about the dependency structure between the observations and it allows non-local dependencies between the state and the observations, the proposed method can not only fuse multiple cues including shape information and motion information to improve the stability of tracking, but also integrate moving object detection and object tracking quite well. At the same time, implementation of multi-object tracking based on JCRFF with measurements from the laser range finder on a mobile robot is studied. Experimental results with the mobile robot developed in our lab show that the proposed method has higher precision and better stability than joint probabilities data association filter (JPDAF.

A procedure for multi-objective optimization of tire design parameters

OpenAIRE

Nikola Korunović; Miloš Madić; Miroslav Trajanović; Miroslav Radovanović

2015-01-01

The identification of optimal tire design parameters for satisfying different requirements, i.e. tire performance characteristics, plays an essential role in tire design. In order to improve tire performance characteristics, formulation and solving of multi-objective optimization problem must be performed. This paper presents a multi-objective optimization procedure for determination of optimal tire design parameters for simultaneous minimization of strain energy density at two distinctive zo...

A Survey of Multi-Objective Sequential Decision-Making

OpenAIRE

Roijers, D.M.; Vamplew, P.; Whiteson, S.; Dazeley, R.

2013-01-01

Sequential decision-making problems with multiple objectives arise naturally in practice and pose unique challenges for research in decision-theoretic planning and learning, which has largely focused on single-objective settings. This article surveys algorithms designed for sequential decision-making problems with multiple objectives. Though there is a growing body of literature on this subject, little of it makes explicit under what circumstances special methods are needed to solve multi-obj...

Precision measurements with the multi-reflection time-of-flight mass spectrometer of ISOLTRAP at ISOLDE/CERN

Energy Technology Data Exchange (ETDEWEB)

Atanasov, Dinko; Ascher, Pauline; Borgmann, Christopher; Boehm, Christine; Eliseev, Sergey; Eronen, Tommi; George, Sebastian; Kisler, Dmitry; Naimi, Sarah [Max-Planck-Institut fuer Kernphysik, Saupfercheckweg 1, 69117 Heidelberg (Germany); Beck, Dietrich; Herfurth, Frank; Litvinov, Yuri; Minaya Ramirez, Enrique; Neidherr, Dennis [GSI Helmholtzzentrum fuer Schwerionenforschung GmbH, Planckstr. 1, 64291 Darmstadt (Germany); Breitenfeldt, Martin [Instituut voor Kern- en Stralingsfysica, Celestijnenlaan 200d - bus 2418, 3001 Heverlee (Belgium); Cakirli, Burcu [University of Istanbul, Department of Physics, 34134 Istanbul (Turkey); Cocolios, Thomas Elias [University of Manchester, Manchester (United Kingdom); Herlert, Alexander Josef [FAIR GmbH, Planckstr. 1, D-64291 Darmstadt (Germany); Kowalska, Magdalena [CERN, Geneva 23, 1211 Geneva (Switzerland); Kreim, Susanne [Max-Planck-Institut fuer Kernphysik, Saupfercheckweg 1, 69117 Heidelberg (Germany); CERN, Geneva 23, 1211 Geneva (Switzerland); Lunney, David; Manea, Vladimir [CSNSM-IN2P3-CNRS, 91405 Orsay Campus, Bat. 104, 108 (France); Rosenbusch, Marco; Schweikhard, Lutz; Wienholtz, Frank; Wolf, Robert [Ernst-Moritz-Arndt-Universitaet, Institut fuer Physik, Felix-Hausdorff-Str. 6, 17487 Greifswald (Germany); Stanja, Juliane; Zuber, Kai [Institut fuer Kern- und Teilchenphysik, Technische Universitaet Dresden, Zellescher Weg 19, 01069 Dresden (Germany)

2014-07-01

The masses of exotic nuclides are among the most important input parameters for modern nuclear theory and astrophysical models. At the high-precision Penning-trap mass spectrometer ISOLTRAP at ISOLDE/CERN, a multi-reflection time-of-flight mass spectrometer (MR-ToF-MS) in combination with a Bradbury-Nielsen gate (BNG) can be used to achieve high-resolution isobar purification with mass-resolving powers of 105 in a few tens of milliseconds. Furthermore, the MR-ToF device can be used as a spectrometer to determine the masses of nuclides with very low yields and short half-lives, where a Penning-trap mass measurement becomes impractical due to the lower transport efficiency and decay losses during the purification and measurement cycles. Recent cross-check experiments show that the MR-ToF MS allows mass measurements with uncertainties in the sub-ppm range. In a first application the mass measurements of the nuclides 53,54Ca was performed, delivered with production rates as low as 10/s and half-lives of only 90(6) ms. The nuclides serve as important benchmarks for testing modern chiral effective theory with realistic 3-body forces. The contribution presents the on-line mass spectrometer ISOLTRAP focusing on the new applications, which became possible after the implementation of the MR-ToF MS into the current setup. In particular, the mass measurements of the neutron-rich calcium isotopes up to A=54 are discussed. In addition, measurements of the isotonic potassium isotopes are reported.

Irrigation water allocation optimization using multi-objective evolutionary algorithm (MOEA) - a review

Science.gov (United States)

Fanuel, Ibrahim Mwita; Mushi, Allen; Kajunguri, Damian

2018-03-01

This paper analyzes more than 40 papers with a restricted area of application of Multi-Objective Genetic Algorithm, Non-Dominated Sorting Genetic Algorithm-II and Multi-Objective Differential Evolution (MODE) to solve the multi-objective problem in agricultural water management. The paper focused on different application aspects which include water allocation, irrigation planning, crop pattern and allocation of available land. The performance and results of these techniques are discussed. The review finds that there is a potential to use MODE to analyzed the multi-objective problem, the application is more significance due to its advantage of being simple and powerful technique than any Evolutionary Algorithm. The paper concludes with the hopeful new trend of research that demand effective use of MODE; inclusion of benefits derived from farm byproducts and production costs into the model.

Multi-Objective Weather Routing of Sailing Vessels

Directory of Open Access Journals (Sweden)

Życzkowski Marcin

2017-12-01

Full Text Available The paper presents a multi-objective deterministic method of weather routing for sailing vessels. Depending on a particular purpose of sailboat weather routing, the presented method makes it possible to customize the criteria and constraints so as to fit a particular user’s needs. Apart from a typical shortest time criterion, safety and comfort can also be taken into account. Additionally, the method supports dynamic weather data: in its present version short-term, mid-term and long-term term weather forecasts are used during optimization process. In the paper the multi-objective optimization problem is first defined and analysed. Following this, the proposed method solving this problem is described in detail. The method has been implemented as an online SailAssistance application. Some representative examples solutions are presented, emphasizing the effects of applying different criteria or different values of customized parameters.

A new three-tier architecture design for multi-sphere neutron spectrometer with the FLUKA code

Science.gov (United States)

Huang, Hong; Yang, Jian-Bo; Tuo, Xian-Guo; Liu, Zhi; Wang, Qi-Biao; Wang, Xu

2016-07-01

The current commercially, available Bonner sphere neutron spectrometer (BSS) has high sensitivity to neutrons below 20 MeV, which causes it to be poorly placed to measure neutrons ranging from a few MeV to 100 MeV. The paper added moderator layers and the auxiliary material layer upon 3He proportional counters with FLUKA code, with a view to improve. The results showed that the responsive peaks to neutrons below 20 MeV gradually shift to higher energy region and decrease slightly with the increasing moderator thickness. On the contrary, the response for neutrons above 20 MeV was always very low until we embed auxiliary materials such as copper (Cu), lead (Pb), tungsten (W) into moderator layers. This paper chose the most suitable auxiliary material Pb to design a three-tier architecture multi-sphere neutron spectrometer (NBSS). Through calculating and comparing, the NBSS was advantageous in terms of response for 5-100 MeV and the highest response was 35.2 times the response of polyethylene (PE) ball with the same PE thickness.

Design of a centrifugal compressor impeller using multi-objective optimization algorithm

International Nuclear Information System (INIS)

Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong; Choi, Jae Ho

2009-01-01

This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ε-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

Design of a centrifugal compressor impeller using multi-objective optimization algorithm

Energy Technology Data Exchange (ETDEWEB)

Kim, Jin Hyuk; Husain, Afzal; Kim, Kwang Yong [Inha University, Incheon (Korea, Republic of); Choi, Jae Ho [Samsung Techwin Co., Ltd., Changwon (Korea, Republic of)

2009-07-01

This paper presents a design optimization of a centrifugal compressor impeller with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by finite volume approximations and solved on hexahedral grids for flow analyses. Two objectives, i.e., isentropic efficiency and total pressure ratio are selected with four design variables defining impeller hub and shroud contours in meridional contours to optimize the system. Non-dominated Sorting of Genetic Algorithm (NSGA-II) with {epsilon}-constraint strategy for local search coupled with Radial Basis Neural Network model is used for multi-objective optimization. The optimization results show that isentropic efficiencies and total pressure ratios of the five cluster points at the Pareto-optimal solutions are enhanced by multi-objective optimization.

High performance pseudo-analytical simulation of multi-object adaptive optics over multi-GPU systems

KAUST Repository

Abdelfattah, Ahmad; Gendron, É ric; Gratadour, Damien; Keyes, David E.; Ltaief, Hatem; Sevin, Arnaud; Vidal, Fabrice

2014-01-01

Multi-object adaptive optics (MOAO) is a novel adaptive optics (AO) technique dedicated to the special case of wide-field multi-object spectrographs (MOS). It applies dedicated wavefront corrections to numerous independent tiny patches spread over a large field of view (FOV). The control of each deformable mirror (DM) is done individually using a tomographic reconstruction of the phase based on measurements from a number of wavefront sensors (WFS) pointing at natural and artificial guide stars in the field. The output of this study helps the design of a new instrument called MOSAIC, a multi-object spectrograph proposed for the European Extremely Large Telescope (E-ELT). We have developed a novel hybrid pseudo-analytical simulation scheme that allows us to accurately simulate in detail the tomographic problem. The main challenge resides in the computation of the tomographic reconstructor, which involves pseudo-inversion of a large dense symmetric matrix. The pseudo-inverse is computed using an eigenvalue decomposition, based on the divide and conquer algorithm, on multicore systems with multi-GPUs. Thanks to a new symmetric matrix-vector product (SYMV) multi-GPU kernel, our overall implementation scores significant speedups over standard numerical libraries on multicore, like Intel MKL, and up to 60% speedups over the standard MAGMA implementation on 8 Kepler K20c GPUs. At 40,000 unknowns, this appears to be the largest-scale tomographic AO matrix solver submitted to computation, to date, to our knowledge and opens new research directions for extreme scale AO simulations. © 2014 Springer International Publishing Switzerland.

Hybrid Robust Multi-Objective Evolutionary Optimization Algorithm

Science.gov (United States)

2009-03-10

xfar by xint. Else, generate a new individual, using the Sobol pseudo- random sequence generator within the upper and lower bounds of the variables...12. Deb, K., Multi-Objective Optimization Using Evolutionary Algorithms, John Wiley & Sons. 2002. 13. Sobol , I. M., "Uniformly Distributed Sequences

A multi-objective approach for developing national energy efficiency plans

International Nuclear Information System (INIS)

Haydt, Gustavo; Leal, Vítor; Dias, Luís

2014-01-01

This paper proposes a new approach to deal with the problem of building national energy efficiency (EE) plans, considering multiple objectives instead of only energy savings. The objectives considered are minimizing the influence of energy use on climate change, minimizing the financial risk from the investment, maximizing the security of energy supply, minimizing investment costs, minimizing the impacts of building new power plants and transmission infrastructures, and maximizing the local air quality. These were identified through literature review and interaction with real decision makers. A database of measures is established, from which millions of potential EE plans can be built by combining measures and their respective degree of implementation. Finally, a hybrid multi-objective and multi-criteria decision analysis (MCDA) model is proposed to search and select the EE plans that best match the decision makers’ preferences. An illustration of the working mode and the type of results obtained from this novel hybrid model is provided through an application to Portugal. For each of five decision perspectives a wide range of potential best plans were identified. These wide ranges show the relevance of introducing multi-objective analysis in a comprehensive search space as a tool to inform decisions about national EE plans. - Highlights: • A multiple objective approach to aid the choice of national energy efficiency plans. • A hybrid multi-objective MCDA model is proposed to search among the possible plans. • The model identified relevant plans according to five different idealized DMs. • The approach is tested with Portugal

OBJECT-ORIENTED CHANGE DETECTION BASED ON MULTI-SCALE APPROACH

Directory of Open Access Journals (Sweden)

Y. Jia

2016-06-01

Full Text Available The change detection of remote sensing images means analysing the change information quantitatively and recognizing the change types of the surface coverage data in different time phases. With the appearance of high resolution remote sensing image, object-oriented change detection method arises at this historic moment. In this paper, we research multi-scale approach for high resolution images, which includes multi-scale segmentation, multi-scale feature selection and multi-scale classification. Experimental results show that this method has a stronger advantage than the traditional single-scale method of high resolution remote sensing image change detection.

Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

Science.gov (United States)

Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

2015-04-01

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

Spectrometer for shot-to-shot photon energy characterization in the multi-bunch mode of the free electron laser at Hamburg

International Nuclear Information System (INIS)

Palutke, S.; Wurth, W.; Gerken, N. C.; Mertens, K.; Klumpp, S.; Martins, M.; Mozzanica, A.; Schmitt, B.; Wunderer, C.; Graafsma, H.; Meiwes-Broer, K.-H.

2015-01-01

The setup and first results from commissioning of a fast online photon energy spectrometer for the vacuum ultraviolet free electron laser at Hamburg (FLASH) at DESY are presented. With the use of the latest advances in detector development, the presented spectrometer reaches readout frequencies up to 1 MHz. In this paper, we demonstrate the ability to record online photon energy spectra on a shot-to-shot base in the multi-bunch mode of FLASH. Clearly resolved shifts in the mean wavelength over the pulse train as well as shot-to-shot wavelength fluctuations arising from the statistical nature of the photon generating self-amplified spontaneous emission process have been observed. In addition to an online tool for beam calibration and photon diagnostics, the spectrometer enables the determination and selection of spectral data taken with a transparent experiment up front over the photon energy of every shot. This leads to higher spectral resolutions without the loss of efficiency or photon flux by using single-bunch mode or monochromators

A hybrid multi-objective evolutionary algorithm approach for ...

Indian Academy of Sciences (India)

V K MANUPATI

for handling sequence- and machine-dependent set-up times ... algorithm has been compared to that of multi-objective particle swarm optimization (MOPSO) and conventional ..... position and cognitive learning factor are considered for.

Absolute sensitivity calibration from 20 A to 430 A of a grazing incidence spectrometer with a multi-element spectral detector

International Nuclear Information System (INIS)

Terry, J.L.; Manning, H.L.; Marmar, E.S.

1986-07-01

Two methods which together allow sensitivity calibration from 20 A to 430 A are described in detail. The first method, useful up to 120 A, uses a low power source to generate Kα x-rays which are alternately viewed by an absolute detector (a proportional counter) and the spectrometer. The second method extends that calibration to 430 A. It relies on the 2:1 brightness ratio of bright doublet lines from impurity ions which have a single outer shell electron and which are present in hot, magnetically confined plasmas. It requires that the absolute sensitivity of the spectrometer be known at one wavelength point, and in practice requires a multi-element spectral detector

Recent developments in the Thomson Parabola Spectrometer diagnostic for laser-driven multi-species ion sources

International Nuclear Information System (INIS)

Alejo, A.; Gwynne, D.; Doria, D.; Ahmed, H.; Borghesi, M.; Kar, S.; Carroll, D.C.; Clarke, R.J.; Neely, D.; Scott, G.G.

2016-01-01

Ongoing developments in laser-driven ion acceleration warrant appropriate modifications to the standard Thomson Parabola Spectrometer (TPS) arrangement in order to match the diagnostic requirements associated to the particular and distinctive properties of laser-accelerated beams. Here we present an overview of recent developments by our group of the TPS diagnostic aimed to enhance the capability of diagnosing multi-species high-energy ion beams. In order to facilitate discrimination between ions with same Z / A , a recursive differential filtering technique was implemented at the TPS detector in order to allow only one of the overlapping ion species to reach the detector, across the entire energy range detectable by the TPS. In order to mitigate the issue of overlapping ion traces towards the higher energy part of the spectrum, an extended, trapezoidal electric plates design was envisaged, followed by its experimental demonstration. The design allows achieving high energy-resolution at high energies without sacrificing the lower energy part of the spectrum. Finally, a novel multi-pinhole TPS design is discussed, that would allow angularly resolved, complete spectral characterization of the high-energy, multi-species ion beams.

Recent developments in the Thomson Parabola Spectrometer diagnostic for laser-driven multi-species ion sources

Science.gov (United States)

Alejo, A.; Gwynne, D.; Doria, D.; Ahmed, H.; Carroll, D. C.; Clarke, R. J.; Neely, D.; Scott, G. G.; Borghesi, M.; Kar, S.

2016-10-01

Ongoing developments in laser-driven ion acceleration warrant appropriate modifications to the standard Thomson Parabola Spectrometer (TPS) arrangement in order to match the diagnostic requirements associated to the particular and distinctive properties of laser-accelerated beams. Here we present an overview of recent developments by our group of the TPS diagnostic aimed to enhance the capability of diagnosing multi-species high-energy ion beams. In order to facilitate discrimination between ions with same Z/A, a recursive differential filtering technique was implemented at the TPS detector in order to allow only one of the overlapping ion species to reach the detector, across the entire energy range detectable by the TPS. In order to mitigate the issue of overlapping ion traces towards the higher energy part of the spectrum, an extended, trapezoidal electric plates design was envisaged, followed by its experimental demonstration. The design allows achieving high energy-resolution at high energies without sacrificing the lower energy part of the spectrum. Finally, a novel multi-pinhole TPS design is discussed, that would allow angularly resolved, complete spectral characterization of the high-energy, multi-species ion beams.

Effective multi-objective optimization of Stirling engine systems

International Nuclear Information System (INIS)

Punnathanam, Varun; Kotecha, Prakash

2016-01-01

Highlights: • Multi-objective optimization of three recent Stirling engine models. • Use of efficient crossover and mutation operators for real coded Genetic Algorithm. • Demonstrated supremacy of the strategy over the conventionally used algorithm. • Improvements of up to 29% in comparison to literature results. - Abstract: In this article we demonstrate the supremacy of the Non-dominated Sorting Genetic Algorithm-II with Simulated Binary Crossover and Polynomial Mutation operators for the multi-objective optimization of Stirling engine systems by providing three examples, viz., (i) finite time thermodynamic model, (ii) Stirling engine thermal model with associated irreversibility and (iii) polytropic finite speed based thermodynamics. The finite time thermodynamic model involves seven decision variables and consists of three objectives: output power, thermal efficiency and rate of entropy generation. In comparison to literature, it was observed that the used strategy provides a better Pareto front and leads to improvements of up to 29%. The performance is also evaluated on a Stirling engine thermal model which considers the associated irreversibility of the cycle and consists of three objectives involving eleven decision variables. The supremacy of the suggested strategy is also demonstrated on the experimentally validated polytropic finite speed thermodynamics based Stirling engine model for optimization involving two objectives and ten decision variables.

FGP Approach for Solving Multi-level Multi-objective Quadratic Fractional Programming Problem with Fuzzy parameters

Directory of Open Access Journals (Sweden)

m. s. osman

2017-09-01

Full Text Available In this paper, we consider fuzzy goal programming (FGP approach for solving multi-level multi-objective quadratic fractional programming (ML-MOQFP problem with fuzzy parameters in the constraints. Firstly, the concept of the ?-cut approach is applied to transform the set of fuzzy constraints into a common deterministic one. Then, the quadratic fractional objective functions in each level are transformed into quadratic objective functions based on a proposed transformation. Secondly, the FGP approach is utilized to obtain a compromise solution for the ML-MOQFP problem by minimizing the sum of the negative deviational variables. Finally, an illustrative numerical example is given to demonstrate the applicability and performance of the proposed approach.

Convex Coverage Set Methods for Multi-Objective Collaborative Decision Making

NARCIS (Netherlands)

Roijers, D.M.; Lomuscio, A.; Scerri, P.; Bazzan, A.; Huhns, M.

2014-01-01

My research is aimed at finding efficient coordination methods for multi-objective collaborative multi-agent decision theoretic planning. Key to coordinating efficiently in these settings is exploiting loose couplings between agents. We proposed two algorithms for the case in which the agents need

Multi-objective and multi-criteria optimization for power generation expansion planning with CO2 mitigation in Thailand

Directory of Open Access Journals (Sweden)

Kamphol Promjiraprawat

2013-06-01

Full Text Available In power generation expansion planning, electric utilities have encountered the major challenge of environmental awareness whilst being concerned with budgetary burdens. The approach for selecting generating technologies should depend on economic and environmental constraint as well as externalities. Thus, the multi-objective optimization becomes a more attractive approach. This paper presents a hybrid framework of multi-objective optimization and multi-criteria decision making to solve power generation expansion planning problems in Thailand. In this paper, CO2 emissions and external cost are modeled as a multi-objective optimization problem. Then the analytic hierarchy process is utilized to determine thecompromised solution. For carbon capture and storage technology, CO2 emissions can be mitigated by 74.7% from the least cost plan and leads to the reduction of the external cost of around 500 billion US dollars over the planning horizon. Results indicate that the proposed approach provides optimum cost-related CO2 mitigation plan as well as external cost.

Multi-objective Search-based Mobile Testing

OpenAIRE

Mao, K.

2017-01-01

Despite the tremendous popularity of mobile applications, mobile testing still relies heavily on manual testing. This thesis presents mobile test automation approaches based on multi-objective search. We introduce three approaches: Sapienz (for native Android app testing), Octopuz (for hybrid/web JavaScript app testing) and Polariz (for using crowdsourcing to support search-based mobile testing). These three approaches represent the primary scientific and technical contributions of the thesis...

Multi-objective decision-making model based on CBM for an aircraft fleet

Science.gov (United States)

Luo, Bin; Lin, Lin

2018-04-01

Modern production management patterns, in which multi-unit (e.g., a fleet of aircrafts) are managed in a holistic manner, have brought new challenges for multi-unit maintenance decision making. To schedule a good maintenance plan, not only does the individual machine maintenance have to be considered, but also the maintenance of the other individuals have to be taken into account. Since most condition-based maintenance researches for aircraft focused on solely reducing maintenance cost or maximizing the availability of single aircraft, as well as considering that seldom researches concentrated on both the two objectives: minimizing cost and maximizing the availability of a fleet (total number of available aircraft in fleet), a multi-objective decision-making model based on condition-based maintenance concentrated both on the above two objectives is established. Furthermore, in consideration of the decision maker may prefer providing the final optimal result in the form of discrete intervals instead of a set of points (non-dominated solutions) in real decision-making problem, a novel multi-objective optimization method based on support vector regression is proposed to solve the above multi-objective decision-making model. Finally, a case study regarding a fleet is conducted, with the results proving that the approach efficiently generates outcomes that meet the schedule requirements.

Multi-objective approach in thermoenvironomic optimization of a benchmark cogeneration system

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn

2009-01-01

Multi-objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the exergetic, economic and environmental aspects have been considered, simultaneously. The thermodynamic modeling has been implemented comprehensively while economic analysis conducted in accordance with the total revenue requirement (TRR) method. The results for the single objective thermoeconomic optimization have been compared with the previous studies in optimization of CGAM problem. In multi-objective optimization of the CGAM problem, the three objective functions including the exergetic efficiency, total levelized cost rate of the system product and the cost rate of environmental impact have been considered. The environmental impact objective function has been defined and expressed in cost terms. This objective has been integrated with the thermoeconomic objective to form a new unique objective function known as a thermoenvironomic objective function. The thermoenvironomic objective has been minimized while the exergetic objective has been maximized. One of the most suitable optimization techniques developed using a particular class of search algorithms known as multi-objective evolutionary algorithms (MOEAs) has been considered here. This approach which is developed based on the genetic algorithm has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of decision-making has been presented and a final optimal solution has been introduced. The sensitivity of the solutions to the interest rate and the fuel cost has been studied

Integrated production planning and control: A multi-objective optimization model

Directory of Open Access Journals (Sweden)

Cheng Wang

2013-09-01

Full Text Available Purpose: Production planning and control has crucial impact on the production and business activities of enterprise. Enterprise Resource Planning (ERP is the most popular resources planning and management system, however there are some shortcomings and deficiencies in the production planning and control because its core component is still the Material Requirements Planning (MRP. For the defects of ERP system, many local improvement and optimization schemes have been proposed, and improve the feasibility and practicality of the plan in some extent, but study considering the whole planning system optimization in the multiple performance management objectives and achieving better application performance is less. The purpose of this paper is to propose a multi-objective production planning optimization model Based on the point of view of the integration of production planning and control, in order to achieve optimization and control of enterprise manufacturing management. Design/methodology/approach: On the analysis of ERP planning system’s defects and disadvantages, and related research and literature, a multi-objective production planning optimization model is proposed, in addition to net demand and capacity, multiple performance management objectives, such as on-time delivery, production balance, inventory, overtime production, are considered incorporating into the examination scope of the model, so that the manufacturing process could be management and controlled Optimally between multiple objectives. The validity and practicability of the model will be verified by the instance in the last part of the paper. Findings: The main finding is that production planning management of manufacturing enterprise considers not only the capacity and materials, but also a variety of performance management objectives in the production process, and building a multi-objective optimization model can effectively optimize the management and control of enterprise

Exploring the trade-off between competing objectives for electricity energy retailers through a novel multi-objective framework

International Nuclear Information System (INIS)

Charwand, Mansour; Ahmadi, Abdollah; Siano, Pierluigi; Dargahi, Vahid; Sarno, Debora

2015-01-01

Highlights: • Proposing a new stochastic multi-objective framework for an electricity retailer. • Proposing a MIP model for an electricity retailer problem. • Employing ε-constraint method to generate Pareto solution. - Abstract: Energy retailer is the intermediary between Generation Companies and consumers. In the medium time horizon, in order to gain market share, he has to minimize his selling price while looking at the profit, which is dependent on the revenues from selling and the costs to buy energy from forward contracts and participation in the market pool. In this paper, the two competing objectives are engaged proposing a new multi-objective framework in which a ε-constraint mathematical technique is used to produce the Pareto front (set of optimal solutions). The stochasticity of energy prices in the market and customer load demand are coped with the Lattice Monte Carlo Simulation (LMCS) and the method of the roulette wheel, which allow the stochastic multi-objective problem to be turned into a set of deterministic equivalents. The method performance is tested into some case studies

Use of interactive data visualization in multi-objective forest planning.

Science.gov (United States)

Haara, Arto; Pykäläinen, Jouni; Tolvanen, Anne; Kurttila, Mikko

2018-03-15

Common to multi-objective forest planning situations is that they all require comparisons, searches and evaluation among decision alternatives. Through these actions, the decision maker can learn from the information presented and thus make well-justified decisions. Interactive data visualization is an evolving approach that supports learning and decision making in multidimensional decision problems and planning processes. Data visualization contributes the formation of mental image data and this process is further boosted by allowing interaction with the data. In this study, we introduce a multi-objective forest planning decision problem framework and the corresponding characteristics of data. We utilize the framework with example planning data to illustrate and evaluate the potential of 14 interactive data visualization techniques to support multi-objective forest planning decisions. Furthermore, broader utilization possibilities of these techniques to incorporate the provisioning of ecosystem services into forest management and planning are discussed. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multi-objective optimization for generating a weighted multi-model ensemble

Science.gov (United States)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic

Multi-element analysis of unidentified fallen objects from Tatale in ...

African Journals Online (AJOL)

A multi-element analysis has been carried out on two fallen objects, # 01 and # 02, using instrumental neutron activation analysis technique. A total of 17 elements were identified in object # 01 while 21 elements were found in object # 02. The two major elements in object # 01 were Fe and Mg, which together constitute ...

A new small-angle neutron scattering spectrometer at China Mianyang research reactor

Energy Technology Data Exchange (ETDEWEB)

Peng, Mei, E-mail: pm740509@163.com; Sun, Liangwei; Chen, Liang; Sun, Guangai; Chen, Bo; Xie, Chaomei; Xia, Qingzhong; Yan, Guanyun; Tian, Qiang; Huang, Chaoqiang; Pang, Beibei; Zhang, Ying; Wang, Yun; Liu, Yaoguang; Kang, Wu; Gong, Jian

2016-02-21

A new pinhole small-angle neutron scattering (SANS) spectrometer, installed at the cold neutron source of the 20 MW China Mianyang Research Reactor (CMRR) in the Institute of Nuclear Physics and Chemistry, China Academy of Engineering Physics, has been put into use since 2014. The spectrometer is equipped with a multi-blade mechanical velocity selector, a multi-beam collimation system, and a two-dimensional He-3 position sensitive neutron detector. The q-range of the spectrometer covers from 0.01 nm{sup −1} to 5.0 nm{sup −1}. In this paper, the design and characteristics of the SANS spectrometer are described. The q-resolution calculations, together with calibration measurements of silver behenate and a dispersion of nearly monodisperse poly-methyl-methacrylate nanoparticles indicate that our SANS spectrometer has a good performance and is now in routine service. - Highlights: • A new SANS spectrometer has been put into use since 2014 in China. • One MBR selector possesses a higher resolution compared with traditional selector is used. • The spectrometer has a good performance and is now in routinely service.

Optimization of time-correlated single photon counting spectrometer

International Nuclear Information System (INIS)

Zhang Xiufeng; Du Haiying; Sun Jinsheng

2011-01-01

The paper proposes a performance improving scheme for the conventional time-correlated single photon counting spectrometer and develops a high speed data acquisition card based on PCI bus and FPGA technologies. The card is used to replace the multi-channel analyzer to improve the capability and decrease the volume of the spectrometer. The process of operation is introduced along with the integration of the spectrometer system. Many standard samples are measured. The experimental results show that the sensitivity of the spectrometer is single photon counting, and the time resolution of fluorescence lifetime measurement can be picosecond level. The instrument could measure the time-resolved spectroscopy. (authors)

Exergoeconomic multi objective optimization and sensitivity analysis of a regenerative Brayton cycle

International Nuclear Information System (INIS)

Naserian, Mohammad Mahdi; Farahat, Said; Sarhaddi, Faramarz

2016-01-01

Highlights: • Finite time exergoeconomic multi objective optimization of a Brayton cycle. • Comparing the exergoeconomic and the ecological function optimization results. • Inserting the cost of fluid streams concept into finite-time thermodynamics. • Exergoeconomic sensitivity analysis of a regenerative Brayton cycle. • Suggesting the cycle performance curve drawing and utilization. - Abstract: In this study, the optimal performance of a regenerative Brayton cycle is sought through power maximization and then exergoeconomic optimization using finite-time thermodynamic concept and finite-size components. Optimizations are performed using genetic algorithm. In order to take into account the finite-time and finite-size concepts in current problem, a dimensionless mass-flow parameter is used deploying time variations. The decision variables for the optimum state (of multi objective exergoeconomic optimization) are compared to the maximum power state. One can see that the multi objective exergoeconomic optimization results in a better performance than that obtained with the maximum power state. The results demonstrate that system performance at optimum point of multi objective optimization yields 71% of the maximum power, but only with exergy destruction as 24% of the amount that is produced at the maximum power state and 67% lower total cost rate than that of the maximum power state. In order to assess the impact of the variation of the decision variables on the objective functions, sensitivity analysis is conducted. Finally, the cycle performance curve drawing according to exergoeconomic multi objective optimization results and its utilization, are suggested.

Qualification study of LiF flight crystals for the objective crystal spectrometer on the SPECTRUM-X-GAMMA satellite

DEFF Research Database (Denmark)

Christensen, Finn Erland; Rasmussen, I.; Schnopper, Herbert W.

1992-01-01

The Objective Crystal Spectrometer (OXS) on the SPECTRUM-X-GAMMA satellite will carry these types of natural crystals LiF(220), Ge(111) and RAP(001). They will be used to study, among others, the H- and the He-like emission from the cosmically important elements Fe, S, Ar and O. More than 300 Li...

A new approach for accurate mass assignment on a multi-turn time-of-flight mass spectrometer.

Science.gov (United States)

Hondo, Toshinobu; Jensen, Kirk R; Aoki, Jun; Toyoda, Michisato

2017-12-01

A simple, effective accurate mass assignment procedure for a time-of-flight mass spectrometer is desirable. External mass calibration using a mass calibration standard together with an internal mass reference (lock mass) is a common technique for mass assignment, however, using polynomial fitting can result in mass-dependent errors. By using the multi-turn time-of-flight mass spectrometer infiTOF-UHV, we were able to obtain multiple time-of-flight data from an ion monitored under several different numbers of laps that was then used to calculate a mass calibration equation. We have developed a data acquisition system that simultaneously monitors spectra at several different lap conditions with on-the-fly centroid determination and scan law estimation, which is a function of acceleration voltage, flight path, and instrumental time delay. Less than 0.9 mDa mass errors were observed for assigned mass to charge ratios ( m/z) ranging between 4 and 134 using only 40 Ar + as a reference. It was also observed that estimating the scan law on-the-fly provides excellent mass drift compensation.

Optical cryptography with biometrics for multi-depth objects.

Science.gov (United States)

Yan, Aimin; Wei, Yang; Hu, Zhijuan; Zhang, Jingtao; Tsang, Peter Wai Ming; Poon, Ting-Chung

2017-10-11

We propose an optical cryptosystem for encrypting images of multi-depth objects based on the combination of optical heterodyne technique and fingerprint keys. Optical heterodyning requires two optical beams to be mixed. For encryption, each optical beam is modulated by an optical mask containing either the fingerprint of the person who is sending, or receiving the image. The pair of optical masks are taken as the encryption keys. Subsequently, the two beams are used to scan over a multi-depth 3-D object to obtain an encrypted hologram. During the decryption process, each sectional image of the 3-D object is recovered by convolving its encrypted hologram (through numerical computation) with the encrypted hologram of a pinhole image that is positioned at the same depth as the sectional image. Our proposed method has three major advantages. First, the lost-key situation can be avoided with the use of fingerprints as the encryption keys. Second, the method can be applied to encrypt 3-D images for subsequent decrypted sectional images. Third, since optical heterodyning scanning is employed to encrypt a 3-D object, the optical system is incoherent, resulting in negligible amount of speckle noise upon decryption. To the best of our knowledge, this is the first time optical cryptography of 3-D object images has been demonstrated in an incoherent optical system with biometric keys.

SuperSpec, The On-Chip Spectrometer: Improved NEP and Antenna Performance

Science.gov (United States)

Wheeler, Jordan; Hailey-Dunsheath, S.; Shirokoff, E.; Barry, P. S.; Bradford, C. M.; Chapman, S.; Che, G.; Doyle, S.; Glenn, J.; Gordon, S.; Hollister, M.; Kovács, A.; LeDuc, H. G.; Mauskopf, P.; McGeehan, R.; McKenney, C.; Reck, T.; Redford, J.; Ross, C.; Shiu, C.; Tucker, C.; Turner, J.; Walker, S.; Zmuidzinas, J.

2018-05-01

SuperSpec is a new technology for mm and sub-mm spectroscopy. It is an on-chip spectrometer being developed for multi-object, moderate-resolution (R˜ 300 ), large bandwidth survey spectroscopy of high-redshift galaxies for the 1 mm atmospheric window. This band accesses the CO ladder in the redshift range of z = 0-4 and the [CII] 158 μm line from redshift z = 5-9. SuperSpec employs a novel architecture in which detectors are coupled to a series of resonant filters along a single microwave feedline instead of using dispersive optics. This construction allows for the creation of a full spectrometer occupying only ˜ 10 cm^2 of silicon, a reduction in size of several orders of magnitude when compared to standard grating spectrometers. This small profile enables the production of future multi-beam spectroscopic instruments envisioned for the millimeter band to measure the redshifts of dusty galaxies efficiently. The SuperSpec collaboration is currently pushing toward the deployment of a SuperSpec demonstration instrument in fall of 2018. The progress with the latest SuperSpec prototype devices is presented; reporting increased responsivity via a reduced inductor volume (2.6 μm^3 ) and the incorporation of a new broadband antenna. A detector NEP of 3-4 × 10^{-18} W/Hz^{0.5} is obtained, sufficient for background-limited observation on mountaintop sites. In addition, beam maps and efficiency measurements of a new wide-band dual bow-tie slot antenna are shown.

Characterisation of deuterium spectra from laser driven multi-species sources by employing differentially filtered image plate detectors in Thomson spectrometers

International Nuclear Information System (INIS)

Alejo, A.; Kar, S.; Ahmed, H.; Doria, D.; Green, A.; Jung, D.; Lewis, C. L. S.; Nersisyan, G.; Krygier, A. G.; Freeman, R. R.; Clarke, R.; Green, J. S.; Notley, M.; Fernandez, J.; Fuchs, J.; Kleinschmidt, A.; Roth, M.; Morrison, J. T.; Najmudin, Z.; Nakamura, H.

2014-01-01

A novel method for characterising the full spectrum of deuteron ions emitted by laser driven multi-species ion sources is discussed. The procedure is based on using differential filtering over the detector of a Thompson parabola ion spectrometer, which enables discrimination of deuterium ions from heavier ion species with the same charge-to-mass ratio (such as C 6+ , O 8+ , etc.). Commonly used Fuji Image plates were used as detectors in the spectrometer, whose absolute response to deuterium ions over a wide range of energies was calibrated by using slotted CR-39 nuclear track detectors. A typical deuterium ion spectrum diagnosed in a recent experimental campaign is presented, which was produced from a thin deuterated plastic foil target irradiated by a high power laser

Characterisation of deuterium spectra from laser driven multi-species sources by employing differentially filtered image plate detectors in Thomson spectrometers

Science.gov (United States)

Alejo, A.; Kar, S.; Ahmed, H.; Krygier, A. G.; Doria, D.; Clarke, R.; Fernandez, J.; Freeman, R. R.; Fuchs, J.; Green, A.; Green, J. S.; Jung, D.; Kleinschmidt, A.; Lewis, C. L. S.; Morrison, J. T.; Najmudin, Z.; Nakamura, H.; Nersisyan, G.; Norreys, P.; Notley, M.; Oliver, M.; Roth, M.; Ruiz, J. A.; Vassura, L.; Zepf, M.; Borghesi, M.

2014-09-01

A novel method for characterising the full spectrum of deuteron ions emitted by laser driven multi-species ion sources is discussed. The procedure is based on using differential filtering over the detector of a Thompson parabola ion spectrometer, which enables discrimination of deuterium ions from heavier ion species with the same charge-to-mass ratio (such as C6 +, O8 +, etc.). Commonly used Fuji Image plates were used as detectors in the spectrometer, whose absolute response to deuterium ions over a wide range of energies was calibrated by using slotted CR-39 nuclear track detectors. A typical deuterium ion spectrum diagnosed in a recent experimental campaign is presented, which was produced from a thin deuterated plastic foil target irradiated by a high power laser.

Characterisation of deuterium spectra from laser driven multi-species sources by employing differentially filtered image plate detectors in Thomson spectrometers

Energy Technology Data Exchange (ETDEWEB)

Alejo, A.; Kar, S., E-mail: s.kar@qub.ac.uk; Ahmed, H.; Doria, D.; Green, A.; Jung, D.; Lewis, C. L. S.; Nersisyan, G. [Centre for Plasma Physics, School of Mathematics and Physics, Queen' s University Belfast, Belfast BT7 1NN (United Kingdom); Krygier, A. G.; Freeman, R. R. [Department of Physics, The Ohio State University, Columbus, Ohio 43210 (United States); Clarke, R.; Green, J. S.; Notley, M. [Central Laser Facility, Rutherford Appleton Laboratory, Didcot, Oxfordshire OX11 0QX (United Kingdom); Fernandez, J. [Central Laser Facility, Rutherford Appleton Laboratory, Didcot, Oxfordshire OX11 0QX (United Kingdom); Instituto de Fusión Nuclear, Universidad Politécnica de Madrid, 28006 Madrid (Spain); Fuchs, J. [LULI, École Polytechnique, CNRS, CEA, UPMC, 91128 Palaiseau (France); Kleinschmidt, A.; Roth, M. [Institut für Kernphysik, Technische Universität Darmstadt, Schloßgartenstrasse 9, D-64289 Darmstadt (Germany); Morrison, J. T. [Propulsion Systems Directorate, Air Force Research Lab, Wright Patterson Air Force Base, Ohio 45433 (United States); Najmudin, Z.; Nakamura, H. [Blackett Laboratory, Department of Physics, Imperial College, London SW7 2AZ (United Kingdom); and others

2014-09-15

A novel method for characterising the full spectrum of deuteron ions emitted by laser driven multi-species ion sources is discussed. The procedure is based on using differential filtering over the detector of a Thompson parabola ion spectrometer, which enables discrimination of deuterium ions from heavier ion species with the same charge-to-mass ratio (such as C{sup 6+}, O{sup 8+}, etc.). Commonly used Fuji Image plates were used as detectors in the spectrometer, whose absolute response to deuterium ions over a wide range of energies was calibrated by using slotted CR-39 nuclear track detectors. A typical deuterium ion spectrum diagnosed in a recent experimental campaign is presented, which was produced from a thin deuterated plastic foil target irradiated by a high power laser.

Multi-objective reliability redundancy allocation in an interval environment using particle swarm optimization

International Nuclear Information System (INIS)

Zhang, Enze; Chen, Qingwei

2016-01-01

Most of the existing works addressing reliability redundancy allocation problems are based on the assumption of fixed reliabilities of components. In real-life situations, however, the reliabilities of individual components may be imprecise, most often given as intervals, under different operating or environmental conditions. This paper deals with reliability redundancy allocation problems modeled in an interval environment. An interval multi-objective optimization problem is formulated from the original crisp one, where system reliability and cost are simultaneously considered. To render the multi-objective particle swarm optimization (MOPSO) algorithm capable of dealing with interval multi-objective optimization problems, a dominance relation for interval-valued functions is defined with the help of our newly proposed order relations of interval-valued numbers. Then, the crowding distance is extended to the multi-objective interval-valued case. Finally, the effectiveness of the proposed approach has been demonstrated through two numerical examples and a case study of supervisory control and data acquisition (SCADA) system in water resource management. - Highlights: • We model the reliability redundancy allocation problem in an interval environment. • We apply the particle swarm optimization directly on the interval values. • A dominance relation for interval-valued multi-objective functions is defined. • The crowding distance metric is extended to handle imprecise objective functions.

Object recognition through a multi-mode fiber

Science.gov (United States)

Takagi, Ryosuke; Horisaki, Ryoichi; Tanida, Jun

2017-04-01

We present a method of recognizing an object through a multi-mode fiber. A number of speckle patterns transmitted through a multi-mode fiber are provided to a classifier based on machine learning. We experimentally demonstrated binary classification of face and non-face targets based on the method. The measurement process of the experimental setup was random and nonlinear because a multi-mode fiber is a typical strongly scattering medium and any reference light was not used in our setup. Comparisons between three supervised learning methods, support vector machine, adaptive boosting, and neural network, are also provided. All of those learning methods achieved high accuracy rates at about 90% for the classification. The approach presented here can realize a compact and smart optical sensor. It is practically useful for medical applications, such as endoscopy. Also our study indicated a promising utilization of artificial intelligence, which has rapidly progressed, for reducing optical and computational costs in optical sensing systems.

Complex engineering objects construction using Multi-D innovative technology

International Nuclear Information System (INIS)

Agafonov, Alexey

2013-01-01

Multi-D technology is an integrated innovative project management system for a construction of complex engineering objects based on a construction process simulation using an intellectual 3D model. Multi-D technology includes: • The unified schedule of E+P+C; • The schedule of loading of human resources, machines & mechanisms; • The budget of expenses and the income integrated with the schedule; • 3D model; • Multi-D model; • Weekly-daily tasks (with 4th level schedules); • Control system of interaction of Customer-EPC(m) company - Contractors; • Change and configuration management system

Strength Pareto particle swarm optimization and hybrid EA-PSO for multi-objective optimization.

Science.gov (United States)

Elhossini, Ahmed; Areibi, Shawki; Dony, Robert

2010-01-01

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

Multi-Objective Optimization of Managed Aquifer Recharge.

Science.gov (United States)

Fatkhutdinov, Aybulat; Stefan, Catalin

2018-04-27

This study demonstrates the utilization of a multi-objective hybrid global/local optimization algorithm for solving managed aquifer recharge (MAR) design problems, in which the decision variables included spatial arrangement of water injection and abstraction wells and time-variant rates of pumping and injection. The objective of the optimization was to maximize the efficiency of the MAR scheme, which includes both quantitative and qualitative aspects. The case study used to demonstrate the capabilities of the proposed approach is based on a published report on designing a real MAR site with defined aquifer properties, chemical groundwater characteristics as well as quality and volumes of injected water. The demonstration problems include steady-state and transient scenarios. The steady-state scenario demonstrates optimization of spatial arrangement of multiple injection and recovery wells, whereas the transient scenario was developed with the purpose of finding optimal regimes of water injection and recovery at a single location. Both problems were defined as multi-objective problems. The scenarios were simulated by applying coupled numerical groundwater flow and solute transport models: MODFLOW-2005 and MT3D-USGS. The applied optimization method was a combination of global - the Non-Dominated Sorting Genetic Algorithm (NSGA-2), and local - the Nelder-Mead Downhill Simplex search algorithms. The analysis of the resulting Pareto optimal solutions led to the discovery of valuable patterns and dependencies between the decision variables, model properties and problem objectives. Additionally, the performance of the traditional global and the hybrid optimization schemes were compared. This article is protected by copyright. All rights reserved.

Intuitionistic Fuzzy Goal Programming Technique for Solving Non-Linear Multi-objective Structural Problem

Directory of Open Access Journals (Sweden)

Samir Dey

2015-07-01

Full Text Available This paper proposes a new multi-objective intuitionistic fuzzy goal programming approach to solve a multi-objective nonlinear programming problem in context of a structural design. Here we describe some basic properties of intuitionistic fuzzy optimization. We have considered a multi-objective structural optimization problem with several mutually conflicting objectives. The design objective is to minimize weight of the structure and minimize the vertical deflection at loading point of a statistically loaded three-bar planar truss subjected to stress constraints on each of the truss members. This approach is used to solve the above structural optimization model based on arithmetic mean and compare with the solution by intuitionistic fuzzy goal programming approach. A numerical solution is given to illustrate our approach.

An experimental analysis of design choices of multi-objective ant colony optimization algorithms

OpenAIRE

Lopez-Ibanez, Manuel; Stutzle, Thomas

2012-01-01

There have been several proposals on how to apply the ant colony optimization (ACO) metaheuristic to multi-objective combinatorial optimization problems (MOCOPs). This paper proposes a new formulation of these multi-objective ant colony optimization (MOACO) algorithms. This formulation is based on adding specific algorithm components for tackling multiple objectives to the basic ACO metaheuristic. Examples of these components are how to represent multiple objectives using pheromone and heuris...

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Directory of Open Access Journals (Sweden)

Lvjiang Yin

2016-12-01

Full Text Available Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T, cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II, Strength Pareto Evolutionary Algorithm 2 (SPEA2, Multiobjective Particle Swarm Optimization (OMOPSO, and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D. Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

Quasielastic high-resolution time-of-flight spectrometers employing multi-disk chopper cascades for spallation sources

International Nuclear Information System (INIS)

Lechner, R.E.

2001-01-01

The design of multi-disk chopper time-of-flight (MTOF) spectrometers for high-resolution quasielastic and low-energy inelastic neutron scattering at spallation sources is discussed in some detail. A continuously variable energy resolution (1 μeV to 10 meV), and a large dynamic range (1 μeV to 100 meV), are outstanding features of this type of instrument, which are easily achieved also at a pulsed source using state-of-the-art technology. The method of intensity-resolution optimization of MTOF spectrometers at spallation sources is treated on the basis of the requirement of using (almost) 'all the neutrons of the pulse', taking into account the constant, but wavelength-dependent duration of the source pulse. It follows, that the optimization procedure (which is slightly different from that employed in the steady-state source case) should give priority to the highest resolution, whenever such a choice becomes necessary. This leads to long monochromator distances (L l2 ) of the order of 50 m, for achieving resolutions now available at reactor sources. A few examples of spectrometer layout and corresponding design parameters for large-angle and for small-angle quasielastic scattering instruments are given. In the latter case higher energy resolution than for large-angle scattering is required and achieved. The use of phase-space transformers, neutron wavelength band-pass filters and multichromatic operation for the purpose of intensity-resolution optimization are discussed. This spectrometer can be designed to make full use of the pulsed source peak flux. Therefore, and because of a number of improvements, high resolution will be available at high intensity: for any given resolution the total intensity at the detectors, when placed at one of the planned new spallation sources (SNS, JSNS, ESS, AUSTRON) will be larger by at least three orders of magnitude than the total intensity of any of the presently existing instruments of this type in routine operation at steady

8th International Conference on Multi-Objective and Goal Programming

CERN Document Server

Tamiz, Mehrdad; Ries, Jana

2010-01-01

This volume shows the state-of-the-art in both theoretical development and application of multiple objective and goal programming. Applications from the fields of supply chain management, financial portfolio selection, financial risk management, insurance, medical imaging, sustainability, nurse scheduling, project management, water resource management, and the interface with data envelopment analysis give a good reflection of current usage. A pleasing variety of techniques are used including models with fuzzy, group-decision, stochastic, interactive, and binary aspects. Additionally, two papers from the upcoming area of multi-objective evolutionary algorithms are included. The book is based on the papers of the 8th International Conference on Multi-Objective and Goal Programming (MOPGP08) which was held in Portsmouth, UK, in September 2008.

Multi-objective optimization of a plate and frame heat exchanger via genetic algorithm

Energy Technology Data Exchange (ETDEWEB)

Najafi, Hamidreza; Najafi, Behzad [K. N. Toosi University of Technology, Department of Mechanical Engineering, Tehran (Iran)

2010-06-15

In the present paper, a plate and frame heat exchanger is considered. Multi-objective optimization using genetic algorithm is developed in order to obtain a set of geometric design parameters, which lead to minimum pressure drop and the maximum overall heat transfer coefficient. Vividly, considered objective functions are conflicting and no single solution can satisfy both objectives simultaneously. Multi-objective optimization procedure yields a set of optimal solutions, called Pareto front, each of which is a trade-off between objectives and can be selected by the user, regarding the application and the project's limits. The presented work takes care of numerous geometric parameters in the presence of logical constraints. A sensitivity analysis is also carried out to study the effects of different geometric parameters on the considered objective functions. Modeling the system and implementing the multi-objective optimization via genetic algorithm has been performed by MATLAB. (orig.)

MONSS: A multi-objective nonlinear simplex search approach

Science.gov (United States)

Zapotecas-Martínez, Saúl; Coello Coello, Carlos A.

2016-01-01

This article presents a novel methodology for dealing with continuous box-constrained multi-objective optimization problems (MOPs). The proposed algorithm adopts a nonlinear simplex search scheme in order to obtain multiple elements of the Pareto optimal set. The search is directed by a well-distributed set of weight vectors, each of which defines a scalarization problem that is solved by deforming a simplex according to the movements described by Nelder and Mead's method. Considering an MOP with n decision variables, the simplex is constructed using n+1 solutions which minimize different scalarization problems defined by n+1 neighbor weight vectors. All solutions found in the search are used to update a set of solutions considered to be the minima for each separate problem. In this way, the proposed algorithm collectively obtains multiple trade-offs among the different conflicting objectives, while maintaining a proper representation of the Pareto optimal front. In this article, it is shown that a well-designed strategy using just mathematical programming techniques can be competitive with respect to the state-of-the-art multi-objective evolutionary algorithms against which it was compared.

Improved multi-objective clustering algorithm using particle swarm optimization.

Science.gov (United States)

Gong, Congcong; Chen, Haisong; He, Weixiong; Zhang, Zhanliang

2017-01-01

Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO) is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

Compressed multi-block local binary pattern for object tracking

Science.gov (United States)

Li, Tianwen; Gao, Yun; Zhao, Lei; Zhou, Hao

2018-04-01

Both robustness and real-time are very important for the application of object tracking under a real environment. The focused trackers based on deep learning are difficult to satisfy with the real-time of tracking. Compressive sensing provided a technical support for real-time tracking. In this paper, an object can be tracked via a multi-block local binary pattern feature. The feature vector was extracted based on the multi-block local binary pattern feature, which was compressed via a sparse random Gaussian matrix as the measurement matrix. The experiments showed that the proposed tracker ran in real-time and outperformed the existed compressive trackers based on Haar-like feature on many challenging video sequences in terms of accuracy and robustness.

Multi-objective Design Method for Hybrid Active Power Filter

Science.gov (United States)

Yu, Jingrong; Deng, Limin; Liu, Maoyun; Qiu, Zhifeng

2017-10-01

In this paper, a multi-objective optimal design for transformerless hybrid active power filter (HAPF) is proposed. The interactions between the active and passive circuits is analyzed, and by taking the interactions into consideration, a three-dimensional objective problem comprising of performance, efficiency and cost of HAPF system is formulated. To deal with the multiple constraints and the strong coupling characteristics of the optimization model, a novel constraint processing mechanism based on distance measurement and adaptive penalty function is presented. In order to improve the diversity of optimal solution and the local searching ability of the particle swarm optimization (PSO) algorithm, a chaotic mutation operator based on multistage neighborhood is proposed. The simulation results show that the optimums near the ordinate origin of the three-dimension space make better tradeoff among the performance, efficiency and cost of HAPF, and the experimental results of transformerless HAPF verify the effectiveness of the method for multi-objective optimization and design.

Multi-objective optimization of Stirling engine systems using Front-based Yin-Yang-Pair Optimization

International Nuclear Information System (INIS)

Punnathanam, Varun; Kotecha, Prakash

2017-01-01

Highlights: • Efficient multi-objective optimization algorithm F-YYPO demonstrated. • Three Stirling engine applications with a total of eight cases. • Improvements in the objective function values of up to 30%. • Superior to the popularly used gamultiobj of MATLAB. • F-YYPO has extremely low time complexity. - Abstract: In this work, we demonstrate the performance of Front-based Yin-Yang-Pair Optimization (F-YYPO) to solve multi-objective problems related to Stirling engine systems. The performance of F-YYPO is compared with that of (i) a recently proposed multi-objective optimization algorithm (Multi-Objective Grey Wolf Optimizer) and (ii) an algorithm popularly employed in literature due to its easy accessibility (MATLAB’s inbuilt multi-objective Genetic Algorithm function: gamultiobj). We consider three Stirling engine based optimization problems: (i) the solar-dish Stirling engine system which considers objectives of output power, thermal efficiency and rate of entropy generation; (ii) Stirling engine thermal model which considers the associated irreversibility of the cycle with objectives of output power, thermal efficiency and pressure drop; and finally (iii) an experimentally validated polytropic finite speed thermodynamics based Stirling engine model also with objectives of output power and pressure drop. We observe F-YYPO to be significantly more effective as compared to its competitors in solving the problems, while requiring only a fraction of the computational time required by the other algorithms.

A Review of Some Superconducting Technologies for AtLAST: Parametric Amplifiers, Kinetic Inductance Detectors, and On-Chip Spectrometers

Science.gov (United States)

Noroozian, Omid

2018-01-01

The current state of the art for some superconducting technologies will be reviewed in the context of a future single-dish submillimeter telescope called AtLAST. The technologies reviews include: 1) Kinetic Inductance Detectors (KIDs), which have now been demonstrated in large-format kilo-pixel arrays with photon background-limited sensitivity suitable for large field of view cameras for wide-field imaging. 2) Parametric amplifiers - specifically the Traveling-Wave Kinetic Inductance (TKIP) amplifier - which has enormous potential to increase sensitivity, bandwidth, and mapping speed of heterodyne receivers, and 3) On-chip spectrometers, which combined with sensitive direct detectors such as KIDs or TESs could be used as Multi-Object Spectrometers on the AtLAST focal plane, and could provide low-medium resolution spectroscopy of 100 objects at a time in each field of view.

Study on hybrid multi-objective optimization algorithm for inverse treatment planning of radiation therapy

International Nuclear Information System (INIS)

Li Guoli; Song Gang; Wu Yican

2007-01-01

Inverse treatment planning for radiation therapy is a multi-objective optimization process. The hybrid multi-objective optimization algorithm is studied by combining the simulated annealing(SA) and genetic algorithm(GA). Test functions are used to analyze the efficiency of algorithms. The hybrid multi-objective optimization SA algorithm, which displacement is based on the evolutionary strategy of GA: crossover and mutation, is implemented in inverse planning of external beam radiation therapy by using two kinds of objective functions, namely the average dose distribution based and the hybrid dose-volume constraints based objective functions. The test calculations demonstrate that excellent converge speed can be achieved. (authors)

Evolving intelligent vehicle control using multi-objective NEAT

NARCIS (Netherlands)

Willigen, W.H. van; Haasdijk, E.; Kester, L.J.H.M.

2013-01-01

The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective algorithm based on NEAT and SPEA2 that evolves controllers for such

Pareto-optimal multi-objective dimensionality reduction deep auto-encoder for mammography classification.

Science.gov (United States)

Taghanaki, Saeid Asgari; Kawahara, Jeremy; Miles, Brandon; Hamarneh, Ghassan

2017-07-01

Feature reduction is an essential stage in computer aided breast cancer diagnosis systems. Multilayer neural networks can be trained to extract relevant features by encoding high-dimensional data into low-dimensional codes. Optimizing traditional auto-encoders works well only if the initial weights are close to a proper solution. They are also trained to only reduce the mean squared reconstruction error (MRE) between the encoder inputs and the decoder outputs, but do not address the classification error. The goal of the current work is to test the hypothesis that extending traditional auto-encoders (which only minimize reconstruction error) to multi-objective optimization for finding Pareto-optimal solutions provides more discriminative features that will improve classification performance when compared to single-objective and other multi-objective approaches (i.e. scalarized and sequential). In this paper, we introduce a novel multi-objective optimization of deep auto-encoder networks, in which the auto-encoder optimizes two objectives: MRE and mean classification error (MCE) for Pareto-optimal solutions, rather than just MRE. These two objectives are optimized simultaneously by a non-dominated sorting genetic algorithm. We tested our method on 949 X-ray mammograms categorized into 12 classes. The results show that the features identified by the proposed algorithm allow a classification accuracy of up to 98.45%, demonstrating favourable accuracy over the results of state-of-the-art methods reported in the literature. We conclude that adding the classification objective to the traditional auto-encoder objective and optimizing for finding Pareto-optimal solutions, using evolutionary multi-objective optimization, results in producing more discriminative features. Copyright © 2017 Elsevier B.V. All rights reserved.

A scalable coevolutionary multi-objective particle swarm optimizer

Directory of Open Access Journals (Sweden)

Xiangwei Zheng

2010-11-01

Full Text Available Multi-Objective Particle Swarm Optimizers (MOPSOs are easily trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A scalable cooperative coevolution and ?-dominance based MOPSO (CEPSO is proposed to address these issues. In CEPSO, Multi-objective Optimization Problems (MOPs are decomposed in terms of their decision variables and are optimized by cooperative coevolutionary subswarms, and a uniform distribution mutation operator is adopted to avoid premature convergence. All subswarms share an external archive based on ?-dominance, which is also used as a leader set. Collaborators are selected from the archive and used to construct context vectors in order to evaluate particles in a subswarm. CEPSO is tested on several classical MOP benchmark functions and experimental results show that CEPSO can readily escape from local optima and optimize both low and high dimensional problems, but the number of function evaluations only increases linearly with respect to the number of decision variables. Therefore, CEPSO is competitive in solving various MOPs.

Multi-objective optimization of a continuous bio-dissimilation process of glycerol to 1, 3-propanediol.

Science.gov (United States)

Xu, Gongxian; Liu, Ying; Gao, Qunwang

2016-02-10

This paper deals with multi-objective optimization of continuous bio-dissimilation process of glycerol to 1, 3-propanediol. In order to maximize the production rate of 1, 3-propanediol, maximize the conversion rate of glycerol to 1, 3-propanediol, maximize the conversion rate of glycerol, and minimize the concentration of by-product ethanol, we first propose six new multi-objective optimization models that can simultaneously optimize any two of the four objectives above. Then these multi-objective optimization problems are solved by using the weighted-sum and normal-boundary intersection methods respectively. Both the Pareto filter algorithm and removal criteria are used to remove those non-Pareto optimal points obtained by the normal-boundary intersection method. The results show that the normal-boundary intersection method can successfully obtain the approximate Pareto optimal sets of all the proposed multi-objective optimization problems, while the weighted-sum approach cannot achieve the overall Pareto optimal solutions of some multi-objective problems. Copyright © 2015 Elsevier B.V. All rights reserved.

Combining simulation and multi-objective optimisation for equipment quantity optimisation in container terminals

OpenAIRE

Lin, Zhougeng

2013-01-01

This thesis proposes a combination framework to integrate simulation and multi-objective optimisation (MOO) for container terminal equipment optimisation. It addresses how the strengths of simulation and multi-objective optimisation can be integrated to find high quality solutions for multiple objectives with low computational cost. Three structures for the combination framework are proposed respectively: pre-MOO structure, integrated MOO structure and post-MOO structure. The applications of ...

A multi-objective programming model for assessment the GHG emissions in MSW management

Energy Technology Data Exchange (ETDEWEB)

Mavrotas, George, E-mail: mavrotas@chemeng.ntua.gr [National Technical University of Athens, Iroon Polytechniou 9, Zografou, Athens, 15780 (Greece); Skoulaxinou, Sotiria [EPEM SA, 141 B Acharnon Str., Athens, 10446 (Greece); Gakis, Nikos [FACETS SA, Agiou Isidorou Str., Athens, 11471 (Greece); Katsouros, Vassilis [Athena Research and Innovation Center, Artemidos 6 and Epidavrou Str., Maroussi, 15125 (Greece); Georgopoulou, Elena [National Observatory of Athens, Thisio, Athens, 11810 (Greece)

2013-09-15

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH{sub 4} generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the

A multi-objective programming model for assessment the GHG emissions in MSW management

International Nuclear Information System (INIS)

Mavrotas, George; Skoulaxinou, Sotiria; Gakis, Nikos; Katsouros, Vassilis; Georgopoulou, Elena

2013-01-01

Highlights: • The multi-objective multi-period optimization model. • The solution approach for the generation of the Pareto front with mathematical programming. • The very detailed description of the model (decision variables, parameters, equations). • The use of IPCC 2006 guidelines for landfill emissions (first order decay model) in the mathematical programming formulation. - Abstract: In this study a multi-objective mathematical programming model is developed for taking into account GHG emissions for Municipal Solid Waste (MSW) management. Mathematical programming models are often used for structure, design and operational optimization of various systems (energy, supply chain, processes, etc.). The last twenty years they are used all the more often in Municipal Solid Waste (MSW) management in order to provide optimal solutions with the cost objective being the usual driver of the optimization. In our work we consider the GHG emissions as an additional criterion, aiming at a multi-objective approach. The Pareto front (Cost vs. GHG emissions) of the system is generated using an appropriate multi-objective method. This information is essential to the decision maker because he can explore the trade-offs in the Pareto curve and select his most preferred among the Pareto optimal solutions. In the present work a detailed multi-objective, multi-period mathematical programming model is developed in order to describe the waste management problem. Apart from the bi-objective approach, the major innovations of the model are (1) the detailed modeling considering 34 materials and 42 technologies, (2) the detailed calculation of the energy content of the various streams based on the detailed material balances, and (3) the incorporation of the IPCC guidelines for the CH 4 generated in the landfills (first order decay model). The equations of the model are described in full detail. Finally, the whole approach is illustrated with a case study referring to the application

Multi-objective optimization of a continuous thermally regenerative electrochemical cycle for waste heat recovery

International Nuclear Information System (INIS)

Long, Rui; Li, Baode; Liu, Zhichun; Liu, Wei

2015-01-01

An optimization analysis of a continuous TREC (thermally regenerative electrochemical cycle) was conducted with maximum power output and exergy efficiency as the objective functions simultaneously. For comparison, the power output, exergy efficiency, and thermal efficiency under the corresponding single-objective optimization schematics were also calculated. Under different optimization methods it was observed that the power output and the thermal efficiency increase with increasing inlet temperature of the heat source, whereas the exergy efficiency increases with increasing inlet temperature, reaches a maximum value, and then decreases. Results revealed that the optimal power output under the multi-objective optimization turned out to be slightly less than that obtained under the single-objective optimization for power output. However, the exergy and thermal efficiencies were much greater. Furthermore, the thermal exergy and exergy efficiency by single-objective optimization for energy efficiency shows no dominant advantage than that obtained under multi-objective optimization, comparing with the increase amplitude of the power output. This suggests that the multi-objective optimization could coordinate well both the power output and the exergy efficiency of the TREC system, and may serve as a more promising guide for operating and designing TREC systems. - Highlights: • An optimal analysis of a continuous TREC is conducted based on multi-objective optimization. • Performance under corresponding single-objective optimizations has also been calculated and compared. • Power under multi-objective optimization is slightly less than the maximum power. • Exergy and thermal efficiencies are much larger than that under the single-objective optimization.

Evaluation of cephalogram using multi-objective frequency processing

Energy Technology Data Exchange (ETDEWEB)

Hagiwara, Sakae; Takizawa, Tsutomu; Osako, Miho; Kaneda, Takashi; Kasai, Kazutaka [Nihon Univ., Chiba (Japan). School of Dentistry at Matsudo

2002-12-01

A diagnosis with cephalogram is important for orthodontic treatment. Recently, computed radiography (CR) has been performed to the cephalogram. However, evaluation of multi-objective frequency processing (MFP) for cephalograms has been received little attention. The purpose of this study was to evaluate the cephalogram using MFP CR. At first, 450 lateral cephalograms were made, from 50 orthodontic patients, with 9 possible spatial frequency parameter combinations and a contrast scale held fixed in images processing. For each film, the clarity of radiographic images were estimated and scored with respect to landmark identification (total 26 points, 20 points of hard tissue and 6 points of soft tissue). A specific combination of spatial frequency scales (multi-frequency balance types (MRB) F-type, multi-frequency enhancement (MRE) 8) was proved to be adequate to achieve the optimal image quality in the cephalogram. (author)

Evaluation of cephalogram using multi-objective frequency processing

International Nuclear Information System (INIS)

Hagiwara, Sakae; Takizawa, Tsutomu; Osako, Miho; Kaneda, Takashi; Kasai, Kazutaka

2002-01-01

A diagnosis with cephalogram is important for orthodontic treatment. Recently, computed radiography (CR) has been performed to the cephalogram. However, evaluation of multi-objective frequency processing (MFP) for cephalograms has been received little attention. The purpose of this study was to evaluate the cephalogram using MFP CR. At first, 450 lateral cephalograms were made, from 50 orthodontic patients, with 9 possible spatial frequency parameter combinations and a contrast scale held fixed in images processing. For each film, the clarity of radiographic images were estimated and scored with respect to landmark identification (total 26 points, 20 points of hard tissue and 6 points of soft tissue). A specific combination of spatial frequency scales (multi-frequency balance types (MRB) F-type, multi-frequency enhancement (MRE) 8) was proved to be adequate to achieve the optimal image quality in the cephalogram. (author)

Multi-objective optimization in quantum parameter estimation

Science.gov (United States)

Gong, BeiLi; Cui, Wei

2018-04-01

We investigate quantum parameter estimation based on linear and Kerr-type nonlinear controls in an open quantum system, and consider the dissipation rate as an unknown parameter. We show that while the precision of parameter estimation is improved, it usually introduces a significant deformation to the system state. Moreover, we propose a multi-objective model to optimize the two conflicting objectives: (1) maximizing the Fisher information, improving the parameter estimation precision, and (2) minimizing the deformation of the system state, which maintains its fidelity. Finally, simulations of a simplified ɛ-constrained model demonstrate the feasibility of the Hamiltonian control in improving the precision of the quantum parameter estimation.

Hydro-environmental management of groundwater resources: A fuzzy-based multi-objective compromise approach

Science.gov (United States)

Alizadeh, Mohammad Reza; Nikoo, Mohammad Reza; Rakhshandehroo, Gholam Reza

2017-08-01

Sustainable management of water resources necessitates close attention to social, economic and environmental aspects such as water quality and quantity concerns and potential conflicts. This study presents a new fuzzy-based multi-objective compromise methodology to determine the socio-optimal and sustainable policies for hydro-environmental management of groundwater resources, which simultaneously considers the conflicts and negotiation of involved stakeholders, uncertainties in decision makers' preferences, existing uncertainties in the groundwater parameters and groundwater quality and quantity issues. The fuzzy multi-objective simulation-optimization model is developed based on qualitative and quantitative groundwater simulation model (MODFLOW and MT3D), multi-objective optimization model (NSGA-II), Monte Carlo analysis and Fuzzy Transformation Method (FTM). Best compromise solutions (best management policies) on trade-off curves are determined using four different Fuzzy Social Choice (FSC) methods. Finally, a unanimity fallback bargaining method is utilized to suggest the most preferred FSC method. Kavar-Maharloo aquifer system in Fars, Iran, as a typical multi-stakeholder multi-objective real-world problem is considered to verify the proposed methodology. Results showed an effective performance of the framework for determining the most sustainable allocation policy in groundwater resource management.

Multi area and multistage expansion-planning of electricity supply with sustainable energy development criteria: a multi objective model

Energy Technology Data Exchange (ETDEWEB)

Unsihuay-Vila, Clodomiro; Marangon-Lima, J.W.; Souza, A.C Zambroni de [Universidade Federal de Itajuba (UNIFEI), MG (Brazil)], emails: clodomirounsihuayvila @gmail.com, marangon@unifei.edu.br, zambroni@unifei.edu.br; Perez-Arriaga, I.J. [Universidad Pontificia Comillas, Madrid (Spain)], email: ipa@mit.edu

2010-07-01

A novel multi objective, multi area and multistage model to long-term expansion-planning of integrated generation and transmission corridors incorporating sustainable energy developing is presented in this paper. The proposed MESEDES model is a multi-regional multi-objective and 'bottom-up' energy model which considers the electricity generation/transmission value-chain, i.e., power generation alternatives including renewable, nuclear and traditional thermal generation along with transmission corridors. The model decides the optimal location and timing of the electricity generation/transmission abroad the multistage planning horizon. The MESEDES model considers three objectives belonging to sustainable energy development criteria such as: a) the minimization of investments and operation costs of : power generation, transmission corridors, energy efficiency (demand side management (DSM) programs) considering CO2 capture technologies; b) minimization of Life Cycle Greenhouse Gas Emissions (LC GHG); c) maximization of the diversification of electricity generation mix. The proposed model consider aspects of the carbon abatement policy under the CDM - Clean Development Mechanism or European Union Greenhouse Gas Emission Trading Scheme. A case study is used to illustrate the proposed framework. (author)

Improved multi-objective clustering algorithm using particle swarm optimization.

Directory of Open Access Journals (Sweden)

Congcong Gong

Full Text Available Multi-objective clustering has received widespread attention recently, as it can obtain more accurate and reasonable solution. In this paper, an improved multi-objective clustering framework using particle swarm optimization (IMCPSO is proposed. Firstly, a novel particle representation for clustering problem is designed to help PSO search clustering solutions in continuous space. Secondly, the distribution of Pareto set is analyzed. The analysis results are applied to the leader selection strategy, and make algorithm avoid trapping in local optimum. Moreover, a clustering solution-improved method is proposed, which can increase the efficiency in searching clustering solution greatly. In the experiments, 28 datasets are used and nine state-of-the-art clustering algorithms are compared, the proposed method is superior to other approaches in the evaluation index ARI.

Multi-objective design of PV-wind-diesel-hydrogen-battery systems

Energy Technology Data Exchange (ETDEWEB)

Dufo-Lopez, Rodolfo; Bernal-Agustin, Jose L. [Department of Electrical Engineering, University of Zaragoza, Calle Maria de Luna 3, 50018-Zaragoza (Spain)

2008-12-15

This paper presents, for the first time, a triple multi-objective design of isolated hybrid systems minimizing, simultaneously, the total cost throughout the useful life of the installation, pollutant emissions (CO{sub 2}) and unmet load. For this task, a multi-objective evolutionary algorithm (MOEA) and a genetic algorithm (GA) have been used in order to find the best combination of components of the hybrid system and control strategies. As an example of application, a complex PV-wind-diesel-hydrogen-battery system has been designed, obtaining a set of possible solutions (Pareto Set). The results achieved demonstrate the practical utility of the developed design method. (author)

Multi-Model Estimation Based Moving Object Detection for Aerial Video

Directory of Open Access Journals (Sweden)

Yanning Zhang

2015-04-01

Full Text Available With the wide development of UAV (Unmanned Aerial Vehicle technology, moving target detection for aerial video has become a popular research topic in the computer field. Most of the existing methods are under the registration-detection framework and can only deal with simple background scenes. They tend to go wrong in the complex multi background scenarios, such as viaducts, buildings and trees. In this paper, we break through the single background constraint and perceive the complex scene accurately by automatic estimation of multiple background models. First, we segment the scene into several color blocks and estimate the dense optical flow. Then, we calculate an affine transformation model for each block with large area and merge the consistent models. Finally, we calculate subordinate degree to multi-background models pixel to pixel for all small area blocks. Moving objects are segmented by means of energy optimization method solved via Graph Cuts. The extensive experimental results on public aerial videos show that, due to multi background models estimation, analyzing each pixel’s subordinate relationship to multi models by energy minimization, our method can effectively remove buildings, trees and other false alarms and detect moving objects correctly.

Multi-objective genetic optimization of linear construction projects

Directory of Open Access Journals (Sweden)

Fatma A. Agrama

2012-08-01

Full Text Available In the real world, the majority cases of optimization problems, met by engineers, are composed of several conflicting objectives. This paper presents an approach for a multi-objective optimization model for scheduling linear construction projects. Linear construction projects have many identical units wherein activities repeat from one unit to another. Highway, pipeline, and tunnels are good examples that exhibit repetitive characteristics. These projects represent a large portion of the construction industry. The present model enables construction planners to generate optimal/near-optimal construction plans that minimize project duration, total work interruptions, and total number of crews. Each of these plans identifies, from a set of feasible alternatives, optimal crew synchronization for each activity and activity interruptions at each unit. This model satisfies the following aspects: (1 it is based on the line of balance technique; (2 it considers non-serial typical activities networks with finish–start relationship and both lag or overlap time between activities is allowed; (3 it utilizes a multi-objective genetic algorithms approach; (4 it is developed as a spreadsheet template that is easy to use. Details of the model with visual charts are presented. An application example is analyzed to illustrate the use of the model and demonstrate its capabilities in optimizing the scheduling of linear construction projects.

A multi-objective approach to solid waste management

International Nuclear Information System (INIS)

Galante, Giacomo; Aiello, Giuseppe; Enea, Mario; Panascia, Enrico

2010-01-01

The issue addressed in this paper consists in the localization and dimensioning of transfer stations, which constitute a necessary intermediate level in the logistic chain of the solid waste stream, from municipalities to the incinerator. Contextually, the determination of the number and type of vehicles involved is carried out in an integrated optimization approach. The model considers both initial investment and operative costs related to transportation and transfer stations. Two conflicting objectives are evaluated, the minimization of total cost and the minimization of environmental impact, measured by pollution. The design of the integrated waste management system is hence approached in a multi-objective optimization framework. To determine the best means of compromise, goal programming, weighted sum and fuzzy multi-objective techniques have been employed. The proposed analysis highlights how different attitudes of the decision maker towards the logic and structure of the problem result in the employment of different methodologies and the obtaining of different results. The novel aspect of the paper lies in the proposal of an effective decision support system for operative waste management, rather than a further contribution to the transportation problem. The model was applied to the waste management of optimal territorial ambit (OTA) of Palermo (Italy).

A multi-objective approach to solid waste management.

Science.gov (United States)

Galante, Giacomo; Aiello, Giuseppe; Enea, Mario; Panascia, Enrico

2010-01-01

The issue addressed in this paper consists in the localization and dimensioning of transfer stations, which constitute a necessary intermediate level in the logistic chain of the solid waste stream, from municipalities to the incinerator. Contextually, the determination of the number and type of vehicles involved is carried out in an integrated optimization approach. The model considers both initial investment and operative costs related to transportation and transfer stations. Two conflicting objectives are evaluated, the minimization of total cost and the minimization of environmental impact, measured by pollution. The design of the integrated waste management system is hence approached in a multi-objective optimization framework. To determine the best means of compromise, goal programming, weighted sum and fuzzy multi-objective techniques have been employed. The proposed analysis highlights how different attitudes of the decision maker towards the logic and structure of the problem result in the employment of different methodologies and the obtaining of different results. The novel aspect of the paper lies in the proposal of an effective decision support system for operative waste management, rather than a further contribution to the transportation problem. The model was applied to the waste management of optimal territorial ambit (OTA) of Palermo (Italy). 2010 Elsevier Ltd. All rights reserved.

Efficient exact optimization of multi-objective redundancy allocation problems in series-parallel systems

International Nuclear Information System (INIS)

Cao, Dingzhou; Murat, Alper; Chinnam, Ratna Babu

2013-01-01

This paper proposes a decomposition-based approach to exactly solve the multi-objective Redundancy Allocation Problem for series-parallel systems. Redundancy allocation problem is a form of reliability optimization and has been the subject of many prior studies. The majority of these earlier studies treat redundancy allocation problem as a single objective problem maximizing the system reliability or minimizing the cost given certain constraints. The few studies that treated redundancy allocation problem as a multi-objective optimization problem relied on meta-heuristic solution approaches. However, meta-heuristic approaches have significant limitations: they do not guarantee that Pareto points are optimal and, more importantly, they may not identify all the Pareto-optimal points. In this paper, we treat redundancy allocation problem as a multi-objective problem, as is typical in practice. We decompose the original problem into several multi-objective sub-problems, efficiently and exactly solve sub-problems, and then systematically combine the solutions. The decomposition-based approach can efficiently generate all the Pareto-optimal solutions for redundancy allocation problems. Experimental results demonstrate the effectiveness and efficiency of the proposed method over meta-heuristic methods on a numerical example taken from the literature.

Multi-Object Spectroscopy in the Next Decade: A Conference Summary

NARCIS (Netherlands)

Trager, S. C.; Skillen, I.; Barcells, M.

2016-01-01

I present a highly-biased summary of the conference "Multi-Object Spectroscopy in the Next Decade: Big Questions, Large Surveys, and Wide Fields," held 2-6 March 2015 in Santa Cruz de la Palma, Spain. I focus on four issues in this summary: (1) complexity in objects, physics, and instruments is

Issues with performance measures for dynamic multi-objective optimisation

CSIR Research Space (South Africa)

Helbig, M

2013-06-01

Full Text Available Symposium on Computational Intelligence in Dynamic and Uncertain Environments (CIDUE), Mexico, 20-23 June 2013 Issues with Performance Measures for Dynamic Multi-objective Optimisation Mard´e Helbig CSIR: Meraka Institute Brummeria, South Africa...

A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders

OpenAIRE

Nguyen, Phong; Dines, John; Krasnodebski, Jan

2017-01-01

Multi-objective recommender systems address the difficult task of recommending items that are relevant to multiple, possibly conflicting, criteria. However these systems are most often designed to address the objective of one single stakeholder, typically, in online commerce, the consumers whose input and purchasing decisions ultimately determine the success of the recommendation systems. In this work, we address the multi-objective, multi-stakeholder, recommendation problem involving one or ...

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions.

Science.gov (United States)

Qu, Shaojian; Ji, Ying

2016-01-01

In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our "worst-case weighted multi-objective game" model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call "robust-weighted Nash equilibrium". We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC). For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.

Multi-objective demand side scheduling considering the operational safety of appliances

International Nuclear Information System (INIS)

Du, Y.F.; Jiang, L.; Li, Y.Z.; Counsell, J.; Smith, J.S.

2016-01-01

Highlights: • Operational safety of appliances is introduced in multi-objective scheduling. • Relationships between operational safety and other objectives are investigated. • Adopted Pareto approach is compared with Weigh and Constraint approaches. • Decision making of Pareto approach is proposed for final appliances’ scheduling. - Abstract: The safe operation of appliances is of great concern to users. The safety risk increases when the appliances are in operation during periods when users are not at home or when they are asleep. In this paper, multi-objective demand side scheduling is investigated with consideration to the appliances’ operational safety together with the electricity cost and the operational delay. The formulation of appliances’ operational safety is proposed based on users’ at-home status and awake status. Then the relationships between the operational safety and the other two objectives are investigated through the approach of finding the Pareto-optimal front. Moreover, this approach is compared with the Weigh and Constraint approaches. As the Pareto-optimal front consists of a set of optimal solutions, this paper proposes a method to make the final scheduling decision based on the relationships among the multiple objectives. Simulation results demonstrate that the operational safety is improved with the sacrifice of the electricity cost and the operational delay, and that the approach of finding the Pareto-optimal front is effective in presenting comprehensive optimal solutions of the multi-objective demand side scheduling.

Solving dynamic multi-objective problems with vector evaluated particle swarm optimisation

CSIR Research Space (South Africa)

Greeff, M

2008-06-01

Full Text Available Many optimisation problems are multi-objective and change dynamically. Many methods use a weighted average approach to the multiple objectives. This paper introduces the usage of the vector evaluated particle swarm optimiser (VEPSO) to solve dynamic...

Multi-objective mixture-based iterated density estimation evolutionary algorithms

NARCIS (Netherlands)

Thierens, D.; Bosman, P.A.N.

2001-01-01

We propose an algorithm for multi-objective optimization using a mixture-based iterated density estimation evolutionary algorithm (MIDEA). The MIDEA algorithm is a prob- abilistic model building evolutionary algo- rithm that constructs at each generation a mixture of factorized probability

Approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems

International Nuclear Information System (INIS)

Zhang, Xiaoshun; Yu, Tao; Yang, Bo; Zheng, Limin; Huang, Linni

2015-01-01

Highlights: • A novel optimal carbon-energy combined-flow (OCECF) model is firstly established. • A novel approximate ideal multi-objective solution Q(λ) learning is designed. • The proposed algorithm has a high convergence stability and reliability. • The proposed algorithm can be applied for OCECF in a large-scale power grid. - Abstract: This paper proposes a novel approximate ideal multi-objective solution Q(λ) learning for optimal carbon-energy combined-flow in multi-energy power systems. The carbon emissions, fuel cost, active power loss, voltage deviation and carbon emission loss are chosen as the optimization objectives, which are simultaneously optimized by five different Q-value matrices. The dynamic optimal weight of each objective is calculated online from the entire Q-value matrices such that the greedy action policy can be obtained. Case studies are carried out to evaluate the optimization performance for carbon-energy combined-flow in an IEEE 118-bus system and the regional power grid of southern China.

Sensitivity Synthesis for MIMO Systems: A Multi Objective H^2 Approach

DEFF Research Database (Denmark)

Stoustrup, Jakob; Niemann, Hans Henrik

1996-01-01

A series of multi objective QTR H-infinity designproblems are considered in this paper. The problems are formulatedas a number of coupled QTR H-infinity design problems. TheseQTR H-infinity problems can be formulated as sensitivityproblems, complementary sensitivity problems, or control...... sensitivityproblems for every output (or input) in the system. It turns out thatthese multi objective QTR H-infinity design problems, based ona number of different types of sensitivity problems, can be exactlydecoupled into k\\QTR H-infinity sensitivity problems for stablesystems, where k is the number of outputs (for...... unstable systems,independent stabilization is required). Further, it is shown how to usesimilar techniques to incorporate simultaneous specifications for differentcontrol objectives such as QTR H-infinity, etc., for the sensitivities....

Soft X-ray Calibration of the Co/C Multilayer Mirrors for the Objective Crystal Spectrometer on the Spectrum Röntgen-Gamma Satellite

DEFF Research Database (Denmark)

Abdali, Salim; Tarrio, C.; Christensen, Finn Erland

1996-01-01

The objective crystal spectrometer (OXS) on the forthcoming Spectrum-Roentgen-Gamma satellite is designed to carry three kinds of crystals: LiF(220), Si(111) and RAP(001), placed in front of the SODART telescope. Thirty six super polished (RMS roughness

Multilayer x-ray mirrors for the objective crystal spectrometer on the Spectrum Roentgen Gamma satellite

DEFF Research Database (Denmark)

Louis, E.; Spiller, E.; Abdali, S.

1995-01-01

with Kr+- and Ar+- ions of 300, 500, and 1000 eV. We examined the effect of different polishing parameters on the smoothening of the Co- and Ni-layers. The in-situ reflectivity of lambda equals 3.16 nm during deposition and the ex-situ grazing incidence reflectivity of Cu-K(alpha ) radiation (lambda...... multiplied by 6 cm2 Si (111) crystals for the Objective Crystal Spectrometer on the Russian Spectrum Rontgen Gamma satellite. The coatings on the flight crystals have a period Lambda of 3.95 plus or minus 0.02 nm and a reflectivity of more than 8% averaged over s- and p-polarization over the entire...

Multi-Objective Optimization of the Hedging Model for reservoir Operation Using Evolutionary Algorithms

Directory of Open Access Journals (Sweden)

sadegh sadeghitabas

2015-12-01

Full Text Available Multi-objective problems rarely ever provide a single optimal solution, rather they yield an optimal set of outputs (Pareto fronts. Solving these problems was previously accomplished by using some simplifier methods such as the weighting coefficient method used for converting a multi-objective problem to a single objective function. However, such robust tools as multi-objective meta-heuristic algorithms have been recently developed for solving these problems. The hedging model is one of the classic problems for reservoir operation that is generally employed for mitigating drought impacts in water resources management. According to this method, although it is possible to supply the total planned demands, only portions of the demands are met to save water by allowing small deficits in the current conditions in order to avoid or reduce severe deficits in future. The approach heavily depends on economic and social considerations. In the present study, the meta-heuristic algorithms of NSGA-II, MOPSO, SPEA-II, and AMALGAM are used toward the multi-objective optimization of the hedging model. For this purpose, the rationing factors involved in Taleghan dam operation are optimized over a 35-year statistical period of inflow. There are two objective functions: a minimizing the modified shortage index, and b maximizing the reliability index (i.e., two opposite objectives. The results show that the above algorithms are applicable to a wide range of optimal solutions. Among the algorithms, AMALGAM is found to produce a better Pareto front for the values of the objective function, indicating its more satisfactory performance.

Ensemble based multi-objective production optimization of smart wells

NARCIS (Netherlands)

Fonseca, R.M.; Leeuwenburgh, O.; Jansen, J.D.

2012-01-01

In a recent study two hierarchical multi-objective methods were suggested to include short-term targets in life-cycle production optimization. However this previous study has two limitations: 1) the adjoint formulation is used to obtain gradient information, requiring simulator source code access

An Extensible Component-Based Multi-Objective Evolutionary Algorithm Framework

DEFF Research Database (Denmark)

Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard

2017-01-01

The ability to easily modify the problem definition is currently missing in Multi-Objective Evolutionary Algorithms (MOEA). Existing MOEA frameworks do not support dynamic addition and extension of the problem formulation. The existing frameworks require a re-specification of the problem definition...

An Agent-Based Co-Evolutionary Multi-Objective Algorithm for Portfolio Optimization

Directory of Open Access Journals (Sweden)

Rafał Dreżewski

2017-08-01

Full Text Available Algorithms based on the process of natural evolution are widely used to solve multi-objective optimization problems. In this paper we propose the agent-based co-evolutionary algorithm for multi-objective portfolio optimization. The proposed technique is compared experimentally to the genetic algorithm, co-evolutionary algorithm and a more classical approach—the trend-following algorithm. During the experiments historical data from the Warsaw Stock Exchange is used in order to assess the performance of the compared algorithms. Finally, we draw some conclusions from these experiments, showing the strong and weak points of all the techniques.

Bi-objective optimization for multi-modal transportation routing planning problem based on Pareto optimality

Directory of Open Access Journals (Sweden)

Yan Sun

2015-09-01

Full Text Available Purpose: The purpose of study is to solve the multi-modal transportation routing planning problem that aims to select an optimal route to move a consignment of goods from its origin to its destination through the multi-modal transportation network. And the optimization is from two viewpoints including cost and time. Design/methodology/approach: In this study, a bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. Minimizing the total transportation cost and the total transportation time are set as the optimization objectives of the model. In order to balance the benefit between the two objectives, Pareto optimality is utilized to solve the model by gaining its Pareto frontier. The Pareto frontier of the model can provide the multi-modal transportation operator (MTO and customers with better decision support and it is gained by the normalized normal constraint method. Then, an experimental case study is designed to verify the feasibility of the model and Pareto optimality by using the mathematical programming software Lingo. Finally, the sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case. Findings: The calculation results indicate that the proposed model and Pareto optimality have good performance in dealing with the bi-objective optimization. The sensitivity analysis also shows the influence of the variation of the demand and supply on the multi-modal transportation organization clearly. Therefore, this method can be further promoted to the practice. Originality/value: A bi-objective mixed integer linear programming model is proposed to optimize the multi-modal transportation routing planning problem. The Pareto frontier based sensitivity analysis of the demand and supply in the multi-modal transportation organization is performed based on the designed case.

The Worst-Case Weighted Multi-Objective Game with an Application to Supply Chain Competitions.

Directory of Open Access Journals (Sweden)

Shaojian Qu

Full Text Available In this paper, we propose a worst-case weighted approach to the multi-objective n-person non-zero sum game model where each player has more than one competing objective. Our "worst-case weighted multi-objective game" model supposes that each player has a set of weights to its objectives and wishes to minimize its maximum weighted sum objectives where the maximization is with respect to the set of weights. This new model gives rise to a new Pareto Nash equilibrium concept, which we call "robust-weighted Nash equilibrium". We prove that the robust-weighted Nash equilibria are guaranteed to exist even when the weight sets are unbounded. For the worst-case weighted multi-objective game with the weight sets of players all given as polytope, we show that a robust-weighted Nash equilibrium can be obtained by solving a mathematical program with equilibrium constraints (MPEC. For an application, we illustrate the usefulness of the worst-case weighted multi-objective game to a supply chain risk management problem under demand uncertainty. By the comparison with the existed weighted approach, we show that our method is more robust and can be more efficiently used for the real-world applications.

Multi objective optimization of horizontal axis tidal current turbines, using Meta heuristics algorithms

International Nuclear Information System (INIS)

Tahani, Mojtaba; Babayan, Narek; Astaraei, Fatemeh Razi; Moghadam, Ali

2015-01-01

Highlights: • The performance of four different Meta heuristic optimization algorithms was studied. • Power coefficient and produced torque on stationary blade were selected as objective functions. • Chord and twist distributions were selected as decision variables. • All optimization algorithms were combined with blade element momentum theory. • The best Pareto front was obtained by multi objective flower pollination algorithm for HATCTs. - Abstract: The performance of horizontal axis tidal current turbines (HATCT) strongly depends on their geometry. According to this fact, the optimum performance will be achieved by optimized geometry. In this research study, the multi objective optimization of the HATCT is carried out by using four different multi objective optimization algorithms and their performance is evaluated in combination with blade element momentum theory (BEM). The second version of non-dominated sorting genetic algorithm (NSGA-II), multi objective particle swarm optimization algorithm (MOPSO), multi objective cuckoo search algorithm (MOCS) and multi objective flower pollination algorithm (MOFPA) are the selected algorithms. The power coefficient and the produced torque on stationary blade are selected as objective functions and chord and twist distributions along the blade span are selected as decision variables. These algorithms are combined with the blade element momentum (BEM) theory for the purpose of achieving the best Pareto front. The obtained Pareto fronts are compared with each other. Different sets of experiments are carried out by considering different numbers of iterations, population size and tip speed ratios. The Pareto fronts which are achieved by MOFPA and NSGA-II have better quality in comparison to MOCS and MOPSO, but on the other hand a detail comparison between the first fronts of MOFPA and NSGA-II indicated that MOFPA algorithm can obtain the best Pareto front and can maximize the power coefficient up to 4.3% and the

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

International Nuclear Information System (INIS)

Zhou, Z; Folkert, M; Wang, J

2016-01-01

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

SU-F-R-10: Selecting the Optimal Solution for Multi-Objective Radiomics Model

Energy Technology Data Exchange (ETDEWEB)

Zhou, Z; Folkert, M; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: To develop an evidential reasoning approach for selecting the optimal solution from a Pareto solution set obtained by a multi-objective radiomics model for predicting distant failure in lung SBRT. Methods: In the multi-objective radiomics model, both sensitivity and specificity are considered as the objective functions simultaneously. A Pareto solution set with many feasible solutions will be resulted from the multi-objective optimization. In this work, an optimal solution Selection methodology for Multi-Objective radiomics Learning model using the Evidential Reasoning approach (SMOLER) was proposed to select the optimal solution from the Pareto solution set. The proposed SMOLER method used the evidential reasoning approach to calculate the utility of each solution based on pre-set optimal solution selection rules. The solution with the highest utility was chosen as the optimal solution. In SMOLER, an optimal learning model coupled with clonal selection algorithm was used to optimize model parameters. In this study, PET, CT image features and clinical parameters were utilized for predicting distant failure in lung SBRT. Results: Total 126 solution sets were generated by adjusting predictive model parameters. Each Pareto set contains 100 feasible solutions. The solution selected by SMOLER within each Pareto set was compared to the manually selected optimal solution. Five-cross-validation was used to evaluate the optimal solution selection accuracy of SMOLER. The selection accuracies for five folds were 80.00%, 69.23%, 84.00%, 84.00%, 80.00%, respectively. Conclusion: An optimal solution selection methodology for multi-objective radiomics learning model using the evidential reasoning approach (SMOLER) was proposed. Experimental results show that the optimal solution can be found in approximately 80% cases.

Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic

DEFF Research Database (Denmark)

Govindan, Kannan; Jafarian, Ahmad; Nourbakhsh, Vahid

2015-01-01

simultaneously considering the sustainable OAP in the sustainable SCND as a strategic decision. The proposed supply chain network is composed of five echelons including suppliers classified in different classes, plants, distribution centers that dispatch products via two different ways, direct shipment......, a novel multi-objective hybrid approach called MOHEV with two strategies for its best particle selection procedure (BPSP), minimum distance, and crowding distance is proposed. MOHEV is constructed through hybridization of two multi-objective algorithms, namely the adapted multi-objective electromagnetism...

Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

Energy Technology Data Exchange (ETDEWEB)

Bellary, Sayed Ahmed Imran; Samad, Abdus [Indian Institute of Technology Madras, Chennai (India); Husain, Afzal [Sultan Qaboos University, Al-Khoudh (Oman)

2014-12-15

The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

Effectiveness of meta-models for multi-objective optimization of centrifugal impeller

International Nuclear Information System (INIS)

Bellary, Sayed Ahmed Imran; Samad, Abdus; Husain, Afzal

2014-01-01

The major issue of multiple fidelity based analysis and optimization of fluid machinery system depends upon the proper construction of low fidelity model or meta-model. A low fidelity model uses responses obtained from a high fidelity model, and the meta-model is then used to produce population of solutions required for evolutionary algorithm for multi-objective optimization. The Pareto-optimal front which shows functional relationships among the multiple objectives can produce erroneous results if the low fidelity models are not well-constructed. In the present research, response surface approximation and Kriging meta-models were evaluated for their effectiveness for the application in the turbomachinery design and optimization. A high fidelity model such as CFD technique along with the metamodels was used to obtain Pareto-optimal front via multi-objective genetic algorithm. A centrifugal impeller has been considered as case study to find relationship between two conflicting objectives, viz., hydraulic efficiency and head. Design variables from the impeller geometry have been chosen and the responses of the objective functions were evaluated through CFD analysis. The fidelity of each metamodel has been discussed in context of their predictions in entire design space in general and near optimal region in particular. Exploitation of the multiple meta-models enhances the quality of multi-objective optimization and provides the information pertaining to fidelity of optimization model. It was observed that the Kriging meta-model was better suited for this type of problem as it involved less approximation error in the Pareto-optimal front.

Multi-Objective Optimization for Energy Performance Improvement of Residential Buildings: A Comparative Study

Directory of Open Access Journals (Sweden)

Kangji Li

2017-02-01

Full Text Available Numerous conflicting criteria exist in building design optimization, such as energy consumption, greenhouse gas emission and indoor thermal performance. Different simulation-based optimization strategies and various optimization algorithms have been developed. A few of them are analyzed and compared in solving building design problems. This paper presents an efficient optimization framework to facilitate optimization designs with the aid of commercial simulation software and MATLAB. The performances of three optimization strategies, including the proposed approach, GenOpt method and artificial neural network (ANN method, are investigated using a case study of a simple building energy model. Results show that the proposed optimization framework has competitive performances compared with the GenOpt method. Further, in another practical case, four popular multi-objective algorithms, e.g., the non-dominated sorting genetic algorithm (NSGA-II, multi-objective particle swarm optimization (MOPSO, the multi-objective genetic algorithm (MOGA and multi-objective differential evolution (MODE, are realized using the propose optimization framework and compared with three criteria. Results indicate that MODE achieves close-to-optimal solutions with the best diversity and execution time. An uncompetitive result is achieved by the MOPSO in this case study.

Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

Science.gov (United States)

2010-03-01

David A. Van Veldhuizen . Evo- lutionary Algorithms for Solving Multi-Objective Problems. Springer, New York, NY, 2nd edition, 2007. [9] Dean, Thomas...J.I. van Hemert, E. Marchiori, and A. G. Steenbeek. “Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method

Science.gov (United States)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability.

Adaptive multi-objective Optimization scheme for cognitive radio resource management

KAUST Repository

Alqerm, Ismail; Shihada, Basem

2014-01-01

configuration by exploiting optimization and machine learning techniques. In this paper, we propose an Adaptive Multi-objective Optimization Scheme (AMOS) for cognitive radio resource management to improve spectrum operation and network performance

Multi-objective game-theory models for conflict analysis in reservoir watershed management.

Science.gov (United States)

Lee, Chih-Sheng

2012-05-01

This study focuses on the development of a multi-objective game-theory model (MOGM) for balancing economic and environmental concerns in reservoir watershed management and for assistance in decision. Game theory is used as an alternative tool for analyzing strategic interaction between economic development (land use and development) and environmental protection (water-quality protection and eutrophication control). Geographic information system is used to concisely illustrate and calculate the areas of various land use types. The MOGM methodology is illustrated in a case study of multi-objective watershed management in the Tseng-Wen reservoir, Taiwan. The innovation and advantages of MOGM can be seen in the results, which balance economic and environmental concerns in watershed management and which can be interpreted easily by decision makers. For comparison, the decision-making process using conventional multi-objective method to produce many alternatives was found to be more difficult. Copyright © 2012 Elsevier Ltd. All rights reserved.

Provisional-Ideal-Point-Based Multi-objective Optimization Method for Drone Delivery Problem

Science.gov (United States)

Omagari, Hiroki; Higashino, Shin-Ichiro

2018-04-01

In this paper, we proposed a new evolutionary multi-objective optimization method for solving drone delivery problems (DDP). It can be formulated as a constrained multi-objective optimization problem. In our previous research, we proposed the "aspiration-point-based method" to solve multi-objective optimization problems. However, this method needs to calculate the optimal values of each objective function value in advance. Moreover, it does not consider the constraint conditions except for the objective functions. Therefore, it cannot apply to DDP which has many constraint conditions. To solve these issues, we proposed "provisional-ideal-point-based method." The proposed method defines a "penalty value" to search for feasible solutions. It also defines a new reference solution named "provisional-ideal point" to search for the preferred solution for a decision maker. In this way, we can eliminate the preliminary calculations and its limited application scope. The results of the benchmark test problems show that the proposed method can generate the preferred solution efficiently. The usefulness of the proposed method is also demonstrated by applying it to DDP. As a result, the delivery path when combining one drone and one truck drastically reduces the traveling distance and the delivery time compared with the case of using only one truck.

Portable scintillation gamma-spectrometer for field measurement of radioactivity in extensive objects; Portativnyj stsintillyatsionnyj gamma-spektrometr dlya izmereniya radioaktivnosti ob''emnykh sred v polevykh usloviyakh

Energy Technology Data Exchange (ETDEWEB)

Besshejko, O. A. [Taras Shevchenko National University of Kyiv, Kyiv (Ukraine); Vishnevskij, I. N.; Denisenko, R. V.; Malyuk, I. A.; Petrosyan, Eh. E. [Institute for Nuclear Research, Kyiv (Ukraine); Karpenko, S. A.; Prijmak, V. N. [Ukrainian Association for Valves Industry, Dnepropetrovsk (Ukraine)

2011-07-01

The portable scintillated gamma-spectrometer for field measurement of radioactivity in the volume objects was designed. The crystal CdWO{sub 4} that has weak dependence of light yield from temperature in combination with PMT was applied as the gamma-rays detector. The design of the device provides the possibility for measuring radioactivity of the extensive objects in 4{pi}-geometry without background measurements. In this case the value of the efficiency needed for specific activity calculation in close approximation depends only from crystal geometry and density of measuring object and may be set as the device parameter without efficiency calibration procedure during the operation. The spectrometer does not have an auxiliary radio-active source and connecting cables in the composition. The smartphone operated under Windows Mobile is used as the control module. Bluetooth connection provides data exchange between smartphone and measurement head.

Robust multi-objective calibration strategies – possibilities for improving flood forecasting

Directory of Open Access Journals (Sweden)

G. H. Schmitz

2012-10-01

Full Text Available Process-oriented rainfall-runoff models are designed to approximate the complex hydrologic processes within a specific catchment and in particular to simulate the discharge at the catchment outlet. Most of these models exhibit a high degree of complexity and require the determination of various parameters by calibration. Recently, automatic calibration methods became popular in order to identify parameter vectors with high corresponding model performance. The model performance is often assessed by a purpose-oriented objective function. Practical experience suggests that in many situations one single objective function cannot adequately describe the model's ability to represent any aspect of the catchment's behaviour. This is regardless of whether the objective is aggregated of several criteria that measure different (possibly opposite aspects of the system behaviour. One strategy to circumvent this problem is to define multiple objective functions and to apply a multi-objective optimisation algorithm to identify the set of Pareto optimal or non-dominated solutions. Nonetheless, there is a major disadvantage of automatic calibration procedures that understand the problem of model calibration just as the solution of an optimisation problem: due to the complex-shaped response surface, the estimated solution of the optimisation problem can result in different near-optimum parameter vectors that can lead to a very different performance on the validation data. Bárdossy and Singh (2008 studied this problem for single-objective calibration problems using the example of hydrological models and proposed a geometrical sampling approach called Robust Parameter Estimation (ROPE. This approach applies the concept of data depth in order to overcome the shortcomings of automatic calibration procedures and find a set of robust parameter vectors. Recent studies confirmed the effectivity of this method. However, all ROPE approaches published so far just identify

MultiController: the PLC-SCADA object for advanced regulation

CERN Document Server

Ortola, J

2007-01-01

Nowadays, industrial solutions with PLCs (Programmable Logic Controllers) have basic control loop features. The SCADA (Supervisory Control And Data Acquisition) system is a key point of the process control system due to an efficient HMI (Human Machine Interfaces) that provides an open method of tuning and leading possibilities. As a consequence, advanced control algorithms have to be developed and implemented for those PLC-SCADA solutions in order to provide perspectives in solving complex and critical regulation problems. The MultiController is an object integrated for a large scale project at CERN (the European Organization for Nuclear Research) named LHC-GCS (Large Hadron Collider - Gas Control System). It is developed for a Framework called CERN-UNICOS based on PLC-SCADA facilities. The MultiController object offers various advanced control loop strategies. It gives to the user advanced control algorithms like PID, Smith Predictor, PFC, GPC and RST. It is implemented as a monolithic entity (in PLC and SCA...

A performance comparison of multi-objective optimization algorithms for solving nearly-zero-energy-building design problems

NARCIS (Netherlands)

Hamdy, M.; Nguyen, A.T. (Anh Tuan); Hensen, J.L.M.

2016-01-01

Integrated building design is inherently a multi-objective optimization problem where two or more conflicting objectives must be minimized and/or maximized concurrently. Many multi-objective optimization algorithms have been developed; however few of them are tested in solving building design

Flexible Multi-Objective Transmission Expansion Planning with Adjustable Risk Aversion

Directory of Open Access Journals (Sweden)

Jing Qiu

2017-07-01

Full Text Available This paper presents a multi-objective transmission expansion planning (TEP framework. Rather than using the conventional deterministic reliability criterion, a risk component based on the probabilistic reliability criterion is incorporated into the TEP objectives. This risk component can capture the stochastic nature of power systems, such as load and wind power output variations, component availability, and incentive-based demand response (IBDR costs. Specifically, the formulation of risk value after risk aversion is explicitly given, and it aims to provide network planners with the flexibility to conduct risk analysis. Thus, a final expansion plan can be selected according to individual risk preferences. Moreover, the economic value of IBDR is modeled and integrated into the cost objective. In addition, a relatively new multi-objective evolutionary algorithm called the MOEA/D is introduced and employed to find Pareto optimal solutions, and tradeoffs between overall cost and risk are provided. The proposed approach is numerically verified on the Garver’s six-bus, IEEE 24-bus RTS and Polish 2383-bus systems. Case study results demonstrate that the proposed approach can effectively reduce cost and hedge risk in relation to increasing wind power integration.

Multi-objective optimization of a vertical ground source heat pump using evolutionary algorithm

International Nuclear Information System (INIS)

Sayyaadi, Hoseyn; Amlashi, Emad Hadaddi; Amidpour, Majid

2009-01-01

Thermodynamic and thermoeconomic optimization of a vertical ground source heat pump system has been studied. A model based on the energy and exergy analysis is presented here. An economic model of the system is developed according to the Total Revenue Requirement (TRR) method. The objective functions based on the thermodynamic and thermoeconomic analysis are developed. The proposed vertical ground source heat pump system including eight decision variables is considered for optimization. An artificial intelligence technique known as evolutionary algorithm (EA) has been utilized as an optimization method. This approach has been applied to minimize either the total levelized cost of the system product or the exergy destruction of the system. Three levels of optimization including thermodynamic single objective, thermoeconomic single objective and multi-objective optimizations are performed. In Multi-objective optimization, both thermodynamic and thermoeconomic objectives are considered, simultaneously. In the case of multi-objective optimization, an example of decision-making process for selection of the final solution from available optimal points on Pareto frontier is presented. The results obtained using the various optimization approaches are compared and discussed. Further, the sensitivity of optimized systems to the interest rate, to the annual number of operating hours and to the electricity cost are studied in detail.

Considering Decision Variable Diversity in Multi-Objective Optimization: Application in Hydrologic Model Calibration

Science.gov (United States)

Sahraei, S.; Asadzadeh, M.

2017-12-01

Any modern multi-objective global optimization algorithm should be able to archive a well-distributed set of solutions. While the solution diversity in the objective space has been explored extensively in the literature, little attention has been given to the solution diversity in the decision space. Selection metrics such as the hypervolume contribution and crowding distance calculated in the objective space would guide the search toward solutions that are well-distributed across the objective space. In this study, the diversity of solutions in the decision-space is used as the main selection criteria beside the dominance check in multi-objective optimization. To this end, currently archived solutions are clustered in the decision space and the ones in less crowded clusters are given more chance to be selected for generating new solution. The proposed approach is first tested on benchmark mathematical test problems. Second, it is applied to a hydrologic model calibration problem with more than three objective functions. Results show that the chance of finding more sparse set of high-quality solutions increases, and therefore the analyst would receive a well-diverse set of options with maximum amount of information. Pareto Archived-Dynamically Dimensioned Search, which is an efficient and parsimonious multi-objective optimization algorithm for model calibration, is utilized in this study.

A Generalized Decision Framework Using Multi-objective Optimization for Water Resources Planning

Science.gov (United States)

Basdekas, L.; Stewart, N.; Triana, E.

2013-12-01

Colorado Springs Utilities (CSU) is currently engaged in an Integrated Water Resource Plan (IWRP) to address the complex planning scenarios, across multiple time scales, currently faced by CSU. The modeling framework developed for the IWRP uses a flexible data-centered Decision Support System (DSS) with a MODSIM-based modeling system to represent the operation of the current CSU raw water system coupled with a state-of-the-art multi-objective optimization algorithm. Three basic components are required for the framework, which can be implemented for planning horizons ranging from seasonal to interdecadal. First, a water resources system model is required that is capable of reasonable system simulation to resolve performance metrics at the appropriate temporal and spatial scales of interest. The system model should be an existing simulation model, or one developed during the planning process with stakeholders, so that 'buy-in' has already been achieved. Second, a hydrologic scenario tool(s) capable of generating a range of plausible inflows for the planning period of interest is required. This may include paleo informed or climate change informed sequences. Third, a multi-objective optimization model that can be wrapped around the system simulation model is required. The new generation of multi-objective optimization models do not require parameterization which greatly reduces problem complexity. Bridging the gap between research and practice will be evident as we use a case study from CSU's planning process to demonstrate this framework with specific competing water management objectives. Careful formulation of objective functions, choice of decision variables, and system constraints will be discussed. Rather than treating results as theoretically Pareto optimal in a planning process, we use the powerful multi-objective optimization models as tools to more efficiently and effectively move out of the inferior decision space. The use of this framework will help CSU

Confidence-Based Data Association and Discriminative Deep Appearance Learning for Robust Online Multi-Object Tracking.

Science.gov (United States)

Bae, Seung-Hwan; Yoon, Kuk-Jin

2018-03-01

Online multi-object tracking aims at estimating the tracks of multiple objects instantly with each incoming frame and the information provided up to the moment. It still remains a difficult problem in complex scenes, because of the large ambiguity in associating multiple objects in consecutive frames and the low discriminability between objects appearances. In this paper, we propose a robust online multi-object tracking method that can handle these difficulties effectively. We first define the tracklet confidence using the detectability and continuity of a tracklet, and decompose a multi-object tracking problem into small subproblems based on the tracklet confidence. We then solve the online multi-object tracking problem by associating tracklets and detections in different ways according to their confidence values. Based on this strategy, tracklets sequentially grow with online-provided detections, and fragmented tracklets are linked up with others without any iterative and expensive association steps. For more reliable association between tracklets and detections, we also propose a deep appearance learning method to learn a discriminative appearance model from large training datasets, since the conventional appearance learning methods do not provide rich representation that can distinguish multiple objects with large appearance variations. In addition, we combine online transfer learning for improving appearance discriminability by adapting the pre-trained deep model during online tracking. Experiments with challenging public datasets show distinct performance improvement over other state-of-the-arts batch and online tracking methods, and prove the effect and usefulness of the proposed methods for online multi-object tracking.

Analysing the performance of dynamic multi-objective optimisation algorithms

CSIR Research Space (South Africa)

Helbig, M

2013-06-01

Full Text Available and the goal of the algorithm is to track a set of tradeoff solutions over time. Analysing the performance of a dynamic multi-objective optimisation algorithm (DMOA) is not a trivial task. For each environment (before a change occurs) the DMOA has to find a set...

Multi-objective evolutionary optimisation for product design and manufacturing

CERN Document Server

2011-01-01

Presents state-of-the-art research in the area of multi-objective evolutionary optimisation for integrated product design and manufacturing Provides a comprehensive review of the literature Gives in-depth descriptions of recently developed innovative and novel methodologies, algorithms and systems in the area of modelling, simulation and optimisation

Study on multi-objective flexible job-shop scheduling problem considering energy consumption

Directory of Open Access Journals (Sweden)

Zengqiang Jiang

2014-06-01

Full Text Available Purpose: Build a multi-objective Flexible Job-shop Scheduling Problem(FJSP optimization model, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered, then Design a Modified Non-dominated Sorting Genetic Algorithm (NSGA-II based on blood variation for above scheduling model.Design/methodology/approach: A multi-objective optimization theory based on Pareto optimal method is used in carrying out the optimization model. NSGA-II is used to solve the model.Findings: By analyzing the research status and insufficiency of multi-objective FJSP, Find that the difference in scheduling will also have an effect on energy consumption in machining process and environmental emissions. Therefore, job-shop scheduling requires not only guaranteeing the processing quality, time and cost, but also optimizing operation plan of machines and minimizing energy consumption.Originality/value: A multi-objective FJSP optimization model is put forward, in which the makespan, processing cost, energy consumption and cost-weighted processing quality are considered. According to above model, Blood-Variation-based NSGA-II (BVNSGA-II is designed. In which, the chromosome mutation rate is determined after calculating the blood relationship between two cross chromosomes, crossover and mutation strategy of NSGA-II is optimized and the prematurity of population is overcome. Finally, the performance of the proposed model and algorithm is evaluated through a case study, and the results proved the efficiency and feasibility of the proposed model and algorithm.

A multi-objective robust optimization model for logistics planning in the earthquake response phase

NARCIS (Netherlands)

Najafi, M.; Eshghi, K.; Dullaert, W.E.H.

2013-01-01

Usually, resources are short in supply when earthquakes occur. In such emergency situations, disaster relief organizations must use these scarce resources efficiently to achieve the best possible emergency relief. This paper therefore proposes a multi-objective, multi-mode, multi-commodity, and

The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem

Science.gov (United States)

Aytaç Adalı, Esra; Tuş Işık, Ayşegül

2017-06-01

A decision making process requires the values of conflicting objectives for alternatives and the selection of the best alternative according to the needs of decision makers. Multi-objective optimization methods may provide solution for this selection. In this paper it is aimed to present the laptop selection problem based on MOORA plus full multiplicative form (MULTIMOORA) and multi-objective optimization on the basis of simple ratio analysis (MOOSRA) which are relatively new multi-objective optimization methods. The novelty of this paper is solving this problem with the MULTIMOORA and MOOSRA methods for the first time.

4D space access neutron spectrometer 4SEASONS (SIKI)

International Nuclear Information System (INIS)

Kajimoto, Ryoichi; Nakamura, Mitsutaka

2010-01-01

The 4D Space Access Neutron Spectrometer (4SEASONS) is a high-intensity Fermi-chopper spectrometer. It is intended to provide high counting rate for thermal neutrons with medium resolution (ΔE/E i -6% at E=0) to efficiently collect weak inelastic signals from novel spin and lattice dynamics especially in high-T c superconductors and related materials. To achieve this goal, the spectrometer equips advanced instrumental design such as an elliptic-shaped converging neutron guide coated with high-Q c (m=3-4) supermirror, and long-length (2.5 m) 3 He position sensitive detectors (PSDs) arranged cylindrically inside the vacuum scattering chamber. Furthermore, the spectrometer is ready for multi-incident-energy measurements by the repetition rate multiplication method, which greatly improves the measurement efficiency. (author)

Chance-constrained multi-objective optimization of groundwater remediation design at DNAPLs-contaminated sites using a multi-algorithm genetically adaptive method.

Science.gov (United States)

Ouyang, Qi; Lu, Wenxi; Hou, Zeyu; Zhang, Yu; Li, Shuai; Luo, Jiannan

2017-05-01

In this paper, a multi-algorithm genetically adaptive multi-objective (AMALGAM) method is proposed as a multi-objective optimization solver. It was implemented in the multi-objective optimization of a groundwater remediation design at sites contaminated by dense non-aqueous phase liquids. In this study, there were two objectives: minimization of the total remediation cost, and minimization of the remediation time. A non-dominated sorting genetic algorithm II (NSGA-II) was adopted to compare with the proposed method. For efficiency, the time-consuming surfactant-enhanced aquifer remediation simulation model was replaced by a surrogate model constructed by a multi-gene genetic programming (MGGP) technique. Similarly, two other surrogate modeling methods-support vector regression (SVR) and Kriging (KRG)-were employed to make comparisons with MGGP. In addition, the surrogate-modeling uncertainty was incorporated in the optimization model by chance-constrained programming (CCP). The results showed that, for the problem considered in this study, (1) the solutions obtained by AMALGAM incurred less remediation cost and required less time than those of NSGA-II, indicating that AMALGAM outperformed NSGA-II. It was additionally shown that (2) the MGGP surrogate model was more accurate than SVR and KRG; and (3) the remediation cost and time increased with the confidence level, which can enable decision makers to make a suitable choice by considering the given budget, remediation time, and reliability. Copyright © 2017 Elsevier B.V. All rights reserved.

Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

Directory of Open Access Journals (Sweden)

Fonseca Carlos M

2010-10-01

Full Text Available Abstract Background Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function. Results & Discussion We present a novel scan statistic algorithm employing a function based on the graph topology to penalize the presence of under-populated disconnection nodes in candidate clusters, the disconnection nodes cohesion function. A disconnection node is defined as a region within a cluster, such that its removal disconnects the cluster. By applying this function, the most geographically meaningful clusters are sifted through the immense set of possible irregularly shaped candidate cluster solutions. To evaluate the statistical significance of solutions for multi-objective scans, a statistical approach based on the concept of attainment function is used. In this paper we compared different penalized likelihoods employing the geometric and non-connectivity regularity functions and the novel disconnection nodes cohesion function. We also build multi-objective scans using those three functions and compare them with the previous penalized likelihood scans. An application is presented using comprehensive state-wide data for Chagas' disease in puerperal women in Minas Gerais state, Brazil. Conclusions We show that, compared to the other single-objective algorithms, multi-objective scans present better performance, regarding power, sensitivity and positive predicted value. The multi-objective non-connectivity scan is faster and better suited for the

Distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm for deployment of wireless sensor networks

DEFF Research Database (Denmark)

Cao, Bin; Zhao, Jianwei; Yang, Po

2018-01-01

-objective evolutionary algorithms the Cooperative Coevolutionary Generalized Differential Evolution 3, the Cooperative Multi-objective Differential Evolution and the Nondominated Sorting Genetic Algorithm III, the proposed algorithm addresses the deployment optimization problem efficiently and effectively.......Using immune algorithms is generally a time-intensive process especially for problems with a large number of variables. In this paper, we propose a distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm that is implemented using the message passing interface...... (MPI). The proposed algorithm is composed of three layers: objective, group and individual layers. First, for each objective in the multi-objective problem to be addressed, a subpopulation is used for optimization, and an archive population is used to optimize all the objectives. Second, the large...

All-hexahedral meshing and remeshing for multi-object manufacturing applications

DEFF Research Database (Denmark)

Nielsen, Chris Valentin; Fernandes, J.L.M.; Martins, P.A.F.

2013-01-01

new developments related to the construction of adaptive core meshes and processing of multi-objects that are typical of manufacturing applications.Along with the aforementioned improvements there are other developments that will also be presented due to their effectiveness in increasing.......The presentation is enriched with examples taken from pure geometry and metal forming applications, and a resistance projection welding industrial test case consisting of four different objects is included to show the capabilities of selective remeshing of objects while maintaining contact conditions and local...

A fuzzy multi-objective optimization model for sustainable reverse logistics network design

DEFF Research Database (Denmark)

Govindan, Kannan; Paam, Parichehr; Abtahi, Amir Reza

2016-01-01

Decreasing the environmental impact, increasing the degree of social responsibility, and considering the economic motivations of organizations are three significant features in designing a reverse logistics network under sustainability respects. Developing a model, which can simultaneously consider...... a multi-echelon multi-period multi-objective model for a sustainable reverse logistics network. To reflect all aspects of sustainability, we try to minimize the present value of costs, as well as environmental impacts, and optimize the social responsibility as objective functions of the model. In order...... these environmental, social, and economic aspects and their indicators, is an important problem for both researchers and practitioners. In this paper, we try to address this comprehensive approach by using indicators for measurement of aforementioned aspects and by applying fuzzy mathematical programming to design...

Multi-objective parallel particle swarm optimization for day-ahead Vehicle-to-Grid scheduling

DEFF Research Database (Denmark)

Soares, Joao; Vale, Zita; Canizes, Bruno

2013-01-01

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle-To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming...... to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow...

The high-resolution time-of-flight spectrometer TOFTOF

Energy Technology Data Exchange (ETDEWEB)

Unruh, Tobias [Technische Universitaet Muenchen, Forschungsneutronenquelle Heinz Maier-Leibnitz FRM II and Physik Department E13, Lichtenbergstr. 1, 85747 Garching (Germany)], E-mail: Tobias.Unruh@frm2.tum.de; Neuhaus, Juergen; Petry, Winfried [Technische Universitaet Muenchen, Forschungsneutronenquelle Heinz Maier-Leibnitz FRM II and Physik Department E13, Lichtenbergstr. 1, 85747 Garching (Germany)

2007-10-11

The TOFTOF spectrometer is a multi-disc chopper time-of-flight spectrometer for cold neutrons at the research neutron source Heinz Maier-Leibnitz (FRM II). After five reactor cycles of routine operation the characteristics of the instrument are reported in this article. The spectrometer features an excellent signal to background ratio due to its remote position in the neutron guide hall, an elaborated shielding concept and an s-shaped curved primary neutron guide which acts i.a. as a neutron velocity filter. The spectrometer is fed with neutrons from the undermoderated cold neutron source of the FRM II leading to a total neutron flux of {approx}10{sup 10}n/cm{sup 2}/s in the continuous white beam at the sample position distributed over a continuous and particularly broad wavelength spectrum. A high energy resolution is achieved by the use of high speed chopper discs made of carbon-fiber-reinforced plastic. In the combination of intensity, resolution and signal to background ratio the spectrometer offers new scientific prospects in the fields of inelastic and quasielastic neutron scattering.

The high-resolution time-of-flight spectrometer TOFTOF

Science.gov (United States)

Unruh, Tobias; Neuhaus, Jürgen; Petry, Winfried

2007-10-01

The TOFTOF spectrometer is a multi-disc chopper time-of-flight spectrometer for cold neutrons at the research neutron source Heinz Maier-Leibnitz (FRM II). After five reactor cycles of routine operation the characteristics of the instrument are reported in this article. The spectrometer features an excellent signal to background ratio due to its remote position in the neutron guide hall, an elaborated shielding concept and an s-shaped curved primary neutron guide which acts i.a. as a neutron velocity filter. The spectrometer is fed with neutrons from the undermoderated cold neutron source of the FRM II leading to a total neutron flux of ˜1010n/cm2/s in the continuous white beam at the sample position distributed over a continuous and particularly broad wavelength spectrum. A high energy resolution is achieved by the use of high speed chopper discs made of carbon-fiber-reinforced plastic. In the combination of intensity, resolution and signal to background ratio the spectrometer offers new scientific prospects in the fields of inelastic and quasielastic neutron scattering.

Project overview of OPTIMOS-EVE: the fibre-fed multi-object spectrograph for the E-ELT

NARCIS (Netherlands)

Navarro, R.; Chemla, F.; Bonifacio, P.; Flores, H.; Guinouard, I.; Huet, J.-M.; Puech, M.; Royer, F.; Pragt, J.H.; Wulterkens, G.; Sawyer, E.C.; Caldwell, M.E.; Tosh, I.A.J.; Whalley, M.S.; Woodhouse, G.F.W.; Spanò, P.; Di Marcantonio, P.; Andersen, M.I.; Dalton, G.B.; Kaper, L.; Hammer, F.

2010-01-01

OPTIMOS-EVE (OPTical Infrared Multi Object Spectrograph - Extreme Visual Explorer) is the fibre fed multi object spectrograph proposed for the European Extremely Large Telescope (E-ELT), planned to be operational in 2018 at Cerro Armazones (Chile). It is designed to provide a spectral resolution of

Multi-objective room acoustic optimization of timber folded plate structure

DEFF Research Database (Denmark)

Skov, Rasmus; Parigi, Dario; Damkilde, Lars

2017-01-01

This paper investigates the application of multi-objective optimization in the design of timber folded plate structures in the scope of the architectural design process. Considering contrasting objectives of structural displacement, early decay time (EDT), clarity (C50) and sound strength (G......), the methodology applied in two benchmarks tests, encompasses both structural and acoustic performance when determining folding characteristics and directionality of surfaces in a timber folded plate structure....

Fuzzy Multi Objective Linear Programming Problem with Imprecise Aspiration Level and Parameters

Directory of Open Access Journals (Sweden)

Zahra Shahraki

2015-07-01

Full Text Available This paper considers the multi-objective linear programming problems with fuzzygoal for each of the objective functions and constraints. Most existing works deal withlinear membership functions for fuzzy goals. In this paper, exponential membershipfunction is used.

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Science.gov (United States)

Budinich, Marko; Bourdon, Jérémie; Larhlimi, Abdelhalim; Eveillard, Damien

2017-01-01

Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs) for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA) and multi-objective flux variability analysis (MO-FVA). Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity) that take place at the ecosystem scale.

Analytic hierarchy process-based approach for selecting a Pareto-optimal solution of a multi-objective, multi-site supply-chain planning problem

Science.gov (United States)

Ayadi, Omar; Felfel, Houssem; Masmoudi, Faouzi

2017-07-01

The current manufacturing environment has changed from traditional single-plant to multi-site supply chain where multiple plants are serving customer demands. In this article, a tactical multi-objective, multi-period, multi-product, multi-site supply-chain planning problem is proposed. A corresponding optimization model aiming to simultaneously minimize the total cost, maximize product quality and maximize the customer satisfaction demand level is developed. The proposed solution approach yields to a front of Pareto-optimal solutions that represents the trade-offs among the different objectives. Subsequently, the analytic hierarchy process method is applied to select the best Pareto-optimal solution according to the preferences of the decision maker. The robustness of the solutions and the proposed approach are discussed based on a sensitivity analysis and an application to a real case from the textile and apparel industry.

Computing Convex Coverage Sets for Faster Multi-Objective Coordination

NARCIS (Netherlands)

Roijers, D.M.; Whiteson, S.; Oliehoek, F.A.

2015-01-01

In this article, we propose new algorithms for multi-objective coordination graphs (MO-CoGs). Key to the efficiency of these algorithms is that they compute a convex coverage set (CCS) instead of a Pareto coverage set (PCS). Not only is a CCS a sufficient solution set for a large class of problems,

Multi-objective decision-making framework for effective waste collection in smart cities

CSIR Research Space (South Africa)

Manqele, Lindelweyizizwe

2017-10-01

Full Text Available T-enabled objects. This implies taking into account multi-objective goals in the collection process while dealing with complexities such as data loss during IoT based data collection. Understanding current decision-making algorithms highlights the deeper insight...

A framework for multi-object tracking over distributed wireless camera networks

Science.gov (United States)

Gau, Victor; Hwang, Jenq-Neng

2010-07-01

In this paper, we propose a unified framework targeting at two important issues in a distributed wireless camera network, i.e., object tracking and network communication, to achieve reliable multi-object tracking over distributed wireless camera networks. In the object tracking part, we propose a fully automated approach for tracking of multiple objects across multiple cameras with overlapping and non-overlapping field of views without initial training. To effectively exchange the tracking information among the distributed cameras, we proposed an idle probability based broadcasting method, iPro, which adaptively adjusts the broadcast probability to improve the broadcast effectiveness in a dense saturated camera network. Experimental results for the multi-object tracking demonstrate the promising performance of our approach on real video sequences for cameras with overlapping and non-overlapping views. The modeling and ns-2 simulation results show that iPro almost approaches the theoretical performance upper bound if cameras are within each other's transmission range. In more general scenarios, e.g., in case of hidden node problems, the simulation results show that iPro significantly outperforms standard IEEE 802.11, especially when the number of competing nodes increases.

Multi-objective genetic algorithm for solving N-version program design problem

Energy Technology Data Exchange (ETDEWEB)

Yamachi, Hidemi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan) and Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamachi@nit.ac.jp; Tsujimura, Yasuhiro [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: tujimr@nit.ac.jp; Kambayashi, Yasushi [Department of Computer and Information Engineering, Nippon Institute of Technology, Miyashiro, Saitama 345-8501 (Japan)]. E-mail: yasushi@nit.ac.jp; Yamamoto, Hisashi [Department of Production and Information Systems Engineering, Tokyo Metropolitan Institute of Technology, Hino, Tokyo 191-0065 (Japan)]. E-mail: yamamoto@cc.tmit.ac.jp

2006-09-15

N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0-1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost.

Multi-objective genetic algorithm for solving N-version program design problem

International Nuclear Information System (INIS)

Yamachi, Hidemi; Tsujimura, Yasuhiro; Kambayashi, Yasushi; Yamamoto, Hisashi

2006-01-01

N-version programming (NVP) is a programming approach for constructing fault tolerant software systems. Generally, an optimization model utilized in NVP selects the optimal set of versions for each module to maximize the system reliability and to constrain the total cost to remain within a given budget. In such a model, while the number of versions included in the obtained solution is generally reduced, the budget restriction may be so rigid that it may fail to find the optimal solution. In order to ameliorate this problem, this paper proposes a novel bi-objective optimization model that maximizes the system reliability and minimizes the system total cost for designing N-version software systems. When solving multi-objective optimization problem, it is crucial to find Pareto solutions. It is, however, not easy to obtain them. In this paper, we propose a novel bi-objective optimization model that obtains many Pareto solutions efficiently. We formulate the optimal design problem of NVP as a bi-objective 0-1 nonlinear integer programming problem. In order to overcome this problem, we propose a Multi-objective genetic algorithm (MOGA), which is a powerful, though time-consuming, method to solve multi-objective optimization problems. When implementing genetic algorithm (GA), the use of an appropriate genetic representation scheme is one of the most important issues to obtain good performance. We employ random-key representation in our MOGA to find many Pareto solutions spaced as evenly as possible along the Pareto frontier. To pursue improve further performance, we introduce elitism, the Pareto-insertion and the Pareto-deletion operations based on distance between Pareto solutions in the selection process. The proposed MOGA obtains many Pareto solutions along the Pareto frontier evenly. The user of the MOGA can select the best compromise solution among the candidates by controlling the balance between the system reliability and the total cost

Application of evolutionary algorithms for multi-objective optimization in VLSI and embedded systems

CERN Document Server

2015-01-01

This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design. Many complex engineering optimization problems can be modelled as multi-objective formulations. This book provides an introduction to multi-objective optimization using meta-heuristic algorithms, GA and PSO, and how they can be applied to problems like hardware/software partitioning in embedded systems, circuit partitioning in VLSI, design of operational amplifiers in analog VLSI, design space exploration in high-level synthesis, delay fault testing in VLSI testing, and scheduling in heterogeneous distributed systems. It is shown how, in each case, the various aspects of the EA, namely its representation, and operators like crossover, mutation, etc. can be separately formulated to solve these problems. This book is intended for design engineers and researchers in the field ...

SU-F-R-46: Predicting Distant Failure in Lung SBRT Using Multi-Objective Radiomics Model

Energy Technology Data Exchange (ETDEWEB)

Zhou, Z; Folkert, M; Iyengar, P; Zhang, Y; Wang, J [UT Southwestern Medical Center, Dallas, TX (United States)

2016-06-15

Purpose: To predict distant failure in lung stereotactic body radiation therapy (SBRT) in early stage non-small cell lung cancer (NSCLC) by using a new multi-objective radiomics model. Methods: Currently, most available radiomics models use the overall accuracy as the objective function. However, due to data imbalance, a single object may not reflect the performance of a predictive model. Therefore, we developed a multi-objective radiomics model which considers both sensitivity and specificity as the objective functions simultaneously. The new model is used to predict distant failure in lung SBRT using 52 patients treated at our institute. Quantitative imaging features of PET and CT as well as clinical parameters are utilized to build the predictive model. Image features include intensity features (9), textural features (12) and geometric features (8). Clinical parameters for each patient include demographic parameters (4), tumor characteristics (8), treatment faction schemes (4) and pretreatment medicines (6). The modelling procedure consists of two steps: extracting features from segmented tumors in PET and CT; and selecting features and training model parameters based on multi-objective. Support Vector Machine (SVM) is used as the predictive model, while a nondominated sorting-based multi-objective evolutionary computation algorithm II (NSGA-II) is used for solving the multi-objective optimization. Results: The accuracy for PET, clinical, CT, PET+clinical, PET+CT, CT+clinical, PET+CT+clinical are 71.15%, 84.62%, 84.62%, 85.54%, 82.69%, 84.62%, 86.54%, respectively. The sensitivities for the above seven combinations are 41.76%, 58.33%, 50.00%, 50.00%, 41.67%, 41.67%, 58.33%, while the specificities are 80.00%, 92.50%, 90.00%, 97.50%, 92.50%, 97.50%, 97.50%. Conclusion: A new multi-objective radiomics model for predicting distant failure in NSCLC treated with SBRT was developed. The experimental results show that the best performance can be obtained by combining

Towards lexicographic multi-objective linear programming using grossone methodology

Science.gov (United States)

Cococcioni, Marco; Pappalardo, Massimo; Sergeyev, Yaroslav D.

2016-10-01

Lexicographic Multi-Objective Linear Programming (LMOLP) problems can be solved in two ways: preemptive and nonpreemptive. The preemptive approach requires the solution of a series of LP problems, with changing constraints (each time the next objective is added, a new constraint appears). The nonpreemptive approach is based on a scalarization of the multiple objectives into a single-objective linear function by a weighted combination of the given objectives. It requires the specification of a set of weights, which is not straightforward and can be time consuming. In this work we present both mathematical and software ingredients necessary to solve LMOLP problems using a recently introduced computational methodology (allowing one to work numerically with infinities and infinitesimals) based on the concept of grossone. The ultimate goal of such an attempt is an implementation of a simplex-like algorithm, able to solve the original LMOLP problem by solving only one single-objective problem and without the need to specify finite weights. The expected advantages are therefore obvious.

Optimal Golomb Ruler Sequences Generation for Optical WDM Systems: A Novel Parallel Hybrid Multi-objective Bat Algorithm

Science.gov (United States)

Bansal, Shonak; Singh, Arun Kumar; Gupta, Neena

2017-02-01

In real-life, multi-objective engineering design problems are very tough and time consuming optimization problems due to their high degree of nonlinearities, complexities and inhomogeneity. Nature-inspired based multi-objective optimization algorithms are now becoming popular for solving multi-objective engineering design problems. This paper proposes original multi-objective Bat algorithm (MOBA) and its extended form, namely, novel parallel hybrid multi-objective Bat algorithm (PHMOBA) to generate shortest length Golomb ruler called optimal Golomb ruler (OGR) sequences at a reasonable computation time. The OGRs found their application in optical wavelength division multiplexing (WDM) systems as channel-allocation algorithm to reduce the four-wave mixing (FWM) crosstalk. The performances of both the proposed algorithms to generate OGRs as optical WDM channel-allocation is compared with other existing classical computing and nature-inspired algorithms, including extended quadratic congruence (EQC), search algorithm (SA), genetic algorithms (GAs), biogeography based optimization (BBO) and big bang-big crunch (BB-BC) optimization algorithms. Simulations conclude that the proposed parallel hybrid multi-objective Bat algorithm works efficiently as compared to original multi-objective Bat algorithm and other existing algorithms to generate OGRs for optical WDM systems. The algorithm PHMOBA to generate OGRs, has higher convergence and success rate than original MOBA. The efficiency improvement of proposed PHMOBA to generate OGRs up to 20-marks, in terms of ruler length and total optical channel bandwidth (TBW) is 100 %, whereas for original MOBA is 85 %. Finally the implications for further research are also discussed.

A hybrid multi-objective cultural algorithm for short-term environmental/economic hydrothermal scheduling

International Nuclear Information System (INIS)

Lu Youlin; Zhou Jianzhong; Qin Hui; Wang Ying; Zhang Yongchuan

2011-01-01

Research highlights: → Multi-objective optimization model of short-term environmental/economic hydrothermal scheduling. → A hybrid multi-objective cultural algorithm (HMOCA) is presented. → New heuristic constraint handling methods are proposed. → Better quality solutions by reducing fuel cost and emission effects simultaneously are obtained. -- Abstract: The short-term environmental/economic hydrothermal scheduling (SEEHS) with the consideration of multiple objectives is a complicated non-linear constrained optimization problem with non-smooth and non-convex characteristics. In this paper, a multi-objective optimization model of SEEHS is proposed to consider the minimal of fuel cost and emission effects synthetically, and the transmission loss, the water transport delays between connected reservoirs as well as the valve-point effects of thermal plants are taken into consideration to formulate the problem precisely. Meanwhile, a hybrid multi-objective cultural algorithm (HMOCA) is presented to deal with SEEHS problem by optimizing both two objectives simultaneously. The proposed method integrated differential evolution (DE) algorithm into the framework of cultural algorithm model to implement the evolution of population space, and two knowledge structures in belief space are redefined according to the characteristics of DE and SEEHS problem to avoid premature convergence effectively. Moreover, in order to deal with the complicated constraints effectively, new heuristic constraint handling methods without any penalty factor settings are proposed in this paper. The feasibility and effectiveness of the proposed HMOCA method are demonstrated by two case studies of a hydrothermal power system. The simulation results reveal that, compared with other methods established recently, HMOCA can get better quality solutions by reducing fuel cost and emission effects simultaneously.

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Institute of Scientific and Technical Information of China (English)

Su YAN; Kaiquan CAI

2017-01-01

Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO),the network-wide 4D flight trajectories planning (N4DFTP) problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs) (3D position and time) for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategic level conflict management is developed in this paper.Specifically,a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm (MOMMA) that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the pro posed MOMMA is effective to solve the N4DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Directory of Open Access Journals (Sweden)

Su YAN

2017-06-01

Full Text Available Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO, the network-wide 4D flight trajectories planning (N4DFTP problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs (3D position and time for all the flights in the whole airway network. Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity, an efficient model for strategic-level conflict management is developed in this paper. Specifically, a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated. In consideration of the large-scale, high-complexity, and multi-objective characteristics of the N4DFTP problem, a multi-objective multi-memetic algorithm (MOMMA that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented. It is capable of rapidly and effectively allocating 4DTs via rerouting, target time controlling, and flight level changing. Additionally, to balance the ability of exploitation and exploration of the algorithm, a special hybridization scheme is adopted for the integration of local and global search. Empirical studies using real air traffic data in China with different network complexities show that the proposed MOMMA is effective to solve the N4DFTP problem. The solutions achieved are competitive for elaborate decision support under a TBO environment.

Ensemble-based hierarchical multi-objective production optimization of smart wells

NARCIS (Netherlands)

Fonseca, R.M.; Leeuwenburgh, O.; Van den Hof, P.M.J.; Jansen, J.D.

2014-01-01

In an earlier study two hierarchical multi-objective methods were suggested to include short-term targets in life-cycle production optimization. However this earlier study has two limitations: 1) the adjoint formulation is used to obtain gradient information, requiring simulator source code access

A Multi-Objective Trade-Off Model in Sustainable Construction Projects

Directory of Open Access Journals (Sweden)

Guangdong Wu

2017-10-01

Full Text Available Based on the consideration of the relative importance of sustainability-related objectives and the inherent nature of sustainable construction projects, this study proposes that the contractor can balance the levels of efforts and resources used to improve the overall project sustainability. A multi-objective trade-off model using game theory was established and verified through simulation and numerical example under a moral hazard situation. Results indicate that effort levels of the contractor on sustainability-related objectives are positively related to the outcome coefficient while negatively to the coefficients of effort cost of the relevant objectives. High levels of the relative importance of sustainability-related objectives contribute to high levels of effort of the contractor. With the variation in effort levels and the coefficient of benefit allocation, the project net benefit increases before declining. The function of project benefit has a marked peak value, with an inverted “U” shape. An equilibrium always exists as for the given relative importance and coefficients of the effort costs of sustainability-related objectives. Under this condition, the owner may offer the contractor a less intense incentive and motivate the contractor reasonably arranging input resources. The coefficient of benefit allocation is affected by the contractor characteristic factors and the project characteristic factors. The owner should balance these two types of factors and select the most appropriate incentive mechanism to improve the project benefit. Meanwhile, the contractor can balance the relative importance of the objectives and arrange the appropriate levels of effort and resources to achieve a sustainability-related objective. Very few studies have emphasized the effects of the relative importance of sustainability-related objectives on the benefits of sustainable construction projects. This study therefore builds a multi-objective trade

Multi-objective optimization approach for air traffic flow management

Directory of Open Access Journals (Sweden)

Fadil Rabie

2017-01-01

The decision-making stage was then performed with the aid of data clustering techniques to reduce the sizeof the Pareto-optimal set and obtain a smaller representation of the multi-objective design space, there by making it easier for the decision-maker to find satisfactory and meaningful trade-offs, and to select a preferred final design solution.

Multi-Modal Inference in Animacy Perception for Artificial Object

Directory of Open Access Journals (Sweden)

Kohske Takahashi

2011-10-01

Full Text Available Sometimes we feel animacy for artificial objects and their motion. Animals usually interact with environments through multiple sensory modalities. Here we investigated how the sensory responsiveness of artificial objects to the environment would contribute to animacy judgment for them. In a 90-s trial, observers freely viewed four objects moving in a virtual 3D space. The objects, whose position and motion were determined following Perlin-noise series, kept drifting independently in the space. Visual flashes, auditory bursts, or synchronous flashes and bursts appeared with 1–2 s intervals. The first object abruptly accelerated their motion just after visual flashes, giving an impression of responding to the flash. The second object responded to bursts. The third object responded to synchronous flashes and bursts. The forth object accelerated at a random timing independent of flashes and bursts. The observers rated how strongly they felt animacy for each object. The results showed that the object responding to the auditory bursts was rated as having weaker animacy compared to the other objects. This implies that sensory modality through which an object interacts with the environment may be a factor for animacy perception in the object and may serve as the basis of multi-modal and cross-modal inference of animacy.

Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm.

Science.gov (United States)

Feng, Yen-Yi; Wu, I-Chin; Chen, Tzu-Li

2017-03-01

The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

Image Segmentation Method Using Fuzzy C Mean Clustering Based on Multi-Objective Optimization

Science.gov (United States)

Chen, Jinlin; Yang, Chunzhi; Xu, Guangkui; Ning, Li

2018-04-01

Image segmentation is not only one of the hottest topics in digital image processing, but also an important part of computer vision applications. As one kind of image segmentation algorithms, fuzzy C-means clustering is an effective and concise segmentation algorithm. However, the drawback of FCM is that it is sensitive to image noise. To solve the problem, this paper designs a novel fuzzy C-mean clustering algorithm based on multi-objective optimization. We add a parameter λ to the fuzzy distance measurement formula to improve the multi-objective optimization. The parameter λ can adjust the weights of the pixel local information. In the algorithm, the local correlation of neighboring pixels is added to the improved multi-objective mathematical model to optimize the clustering cent. Two different experimental results show that the novel fuzzy C-means approach has an efficient performance and computational time while segmenting images by different type of noises.

Multi objective decision making in hybrid energy system design

Science.gov (United States)

Merino, Gabriel Guillermo

The design of grid-connected photovoltaic wind generator system supplying a farmstead in Nebraska has been undertaken in this dissertation. The design process took into account competing criteria that motivate the use of different sources of energy for electric generation. The criteria considered were 'Financial', 'Environmental', and 'User/System compatibility'. A distance based multi-objective decision making methodology was developed to rank design alternatives. The method is based upon a precedence order imposed upon the design objectives and a distance metric describing the performance of each alternative. This methodology advances previous work by combining ambiguous information about the alternatives with a decision-maker imposed precedence order in the objectives. Design alternatives, defined by the photovoltaic array and wind generator installed capacities, were analyzed using the multi-objective decision making approach. The performance of the design alternatives was determined by simulating the system using hourly data for an electric load for a farmstead and hourly averages of solar irradiation, temperature and wind speed from eight wind-solar energy monitoring sites in Nebraska. The spatial variability of the solar energy resource within the region was assessed by determining semivariogram models to krige hourly and daily solar radiation data. No significant difference was found in the predicted performance of the system when using kriged solar radiation data, with the models generated vs. using actual data. The spatial variability of the combined wind and solar energy resources was included in the design analysis by using fuzzy numbers and arithmetic. The best alternative was dependent upon the precedence order assumed for the main criteria. Alternatives with no PV array or wind generator dominated when the 'Financial' criteria preceded the others. In contrast, alternatives with a nil component of PV array but a high wind generator component

Control system and automation of the spectrum acquisition for a perturbed gamma-gamma angular correlation spectrometer

International Nuclear Information System (INIS)

Stachowski, Ademir F.A.; Lapolli, André L.; Carbonari, Artur W.; Saxena, Rajendra N.

2017-01-01

This work reports results from the automation of data acquisition for a gamma-gamma perturbed angular correlation (PAC) spectrometer located in the hyperfine interactions laboratory (LIH) of IPEN. A PAC spectrometer comprises usually four scintillation detectors (BaF_3 for example) and carries out time measurements from gamma rays of any two combinations of detectors in coincidence. Gamma rays come from the decay of probe nuclei embedded into the material to be studied. Measurements are generally performed as a function of temperature. The fourth-detectors PAC spectrometer was automated in Java language because it is a multi platform and object oriented programming language, having, therefore, portability and longer lifetime due to the ease of implementation of peripherals. Finally, the software has a friendly and intuitive interface with the user being able to operate the hardware manually or automatically with time acquisitions up to 100 consecutive hours. Before each acquisition, the software sets the temperature, waits for temperature stability and records data in different files. Moreover, the software also saves in a file all information of relevant parameters during acquisition. (author)

Control system and automation of the spectrum acquisition for a perturbed gamma-gamma angular correlation spectrometer

Energy Technology Data Exchange (ETDEWEB)

Stachowski, Ademir F.A.; Lapolli, André L.; Carbonari, Artur W.; Saxena, Rajendra N., E-mail: alapolli@ipen.br [Instituto de Pesquisas Energeticas e Nucleares (IPEN/CNEN-SP), Sao Paulo, SP (Brazil)

2017-11-01

This work reports results from the automation of data acquisition for a gamma-gamma perturbed angular correlation (PAC) spectrometer located in the hyperfine interactions laboratory (LIH) of IPEN. A PAC spectrometer comprises usually four scintillation detectors (BaF{sub 3} for example) and carries out time measurements from gamma rays of any two combinations of detectors in coincidence. Gamma rays come from the decay of probe nuclei embedded into the material to be studied. Measurements are generally performed as a function of temperature. The fourth-detectors PAC spectrometer was automated in Java language because it is a multi platform and object oriented programming language, having, therefore, portability and longer lifetime due to the ease of implementation of peripherals. Finally, the software has a friendly and intuitive interface with the user being able to operate the hardware manually or automatically with time acquisitions up to 100 consecutive hours. Before each acquisition, the software sets the temperature, waits for temperature stability and records data in different files. Moreover, the software also saves in a file all information of relevant parameters during acquisition. (author)

Nonlinear bioheat transfer models and multi-objective numerical optimization of the cryosurgery operations

Energy Technology Data Exchange (ETDEWEB)

Kudryashov, Nikolay A.; Shilnikov, Kirill E. [National Research Nuclear University MEPhI, Department of Applied Mathematics, Moscow (Russian Federation)

2016-06-08

Numerical computation of the three dimensional problem of the freezing interface propagation during the cryosurgery coupled with the multi-objective optimization methods is used in order to improve the efficiency and safety of the cryosurgery operations performing. Prostate cancer treatment and cutaneous cryosurgery are considered. The heat transfer in soft tissue during the thermal exposure to low temperature is described by the Pennes bioheat model and is coupled with an enthalpy method for blurred phase change computations. The finite volume method combined with the control volume approximation of the heat fluxes is applied for the cryosurgery numerical modeling on the tumor tissue of a quite arbitrary shape. The flux relaxation approach is used for the stability improvement of the explicit finite difference schemes. The method of the additional heating elements mounting is studied as an approach to control the cellular necrosis front propagation. Whereas the undestucted tumor tissue and destucted healthy tissue volumes are considered as objective functions, the locations of additional heating elements in cutaneous cryosurgery and cryotips in prostate cancer cryotreatment are considered as objective variables in multi-objective problem. The quasi-gradient method is proposed for the searching of the Pareto front segments as the multi-objective optimization problem solutions.

Electron volt neutron spectrometers

International Nuclear Information System (INIS)

Pietropaolo, A.; Senesi, R.

2011-01-01

The advent of pulsed neutron sources has made available intense fluxes of epithermal neutrons (500 meV ≤E≤100 eV ). The possibility to open new investigations on condensed matter with eV neutron scattering techniques, is related to the development of methods, concepts and devices that drive, or are inspired by, emerging studies at this energy scale. Electron volt spectrometers have undergone continuous improvements since the construction of the first prototype instruments, but in the last decade major breakthroughs have been accomplished in terms of resolution and counting statistics, leading, for example, to the direct measurement of the proton 3-D Born–Oppenheimer potential in any material, or to quantitatively probe nuclear quantum effects in hydrogen bonded systems. This paper reports on the most effective methods and concepts for energy analysis and detection, as well as devices for the optimization of electron volt spectrometers for different applications. This is set in the context of the progress made up to date in instrument development. Starting from early stages of development of the technique, particular emphasis will be given to the Vesuvio eV spectrometer at the ISIS neutron source, the first spectrometer where extensive scientific, as well as research and development programmes have been carried out. The potential offered by this type of instrumentation, from single particle excitations to momentum distribution studies, is then put in perspective into the emerging fields of eV spectroscopy applied to cultural heritages and neutron irradiation effects in electronics. - Highlights: ► Neutron spectrometers at eV energies. ► Methods and techniques for eV neutrons counting at spallation sources. ► Scattering, imaging and radiation hardness tests with multi-eV neutrons.

A multi-objective constraint-based approach for modeling genome-scale microbial ecosystems.

Directory of Open Access Journals (Sweden)

Marko Budinich

Full Text Available Interplay within microbial communities impacts ecosystems on several scales, and elucidation of the consequent effects is a difficult task in ecology. In particular, the integration of genome-scale data within quantitative models of microbial ecosystems remains elusive. This study advocates the use of constraint-based modeling to build predictive models from recent high-resolution -omics datasets. Following recent studies that have demonstrated the accuracy of constraint-based models (CBMs for simulating single-strain metabolic networks, we sought to study microbial ecosystems as a combination of single-strain metabolic networks that exchange nutrients. This study presents two multi-objective extensions of CBMs for modeling communities: multi-objective flux balance analysis (MO-FBA and multi-objective flux variability analysis (MO-FVA. Both methods were applied to a hot spring mat model ecosystem. As a result, multiple trade-offs between nutrients and growth rates, as well as thermodynamically favorable relative abundances at community level, were emphasized. We expect this approach to be used for integrating genomic information in microbial ecosystems. Following models will provide insights about behaviors (including diversity that take place at the ecosystem scale.

Improving package structure of object-oriented software using multi-objective optimization and weighted class connections

Directory of Open Access Journals (Sweden)

Amarjeet

2017-07-01

Full Text Available The software maintenance activities performed without following the original design decisions about the package structure usually deteriorate the quality of software modularization, leading to decay of the quality of the system. One of the main reasons for such structural deterioration is inappropriate grouping of source code classes in software packages. To improve such grouping/modular-structure, previous researchers formulated the software remodularization problem as an optimization problem and solved it using search-based meta-heuristic techniques. These optimization approaches aimed at improving the quality metrics values of the structure without considering the original package design decisions, often resulting into a totally new software modularization. The entirely changed software modularization becomes costly to realize as well as difficult to understand for the developers/maintainers. To alleviate this issue, we propose a multi-objective optimization approach to improve the modularization quality of an object-oriented system with minimum possible movement of classes between existing packages of original software modularization. The optimization is performed using NSGA-II, a widely-accepted multi-objective evolutionary algorithm. In order to ensure minimum modification of original package structure, a new approach of computing class relations using weighted strengths has been proposed here. The weights of relations among different classes are computed on the basis of the original package structure. A new objective function has been formulated using these weighted class relations. This objective function drives the optimization process toward better modularization quality simultaneously ensuring preservation of original structure. To evaluate the results of the proposed approach, a series of experiments are conducted over four real-worlds and two random software applications. The experimental results clearly indicate the effectiveness

Multi-objective ant algorithm for wireless sensor network positioning

International Nuclear Information System (INIS)

Fidanova, S.; Shindarov, M.; Marinov, P.

2013-01-01

It is impossible to imagine our modern life without telecommunications. Wireless networks are a part of telecommunications. Wireless sensor networks (WSN) consist of spatially distributed sensors, which communicate in wireless way. This network monitors physical or environmental conditions. The objective is the full coverage of the monitoring region and less energy consumption of the network. The most appropriate approach to solve the problem is metaheuristics. In this paper the full coverage of the area is treated as a constrain. The objectives which are optimized are a minimal number of sensors and energy (lifetime) of the network. We apply multi-objective Ant Colony Optimization to solve this important telecommunication problem. We chose MAX-MIN Ant System approach, because it is proven to converge to the global optima

A proposed neutron spectrometer system for JET

International Nuclear Information System (INIS)

Elevant, T.; Hellbom, G.; Scheffel, J.; Malmskog, S.

1979-12-01

A neutron spectrometer system is proposed primarily for measurements of ion temperature and density and ion beam energy distribution in extended fusion plasmas like e.g. in JET. Three different spectrometers are involved: time of flight, proton recoil and 3 He. Energy resolutions of a few percent both for DD and DT neutrons are provided. Six order of magnitudes in flux ranges will be covered by the system when employing multi-target systems. A neutron collimator and shielding system will be desirable in order to obtain relevant information. Due to the entire differences in energy and fluxes for DD and DT plasmas a flexible collimator-shielding system is recommended

Research on connection structure of aluminumbody bus using multi-objective topology optimization

Science.gov (United States)

Peng, Q.; Ni, X.; Han, F.; Rhaman, K.; Ulianov, C.; Fang, X.

2018-01-01

For connecting Aluminum Alloy bus body aluminum components often occur the problem of failure, a new aluminum alloy connection structure is designed based on multi-objective topology optimization method. Determining the shape of the outer contour of the connection structure with topography optimization, establishing a topology optimization model of connections based on SIMP density interpolation method, going on multi-objective topology optimization, and improving the design of the connecting piece according to the optimization results. The results show that the quality of the aluminum alloy connector after topology optimization is reduced by 18%, and the first six natural frequencies are improved and the strength performance and stiffness performance are obviously improved.

Investigating multi-objective fluence and beam orientation IMRT optimization

Science.gov (United States)

Potrebko, Peter S.; Fiege, Jason; Biagioli, Matthew; Poleszczuk, Jan

2017-07-01

Radiation Oncology treatment planning requires compromises to be made between clinical objectives that are invariably in conflict. It would be beneficial to have a ‘bird’s-eye-view’ perspective of the full spectrum of treatment plans that represent the possible trade-offs between delivering the intended dose to the planning target volume (PTV) while optimally sparing the organs-at-risk (OARs). In this work, the authors demonstrate Pareto-aware radiotherapy evolutionary treatment optimization (PARETO), a multi-objective tool featuring such bird’s-eye-view functionality, which optimizes fluence patterns and beam angles for intensity-modulated radiation therapy (IMRT) treatment planning. The problem of IMRT treatment plan optimization is managed as a combined monolithic problem, where all beam fluence and angle parameters are treated equally during the optimization. To achieve this, PARETO is built around a powerful multi-objective evolutionary algorithm, called Ferret, which simultaneously optimizes multiple fitness functions that encode the attributes of the desired dose distribution for the PTV and OARs. The graphical interfaces within PARETO provide useful information such as: the convergence behavior during optimization, trade-off plots between the competing objectives, and a graphical representation of the optimal solution database allowing for the rapid exploration of treatment plan quality through the evaluation of dose-volume histograms and isodose distributions. PARETO was evaluated for two relatively complex clinical cases, a paranasal sinus and a pancreas case. The end result of each PARETO run was a database of optimal (non-dominated) treatment plans that demonstrated trade-offs between the OAR and PTV fitness functions, which were all equally good in the Pareto-optimal sense (where no one objective can be improved without worsening at least one other). Ferret was able to produce high quality solutions even though a large number of parameters

A linear bi-level multi-objective program for optimal allocation of water resources.

Directory of Open Access Journals (Sweden)

Ijaz Ahmad

Full Text Available This paper presents a simple bi-level multi-objective linear program (BLMOLP with a hierarchical structure consisting of reservoir managers and several water use sectors under a multi-objective framework for the optimal allocation of limited water resources. Being the upper level decision makers (i.e., leader in the hierarchy, the reservoir managers control the water allocation system and tend to create a balance among the competing water users thereby maximizing the total benefits to the society. On the other hand, the competing water use sectors, being the lower level decision makers (i.e., followers in the hierarchy, aim only to maximize individual sectoral benefits. This multi-objective bi-level optimization problem can be solved using the simultaneous compromise constraint (SICCON technique which creates a compromise between upper and lower level decision makers (DMs, and transforms the multi-objective function into a single decision-making problem. The bi-level model developed in this study has been applied to the Swat River basin in Pakistan for the optimal allocation of water resources among competing water demand sectors and different scenarios have been developed. The application of the model in this study shows that the SICCON is a simple, applicable and feasible approach to solve the BLMOLP problem. Finally, the comparisons of the model results show that the optimization model is practical and efficient when it is applied to different conditions with priorities assigned to various water users.

Optimum analysis of pavement maintenance using multi-objective genetic algorithms

Directory of Open Access Journals (Sweden)

Amr A. Elhadidy

2015-04-01

Full Text Available Road network expansion in Egypt is considered as a vital issue for the development of the country. This is done while upgrading current road networks to increase the safety and efficiency. A pavement management system (PMS is a set of tools or methods that assist decision makers in finding optimum strategies for providing and maintaining pavements in a serviceable condition over a given period of time. A multi-objective optimization problem for pavement maintenance and rehabilitation strategies on network level is discussed in this paper. A two-objective optimization model considers minimum action costs and maximum condition for used road network. In the proposed approach, Markov-chain models are used for predicting the performance of road pavement and to calculate the expected decline at different periods of time. A genetic-algorithm-based procedure is developed for solving the multi-objective optimization problem. The model searched for the optimum maintenance actions at adequate time to be implemented on an appropriate pavement. Based on the computing results, the Pareto optimal solutions of the two-objective optimization functions are obtained. From the optimal solutions represented by cost and condition, a decision maker can easily obtain the information of the maintenance and rehabilitation planning with minimum action costs and maximum condition. The developed model has been implemented on a network of roads and showed its ability to derive the optimal solution.

Multi-objective experimental design for (13)C-based metabolic flux analysis.

Science.gov (United States)

Bouvin, Jeroen; Cajot, Simon; D'Huys, Pieter-Jan; Ampofo-Asiama, Jerry; Anné, Jozef; Van Impe, Jan; Geeraerd, Annemie; Bernaerts, Kristel

2015-10-01

(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective

Coupled Low-thrust Trajectory and System Optimization via Multi-Objective Hybrid Optimal Control

Science.gov (United States)

Vavrina, Matthew A.; Englander, Jacob Aldo; Ghosh, Alexander R.

2015-01-01

The optimization of low-thrust trajectories is tightly coupled with the spacecraft hardware. Trading trajectory characteristics with system parameters ton identify viable solutions and determine mission sensitivities across discrete hardware configurations is labor intensive. Local independent optimization runs can sample the design space, but a global exploration that resolves the relationships between the system variables across multiple objectives enables a full mapping of the optimal solution space. A multi-objective, hybrid optimal control algorithm is formulated using a multi-objective genetic algorithm as an outer loop systems optimizer around a global trajectory optimizer. The coupled problem is solved simultaneously to generate Pareto-optimal solutions in a single execution. The automated approach is demonstrated on two boulder return missions.

Analysis of process parameters in surface grinding using single objective Taguchi and multi-objective grey relational grade

Directory of Open Access Journals (Sweden)

Prashant J. Patil

2016-09-01

Full Text Available Close tolerance and good surface finish are achieved by means of grinding process. This study was carried out for multi-objective optimization of MQL grinding process parameters. Water based Al2O3 and CuO nanofluids of various concentrations are used as lubricant for MQL system. Grinding experiments were carried out on instrumented surface grinding machine. For experimentation purpose Taguchi's method was used. Important process parameters that affect the G ratio and surface finish in MQL grinding are depth of cut, type of lubricant, feed rate, grinding wheel speed, coolant flow rate, and nanoparticle size. Grinding performance was calculated by the measurement G ratio and surface finish. For improvement of grinding process a multi-objective process parameter optimization is performed by use of Taguchi based grey relational analysis. To identify most significant factor of process analysis of variance (ANOVA has been used.

Multi-objective optimization of p-xylene oxidation process using an improved self-adaptive differential evolution algorithm

Institute of Scientific and Technical Information of China (English)

Lili Tao; Bin Xu; Zhihua Hu; Weimin Zhong

2017-01-01

The rise in the use of global polyester fiber contributed to strong demand of the Terephthalic acid (TPA). The liquid-phase catalytic oxidation of p-xylene (PX) to TPA is regarded as a critical and efficient chemical process in industry [1]. PX oxidation reaction involves many complex side reactions, among which acetic acid combustion and PX combustion are the most important. As the target product of this oxidation process, the quality and yield of TPA are of great concern. However, the improvement of the qualified product yield can bring about the high energy consumption, which means that the economic objectives of this process cannot be achieved simulta-neously because the two objectives are in conflict with each other. In this paper, an improved self-adaptive multi-objective differential evolution algorithm was proposed to handle the multi-objective optimization prob-lems. The immune concept is introduced to the self-adaptive multi-objective differential evolution algorithm (SADE) to strengthen the local search ability and optimization accuracy. The proposed algorithm is successfully tested on several benchmark test problems, and the performance measures such as convergence and divergence metrics are calculated. Subsequently, the multi-objective optimization of an industrial PX oxidation process is carried out using the proposed immune self-adaptive multi-objective differential evolution algorithm (ISADE). Optimization results indicate that application of ISADE can greatly improve the yield of TPA with low combustion loss without degenerating TA quality.

Dual-mode nested search method for categorical uncertain multi-objective optimization

Science.gov (United States)

Tang, Long; Wang, Hu

2016-10-01

Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k-cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.

Constrained multi-objective optimization of storage ring lattices

Science.gov (United States)

Husain, Riyasat; Ghodke, A. D.

2018-03-01

The storage ring lattice optimization is a class of constrained multi-objective optimization problem, where in addition to low beam emittance, a large dynamic aperture for good injection efficiency and improved beam lifetime are also desirable. The convergence and computation times are of great concern for the optimization algorithms, as various objectives are to be optimized and a number of accelerator parameters to be varied over a large span with several constraints. In this paper, a study of storage ring lattice optimization using differential evolution is presented. The optimization results are compared with two most widely used optimization techniques in accelerators-genetic algorithm and particle swarm optimization. It is found that the differential evolution produces a better Pareto optimal front in reasonable computation time between two conflicting objectives-beam emittance and dispersion function in the straight section. The differential evolution was used, extensively, for the optimization of linear and nonlinear lattices of Indus-2 for exploring various operational modes within the magnet power supply capabilities.

Multi-objective trajectory optimization of Space Manoeuvre Vehicle using adaptive differential evolution and modified game theory

Science.gov (United States)

Chai, Runqi; Savvaris, Al; Tsourdos, Antonios; Chai, Senchun

2017-07-01

Highly constrained trajectory optimization for Space Manoeuvre Vehicles (SMV) is a challenging problem. In practice, this problem becomes more difficult when multiple mission requirements are taken into account. Because of the nonlinearity in the dynamic model and even the objectives, it is usually hard for designers to generate a compromised trajectory without violating strict path and box constraints. In this paper, a new multi-objective SMV optimal control model is formulated and parameterized using combined shooting-collocation technique. A modified game theory approach, coupled with an adaptive differential evolution algorithm, is designed in order to generate the pareto front of the multi-objective trajectory optimization problem. In addition, to improve the quality of obtained solutions, a control logic is embedded in the framework of the proposed approach. Several existing multi-objective evolutionary algorithms are studied and compared with the proposed method. Simulation results indicate that without driving the solution out of the feasible region, the proposed method can perform better in terms of convergence ability and convergence speed than its counterparts. Moreover, the quality of the pareto set generated using the proposed method is higher than other multi-objective evolutionary algorithms, which means the newly proposed algorithm is more attractive for solving multi-criteria SMV trajectory planning problem.

Optimization of Fuel Consumption and Emissions for Auxiliary Power Unit Based on Multi-Objective Optimization Model

Directory of Open Access Journals (Sweden)

Yongpeng Shen

2016-02-01

Full Text Available Auxiliary power units (APUs are widely used for electric power generation in various types of electric vehicles, improvements in fuel economy and emissions of these vehicles directly depend on the operating point of the APUs. In order to balance the conflicting goals of fuel consumption and emissions reduction in the process of operating point choice, the APU operating point optimization problem is formulated as a constrained multi-objective optimization problem (CMOP firstly. The four competing objectives of this CMOP are fuel-electricity conversion cost, hydrocarbon (HC emissions, carbon monoxide (CO emissions and nitric oxide (NO x emissions. Then, the multi-objective particle swarm optimization (MOPSO algorithm and weighted metric decision making method are employed to solve the APU operating point multi-objective optimization model. Finally, bench experiments under New European driving cycle (NEDC, Federal test procedure (FTP and high way fuel economy test (HWFET driving cycles show that, compared with the results of the traditional fuel consumption single-objective optimization approach, the proposed multi-objective optimization approach shows significant improvements in emissions performance, at the expense of a slight drop in fuel efficiency.

Scheduling for the National Hockey League Using a Multi-objective Evolutionary Algorithm

Science.gov (United States)

Craig, Sam; While, Lyndon; Barone, Luigi

We describe a multi-objective evolutionary algorithm that derives schedules for the National Hockey League according to three objectives: minimising the teams' total travel, promoting equity in rest time between games, and minimising long streaks of home or away games. Experiments show that the system is able to derive schedules that beat the 2008-9 NHL schedule in all objectives simultaneously, and that it returns a set of schedules that offer a range of trade-offs across the objectives.

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

Directory of Open Access Journals (Sweden)

Syed Ali Abbas Kazmi

2017-02-01

Full Text Available Distribution networks (DNWs are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN needs to consider choices, primarily distributed generation (DG, network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs based on mult-objective optimizations (MOOs in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at

Calibrated infrared ground/air radiometric spectrometer

Science.gov (United States)

Silk, J. K.; Schildkraut, Elliot Robert; Bauldree, Russell S.; Goodrich, Shawn M.

1996-06-01

The calibrated infrared ground/air radiometric spectrometer (CIGARS) is a new high performance, multi-purpose, multi- platform Fourier transform spectrometer (FPS) sensor. It covers the waveband from 0.2 to 12 micrometer, has spectral resolution as fine as 0.3 cm-1, and records over 100 spectra per second. Two CIGARS units are being used for observations of target signatures in the air or on the ground from fixed or moving platforms, including high performance jet aircraft. In this paper we describe the characteristics and capabilities of the CIGARS sensor, which uses four interchangeable detector modules (Si, InGaAs, InSb, and HgCdTe) and two optics modules, with internal calibration. The data recording electronics support observations of transient events, even without precise information on the timing of the event. We present test and calibration data on the sensitivity, spectral resolution, stability, and spectral rate of CIGARS, and examples of in- flight observations of real targets. We also discuss plans for adapting CIGARS for imaging spectroscopy observations, with simultaneous spectral and spatial data, by replacing the existing detectors with a focal plane array (FPA).

Distributed Generation Planning using Peer Enhanced Multi-objective Teaching-Learning based Optimization in Distribution Networks

Science.gov (United States)

Selvam, Kayalvizhi; Vinod Kumar, D. M.; Siripuram, Ramakanth

2017-04-01

In this paper, an optimization technique called peer enhanced teaching learning based optimization (PeTLBO) algorithm is used in multi-objective problem domain. The PeTLBO algorithm is parameter less so it reduced the computational burden. The proposed peer enhanced multi-objective based TLBO (PeMOTLBO) algorithm has been utilized to find a set of non-dominated optimal solutions [distributed generation (DG) location and sizing in distribution network]. The objectives considered are: real power loss and the voltage deviation subjected to voltage limits and maximum penetration level of DG in distribution network. Since the DG considered is capable of injecting real and reactive power to the distribution network the power factor is considered as 0.85 lead. The proposed peer enhanced multi-objective optimization technique provides different trade-off solutions in order to find the best compromise solution a fuzzy set theory approach has been used. The effectiveness of this proposed PeMOTLBO is tested on IEEE 33-bus and Indian 85-bus distribution system. The performance is validated with Pareto fronts and two performance metrics (C-metric and S-metric) by comparing with robust multi-objective technique called non-dominated sorting genetic algorithm-II and also with the basic TLBO.

Short-term economic environmental hydrothermal scheduling using improved multi-objective gravitational search algorithm

International Nuclear Information System (INIS)

Li, Chunlong; Zhou, Jianzhong; Lu, Peng; Wang, Chao

2015-01-01

Highlights: • Improved multi-objective gravitational search algorithm. • An elite archive set is proposed to guide evolutionary process. • Neighborhood searching mechanism to improve local search ability. • Adopt chaotic mutation for avoiding premature convergence. • Propose feasible space method to handle hydro plant constrains. - Abstract: With growing concerns about energy and environment, short-term economic environmental hydrothermal scheduling (SEEHS) plays a more and more important role in power system. Because of the two objectives and various constraints, SEEHS is a complex multi-objective optimization problem (MOOP). In order to solve the problem, we propose an improved multi-objective gravitational search algorithm (IMOGSA) in this paper. In IMOGSA, the mass of the agent is redefined by multiple objectives to make it suitable for MOOP. An elite archive set is proposed to keep Pareto optimal solutions and guide evolutionary process. For balancing exploration and exploitation, a neighborhood searching mechanism is presented to cooperate with chaotic mutation. Moreover, a novel method based on feasible space is proposed to handle hydro plant constraints during SEEHS, and a violation adjustment method is adopted to handle power balance constraint. For verifying its effectiveness, the proposed IMOGSA is applied to a hydrothermal system in two different case studies. The simulation results show that IMOGSA has a competitive performance in SEEHS when compared with other established algorithms

Multi-objective optimization of coal-fired power plants using differential evolution

International Nuclear Information System (INIS)

Wang, Ligang; Yang, Yongping; Dong, Changqing; Morosuk, Tatiana; Tsatsaronis, George

2014-01-01

Highlights: • Multi-objective optimization of large-scale coal-fired power plants using differential evolution. • A newly-proposed algorithm for searching the fronts of decision space in a single run. • A reduction of cost of electricity by 2–4% with an optimal efficiency increase up to 2% points. • The uncertainty comes mainly from temperature- and reheat-related cost factors of steam generator. • An exergoeconomic analysis and comparison between optimal designs and one real industrial design. - Abstract: The design trade-offs between thermodynamics and economics for thermal systems can be studied with the aid of multi-objective optimization techniques. The investment costs usually increase with increasing thermodynamic performance of a system. In this paper, an enhanced differential evolution with diversity-preserving and density-adjusting mechanisms, and a newly-proposed algorithm for searching the decision space frontier in a single run were used, to conduct the multi-objective optimization of large-scale, supercritical coal-fired plants. The uncertainties associated with cost functions were discussed by analyzing the sensitivity of the decision space frontier to some significant parameters involved in cost functions. Comparisons made with the aid of an exergoeconomic analysis between the cost minimum designs and a real industrial design demonstrated how the plant improvement was achieved. It is concluded that the cost of electricity could be reduced by a 2–4%, whereas the efficiency could be increased by up to two percentage points. The largest uncertainty is introduced by the temperature-related and reheat-related cost coefficients of the steam generator. More reliable data on the price prediction of future advanced materials should be used to obtain more accurate fronts of the objective space

Multi-objective decision-making under uncertainty: Fuzzy logic methods

Science.gov (United States)

Hardy, Terry L.

1995-01-01

Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.

NSGA-II algorithm for multi-objective generation expansion planning problem

Energy Technology Data Exchange (ETDEWEB)

Murugan, P.; Kannan, S. [Electronics and Communication Engineering Department, Arulmigu Kalasalingam College of Engineering, Krishnankoil 626190, Tamilnadu (India); Baskar, S. [Electrical Engineering Department, Thiagarajar College of Engineering, Madurai 625015, Tamilnadu (India)

2009-04-15

This paper presents an application of Elitist Non-dominated Sorting Genetic Algorithm version II (NSGA-II), to multi-objective generation expansion planning (GEP) problem. The GEP problem is considered as a two-objective problem. The first objective is the minimization of investment cost and the second objective is the minimization of outage cost (or maximization of reliability). To improve the performance of NSGA-II, two modifications are proposed. One modification is incorporation of Virtual Mapping Procedure (VMP), and the other is introduction of controlled elitism in NSGA-II. A synthetic test system having 5 types of candidate units is considered here for GEP for a 6-year planning horizon. The effectiveness of the proposed modifications is illustrated in detail. (author)

Comparative Study of Evolutionary Multi-objective Optimization Algorithms for a Non-linear Greenhouse Climate Control Problem

DEFF Research Database (Denmark)

Ghoreishi, Newsha; Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard

2015-01-01

Non-trivial real world decision-making processes usually involve multiple parties having potentially conflicting interests over a set of issues. State-of-the-art multi-objective evolutionary algorithms (MOEA) are well known to solve this class of complex real-world problems. In this paper, we...... compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance...... of all aforementioned algorithms is assessed and compared using performance indicators to evaluate proximity, diversity and consistency. Our insights to this comparative study enhanced our understanding of MOEAs performance in order to solve a non-linear complex climate control problem. The empirical...

Efficient solution of a multi objective fuzzy transportation problem

Science.gov (United States)

Vidhya, V.; Ganesan, K.

2018-04-01

In this paper we present a methodology for the solution of multi-objective fuzzy transportation problem when all the cost and time coefficients are trapezoidal fuzzy numbers and the supply and demand are crisp numbers. Using a new fuzzy arithmetic on parametric form of trapezoidal fuzzy numbers and a new ranking method all efficient solutions are obtained. The proposed method is illustrated with an example.

Energy thermal management in commercial bread-baking using a multi-objective optimisation framework

International Nuclear Information System (INIS)

Khatir, Zinedine; Taherkhani, A.R.; Paton, Joe; Thompson, Harvey; Kapur, Nik; Toropov, Vassili

2015-01-01

In response to increasing energy costs and legislative requirements energy efficient high-speed air impingement jet baking systems are now being developed. In this paper, a multi-objective optimisation framework for oven designs is presented which uses experimentally verified heat transfer correlations and high fidelity Computational Fluid Dynamics (CFD) analyses to identify optimal combinations of design features which maximise desirable characteristics such as temperature uniformity in the oven and overall energy efficiency of baking. A surrogate-assisted multi-objective optimisation framework is proposed and used to explore a range of practical oven designs, providing information on overall temperature uniformity within the oven together with ensuing energy usage and potential savings. - Highlights: • A multi-objective optimisation framework to design commercial ovens is presented. • High fidelity CFD embeds experimentally calibrated heat transfer inputs. • The optimum oven design minimises specific energy and bake time. • The Pareto front outlining the surrogate-assisted optimisation framework is built. • Optimisation of industrial bread-baking ovens reveals an energy saving of 637.6 GWh

A study of the LCA based biofuel supply chain multi-objective optimization model with multi-conversion paths in China

International Nuclear Information System (INIS)

Liu, Zhexuan; Qiu, Tong; Chen, Bingzhen

2014-01-01

Highlights: • A LCA based biofuel supply chain model considering 3E criteria was proposed. • The model was used to design a supply chain considering three conversion pathways. • An experimental biofuel supply chain for China was designed. • A Pareto-optimal solution surface of this multi-objective problem was obtained. • The designed supply chain was rather robust to price variation. - Abstract: In this paper we present a life cycle assessment (LCA) based biofuel supply chain model with multi-conversion pathways. This model was formulated as a mixed integer linear programming (MILP) problem which took economic, energy, and environmental criteria (3E) into consideration. The economic objective was measured by the total annual profit. The energy objective was measured by using the average fossil energy input per megajoule (MJ) of biofuel. The environmental objective was measured by greenhouse gas (GHG) emissions per MJ of biofuel. After carefully consideration of the current situation in China, we chose to examine three conversion pathways: bio-ethanol (BE), bio-methanol (BM) and bio-diesel (BD). LCA was integrated to a multi-objective supply chain model by dividing each pathway into several individual parts and analyzing each part. The multi-objective MILP problem was solved using a ε-constraint method by defining the total annual profit as the optimization objective and assigning the average fossil energy input per MJ biofuel and GHG emissions per MJ biofuel as constraints. This model was then used to design an experimental biofuel supply chain for China. A surface of the Pareto optimal solutions was obtained by linear interpolation of the non-inferior solutions. The optimal results included the choice of optimal conversion pathway, biomass type, biomass locations, facility locations, and network topology structure in the biofuel supply chain. Distributed and centralized systems were also factored into our experimental system design. In addition, the

A multi-objective optimization problem for multi-state series-parallel systems: A two-stage flow-shop manufacturing system

International Nuclear Information System (INIS)

Azadeh, A.; Maleki Shoja, B.; Ghanei, S.; Sheikhalishahi, M.

2015-01-01

This research investigates a redundancy-scheduling optimization problem for a multi-state series parallel system. The system is a flow shop manufacturing system with multi-state machines. Each manufacturing machine may have different performance rates including perfect performance, decreased performance and complete failure. Moreover, warm standby redundancy is considered for the redundancy allocation problem. Three objectives are considered for the problem: (1) minimizing system purchasing cost, (2) minimizing makespan, and (3) maximizing system reliability. Universal generating function is employed to evaluate system performance and overall reliability of the system. Since the problem is in the NP-hard class of combinatorial problems, genetic algorithm (GA) is used to find optimal/near optimal solutions. Different test problems are generated to evaluate the effectiveness and efficiency of proposed approach and compared to simulated annealing optimization method. The results show the proposed approach is capable of finding optimal/near optimal solution within a very reasonable time. - Highlights: • A redundancy-scheduling optimization problem for a multi-state series parallel system. • A flow shop with multi-state machines and warm standby redundancy. • Objectives are to optimize system purchasing cost, makespan and reliability. • Different test problems are generated and evaluated by a unique genetic algorithm. • It locates optimal/near optimal solution within a very reasonable time

Multi-objective Optimization of Process Parameters in Friction Stir Welding

DEFF Research Database (Denmark)

Tutum, Cem Celal; Hattel, Jesper Henri

The objective of this paper is to investigate optimum process parameters in Friction Stir Welding (FSW) to minimize residual stresses in the work piece and maximize production efficiency meanwhile satisfying process specific constraints as well. More specifically, the choices of tool rotational...... speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2- dimensional sequentially coupled thermo...

Localized probability of improvement for kriging based multi-objective optimization

Science.gov (United States)

Li, Yinjiang; Xiao, Song; Barba, Paolo Di; Rotaru, Mihai; Sykulski, Jan K.

2017-12-01

The paper introduces a new approach to kriging based multi-objective optimization by utilizing a local probability of improvement as the infill sampling criterion and the nearest neighbor check to ensure diversification and uniform distribution of Pareto fronts. The proposed method is computationally fast and linearly scalable to higher dimensions.

Technology of solving multi-objective problems of control of systems with distributed parameters

Science.gov (United States)

Rapoport, E. Ya.; Pleshivtseva, Yu. E.

2017-07-01

A constructive technology of multi-objective optimization of control of distributed parameter plants is proposed. The technology is based on a single-criterion version in the form of the minimax convolution of normalized performance criteria. The approach under development is based on the transition to an equivalent form of the variational problem with constraints, with the problem solution being a priori Pareto-effective. Further procedures of preliminary parameterization of control actions and subsequent reduction to a special problem of semi-infinite programming make it possible to find the sought extremals with the use of their Chebyshev properties and fundamental laws of the subject domain. An example of multi-objective optimization of operation modes of an engineering thermophysics object is presented, which is of independent interest.

Enhanced Multi-Objective Optimization of Groundwater Monitoring Networks

DEFF Research Database (Denmark)

Bode, Felix; Binning, Philip John; Nowak, Wolfgang

Drinking-water well catchments include many sources for potential contaminations like gas stations or agriculture. Finding optimal positions of monitoring wells for such purposes is challenging because there are various parameters (and their uncertainties) that influence the reliability...... and optimality of any suggested monitoring location or monitoring network. The goal of this project is to develop and establish a concept to assess, design, and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: (1) a high...... be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, wrapped up within the framework of formal multi-objective optimization. In order to gain insight into the flow and transport physics...

Feature Selection using Multi-objective Genetic Algorith m: A Hybrid Approach

OpenAIRE

Ahuja, Jyoti; GJUST - Guru Jambheshwar University of Sciecne and Technology; Ratnoo, Saroj Dahiya; GJUST - Guru Jambheshwar University of Sciecne and Technology

2015-01-01

Feature selection is an important pre-processing task for building accurate and comprehensible classification models. Several researchers have applied filter, wrapper or hybrid approaches using genetic algorithms which are good candidates for optimization problems that involve large search spaces like in the case of feature selection. Moreover, feature selection is an inherently multi-objective problem with many competing objectives involving size, predictive power and redundancy of the featu...

Thermodynamic design of Stirling engine using multi-objective particle swarm optimization algorithm

International Nuclear Information System (INIS)

Duan, Chen; Wang, Xinggang; Shu, Shuiming; Jing, Changwei; Chang, Huawei

2014-01-01

Highlights: • An improved thermodynamic model taking into account irreversibility parameter was developed. • A multi-objective optimization method for designing Stirling engine was investigated. • Multi-objective particle swarm optimization algorithm was adopted in the area of Stirling engine for the first time. - Abstract: In the recent years, the interest in Stirling engine has remarkably increased due to its ability to use any heat source from outside including solar energy, fossil fuels and biomass. A large number of studies have been done on Stirling cycle analysis. In the present study, a mathematical model based on thermodynamic analysis of Stirling engine considering regenerative losses and internal irreversibilities has been developed. Power output, thermal efficiency and the cycle irreversibility parameter of Stirling engine are optimized simultaneously using Particle Swarm Optimization (PSO) algorithm, which is more effective than traditional genetic algorithms. In this optimization problem, some important parameters of Stirling engine are considered as decision variables, such as temperatures of the working fluid both in the high temperature isothermal process and in the low temperature isothermal process, dead volume ratios of each heat exchanger, volumes of each working spaces, effectiveness of the regenerator, and the system charge pressure. The Pareto optimal frontier is obtained and the final design solution has been selected by Linear Programming Technique for Multidimensional Analysis of Preference (LINMAP). Results show that the proposed multi-objective optimization approach can significantly outperform traditional single objective approaches

Multi-objective optimal strategy for generating and bidding in the power market

International Nuclear Information System (INIS)

Peng Chunhua; Sun Huijuan; Guo Jianfeng; Liu Gang

2012-01-01

Highlights: ► A new benefit/risk/emission comprehensive generation optimization model is established. ► A hybrid multi-objective differential evolution optimization algorithm is designed. ► Fuzzy set theory and entropy weighting method are employed to extract the general best solution. ► The proposed approach of generating and bidding is efficient for maximizing profit and minimizing both risk and emissions. - Abstract: Based on the coordinated interaction between units output and electricity market prices, the benefit/risk/emission comprehensive generation optimization model with objectives of maximal profit and minimal bidding risk and emissions is established. A hybrid multi-objective differential evolution optimization algorithm, which successfully integrates Pareto non-dominated sorting with differential evolution algorithm and improves individual crowding distance mechanism and mutation strategy to avoid premature and unevenly search, is designed to achieve Pareto optimal set of this model. Moreover, fuzzy set theory and entropy weighting method are employed to extract one of the Pareto optimal solutions as the general best solution. Several optimization runs have been carried out on different cases of generation bidding and scheduling. The results confirm the potential and effectiveness of the proposed approach in solving the multi-objective optimization problem of generation bidding and scheduling. In addition, the comparison with the classical optimization algorithms demonstrates the superiorities of the proposed algorithm such as integrality of Pareto front, well-distributed Pareto-optimal solutions, high search speed.

Multi-objective superstructure-free synthesis and optimization of thermal power plants

International Nuclear Information System (INIS)

Wang, Ligang; Lampe, Matthias; Voll, Philip; Yang, Yongping; Bardow, André

2016-01-01

The merits of superstructure-free synthesis are demonstrated for bi-objective design of thermal power plants. The design of thermal power plants is complex and thus best solved by optimization. Common optimization methods require specification of a superstructure which becomes a tedious and error-prone task for complex systems. Superstructure specification is avoided by the presented superstructure-free approach, which is shown to successfully solve the design task yielding a high-quality Pareto front of promising structural alternatives. The economic objective function avoids introducing infinite numbers of units (e.g., turbine, reheater and feedwater preheater) as favored by pure thermodynamic optimization. The number of feasible solutions found per number of mutation tries is still high even after many generations but declines after introducing highly-nonlinear cost functions leading to challenging MINLP problems. The identified Pareto-optimal solutions tend to employ more units than found in modern power plants indicating the need for cost functions to reflect current industrial practice. In summary, the multi-objective superstructure-free synthesis framework is a robust approach for very complex problems in the synthesis of thermal power plants. - Highlights: • A generalized multi-objective superstructure-free synthesis framework for thermal power plants is presented. • The superstructure-free synthesis framework is comprehensively evaluated by complex bi-objective synthesis problems. • The proposed framework is effective to explore the structural design space even for complex problems.

Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification.

Science.gov (United States)

Zhang, Yong; Gong, Dun-Wei; Cheng, Jian

2017-01-01

Feature selection is an important data-preprocessing technique in classification problems such as bioinformatics and signal processing. Generally, there are some situations where a user is interested in not only maximizing the classification performance but also minimizing the cost that may be associated with features. This kind of problem is called cost-based feature selection. However, most existing feature selection approaches treat this task as a single-objective optimization problem. This paper presents the first study of multi-objective particle swarm optimization (PSO) for cost-based feature selection problems. The task of this paper is to generate a Pareto front of nondominated solutions, that is, feature subsets, to meet different requirements of decision-makers in real-world applications. In order to enhance the search capability of the proposed algorithm, a probability-based encoding technology and an effective hybrid operator, together with the ideas of the crowding distance, the external archive, and the Pareto domination relationship, are applied to PSO. The proposed PSO-based multi-objective feature selection algorithm is compared with several multi-objective feature selection algorithms on five benchmark datasets. Experimental results show that the proposed algorithm can automatically evolve a set of nondominated solutions, and it is a highly competitive feature selection method for solving cost-based feature selection problems.

A Pareto-based multi-objective optimization algorithm to design energy-efficient shading devices

International Nuclear Information System (INIS)

Khoroshiltseva, Marina; Slanzi, Debora; Poli, Irene

2016-01-01

Highlights: • We present a multi-objective optimization algorithm for shading design. • We combine Harmony search and Pareto-based procedures. • Thermal and daylighting performances of external shading were considered. • We applied the optimization process to a residential social housing in Madrid. - Abstract: In this paper we address the problem of designing new energy-efficient static daylight devices that will surround the external windows of a residential building in Madrid. Shading devices can in fact largely influence solar gains in a building and improve thermal and lighting comforts by selectively intercepting the solar radiation and by reducing the undesirable glare. A proper shading device can therefore significantly increase the thermal performance of a building by reducing its energy demand in different climate conditions. In order to identify the set of optimal shading devices that allow a low energy consumption of the dwelling while maintaining high levels of thermal and lighting comfort for the inhabitants we derive a multi-objective optimization methodology based on Harmony Search and Pareto front approaches. The results show that the multi-objective approach here proposed is an effective procedure in designing energy efficient shading devices when a large set of conflicting objectives characterizes the performance of the proposed solutions.

Pareto-Optimal Multi-objective Inversion of Geophysical Data

Science.gov (United States)

Schnaidt, Sebastian; Conway, Dennis; Krieger, Lars; Heinson, Graham

2018-01-01

In the process of modelling geophysical properties, jointly inverting different data sets can greatly improve model results, provided that the data sets are compatible, i.e., sensitive to similar features. Such a joint inversion requires a relationship between the different data sets, which can either be analytic or structural. Classically, the joint problem is expressed as a scalar objective function that combines the misfit functions of multiple data sets and a joint term which accounts for the assumed connection between the data sets. This approach suffers from two major disadvantages: first, it can be difficult to assess the compatibility of the data sets and second, the aggregation of misfit terms introduces a weighting of the data sets. We present a pareto-optimal multi-objective joint inversion approach based on an existing genetic algorithm. The algorithm treats each data set as a separate objective, avoiding forced weighting and generating curves of the trade-off between the different objectives. These curves are analysed by their shape and evolution to evaluate data set compatibility. Furthermore, the statistical analysis of the generated solution population provides valuable estimates of model uncertainty.

Multi-objective reliability optimization of series-parallel systems with a choice of redundancy strategies

International Nuclear Information System (INIS)

Safari, Jalal

2012-01-01

This paper proposes a variant of the Non-dominated Sorting Genetic Algorithm (NSGA-II) to solve a novel mathematical model for multi-objective redundancy allocation problems (MORAP). Most researchers about redundancy allocation problem (RAP) have focused on single objective optimization, while there has been some limited research which addresses multi-objective optimization. Also all mathematical multi-objective models of general RAP assume that the type of redundancy strategy for each subsystem is predetermined and known a priori. In general, active redundancy has traditionally received greater attention; however, in practice both active and cold-standby redundancies may be used within a particular system design. The choice of redundancy strategy then becomes an additional decision variable. Thus, the proposed model and solution method are to select the best redundancy strategy, type of components, and levels of redundancy for each subsystem that maximizes the system reliability and minimize total system cost under system-level constraints. This problem belongs to the NP-hard class. This paper presents a second-generation Multiple-Objective Evolutionary Algorithm (MOEA), named NSGA-II to find the best solution for the given problem. The proposed algorithm demonstrates the ability to identify a set of optimal solutions (Pareto front), which provides the Decision Maker (DM) with a complete picture of the optimal solution space. After finding the Pareto front, a procedure is used to select the best solution from the Pareto front. Finally, the advantages of the presented multi-objective model and of the proposed algorithm are illustrated by solving test problems taken from the literature and the robustness of the proposed NSGA-II is discussed.

Image de-noising based on mathematical morphology and multi-objective particle swarm optimization

Science.gov (United States)

Dou, Liyun; Xu, Dan; Chen, Hao; Liu, Yicheng

2017-07-01

To overcome the problem of image de-noising, an efficient image de-noising approach based on mathematical morphology and multi-objective particle swarm optimization (MOPSO) is proposed in this paper. Firstly, constructing a series and parallel compound morphology filter based on open-close (OC) operation and selecting a structural element with different sizes try best to eliminate all noise in a series link. Then, combining multi-objective particle swarm optimization (MOPSO) to solve the parameters setting of multiple structural element. Simulation result shows that our algorithm can achieve a superior performance compared with some traditional de-noising algorithm.

Multi-objective optimization of water quality, pumps operation, and storage sizing of water distribution systems.

Science.gov (United States)

Kurek, Wojciech; Ostfeld, Avi

2013-01-30

A multi-objective methodology utilizing the Strength Pareto Evolutionary Algorithm (SPEA2) linked to EPANET for trading-off pumping costs, water quality, and tanks sizing of water distribution systems is developed and demonstrated. The model integrates variable speed pumps for modeling the pumps operation, two water quality objectives (one based on chlorine disinfectant concentrations and one on water age), and tanks sizing cost which are assumed to vary with location and diameter. The water distribution system is subject to extended period simulations, variable energy tariffs, Kirchhoff's laws 1 and 2 for continuity of flow and pressure, tanks water level closure constraints, and storage-reliability requirements. EPANET Example 3 is employed for demonstrating the methodology on two multi-objective models, which differ in the imposed water quality objective (i.e., either with disinfectant or water age considerations). Three-fold Pareto optimal fronts are presented. Sensitivity analysis on the storage-reliability constraint, its influence on pumping cost, water quality, and tank sizing are explored. The contribution of this study is in tailoring design (tank sizing), pumps operational costs, water quality of two types, and reliability through residual storage requirements, in a single multi-objective framework. The model was found to be stable in generating multi-objective three-fold Pareto fronts, while producing explainable engineering outcomes. The model can be used as a decision tool for both pumps operation, water quality, required storage for reliability considerations, and tank sizing decision-making. Copyright © 2012 Elsevier Ltd. All rights reserved.

The HISS spectrometer at LBL

International Nuclear Information System (INIS)

Greiner, D.

1981-01-01

The Heavy Ion Spectrometer System at LBL is designed to be a general purpose experimental work bench able to support a wide variety of experiments. Our philosophy is to provide instruments capable of investigating, with multi-particle sensitivity, a large portion of phase space. We have not chosen a particular region such as mid-rapidity or projectile frame, but instead, have made sure that the magnet and the instrumentation allow these choices as well as many others. (orig.)

Multi-period multi-objective electricity generation expansion planning problem with Monte-Carlo simulation

Energy Technology Data Exchange (ETDEWEB)

Tekiner, Hatice [Industrial Engineering, College of Engineering and Natural Sciences, Istanbul Sehir University, 2 Ahmet Bayman Rd, Istanbul (Turkey); Coit, David W. [Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Rd., Piscataway, NJ (United States); Felder, Frank A. [Edward J. Bloustein School of Planning and Public Policy, Rutgers University, Piscataway, NJ (United States)

2010-12-15

A new approach to the electricity generation expansion problem is proposed to minimize simultaneously multiple objectives, such as cost and air emissions, including CO{sub 2} and NO{sub x}, over a long term planning horizon. In this problem, system expansion decisions are made to select the type of power generation, such as coal, nuclear, wind, etc., where the new generation asset should be located, and at which time period expansion should take place. We are able to find a Pareto front for the multi-objective generation expansion planning problem that explicitly considers availability of the system components over the planning horizon and operational dispatching decisions. Monte-Carlo simulation is used to generate numerous scenarios based on the component availabilities and anticipated demand for energy. The problem is then formulated as a mixed integer linear program, and optimal solutions are found based on the simulated scenarios with a combined objective function considering the multiple problem objectives. The different objectives are combined using dimensionless weights and a Pareto front can be determined by varying these weights. The mathematical model is demonstrated on an example problem with interesting results indicating how expansion decisions vary depending on whether minimizing cost or minimizing greenhouse gas emissions or pollutants is given higher priority. (author)

Multi-Objective Optimization Considering Battery Degradation for a Multi-Mode Power-Split Electric Vehicle

Directory of Open Access Journals (Sweden)

Xuerui Ma

2017-07-01

Full Text Available A multi-mode power-split (MMPS hybrid electric vehicle (HEV has two planetary gearsets and clutches/grounds which results in several operation modes with enhanced electric drive capability and better fuel economy. Basically, the battery storage system is involved in different operation modes to satisfy the power demand and minimize the fuel consumption, whereas the complicated operation modes with frequent charging/discharging will absolutely influence the battery life because of degradation. In this paper, firstly, we introduce the solid electrolyte interface (SEI film growth model based on the previous study of the battery degradation principles and was verified according to the test data. We consider both the fuel economy and battery degradation as a multi-objective problem for MMPS HEV by normalization with a weighting factor. An instantaneous optimization is implemented based on the equivalent fuel consumption concept. Then the control strategy is implemented on a simulation framework integrating the MMPS powertrain model and the SEI film growth map model over some typical driving cycles, such as New European Driving Cycle (NEDC and Urban Dynamometer Driving Schedule (UDDS. Finally, the result demonstrates that these two objectives are conflicting and the trade-off reduces the battery degradation with fuel sacrifice. Additionally, the analysis reveals how the mode selection will reflect the battery degradation.

HISS spectrometer at LBL

International Nuclear Information System (INIS)

Greiner, D.

1980-11-01

The Heavy Ion Spectrometer System at LBL is designed to be a general purpose experimental work bench able to support a wide variety of experiments. Our philosophy is to provide instruments capable of investigating, with multi-particle sensitivity, a large portion of phase space. We have not chosen a particular region such as mid-rapidity or projectile frame but, instead, have made sure that the magnet and the instrumentation allow these choices as well as many others. The beam can be brought into the magnet at a variable position and the magnet can be rotated

Multi-Objective Sensitivity Analyses for Power Generation Mix: Malaysia Case Study

OpenAIRE

Siti Mariam Mohd Shokri; Nofri Yenita Dahlan; Hasmaini Mohamad

2017-01-01

This paper presents an optimization framework to determine long-term optimal generation mix for Malaysia Power Sector using Dynamic Programming (DP) technique. Several new candidate units with a pre-defined MW capacity were included in the model for generation expansion planning from coal, natural gas, hydro and renewable energy (RE). Four objective cases were considered, 1) economic cost, 2) environmental, 3) reliability and 4) multi-objectives that combining the three cases. Results show th...

Multi-objective optimization and exergetic-sustainability of an irreversible nano scale Braysson cycle operating with Ma

Directory of Open Access Journals (Sweden)

Mohammad H. Ahmadi

2016-06-01

Full Text Available Nano technology is developed in this decade and changes the way of life. Moreover, developing nano technology has effect on the performance of the materials and consequently improves the efficiency and robustness of them. So, nano scale thermal cycles will be probably engaged in the near future. In this paper, a nano scale irreversible Braysson cycle is studied thermodynamically for optimizing the performance of the Braysson cycle. In the aforementioned cycle an ideal Maxwell–Boltzmann gas is used as a working fluid. Furthermore, three different plans are used for optimizing with multi-objectives; though, the outputs of the abovementioned plans are assessed autonomously. Throughout the first plan, with the purpose of maximizing the ecological coefficient of performance and energy efficiency of the system, multi-objective optimization algorithms are used. Furthermore, in the second plan, two objective functions containing the ecological coefficient of performance and the dimensionless Maximum available work are maximized synchronously by utilizing multi-objective optimization approach. Finally, throughout the third plan, three objective functions involving the dimensionless Maximum available work, the ecological coefficient of performance and energy efficiency of the system are maximized synchronously by utilizing multi-objective optimization approach. The multi-objective evolutionary approach based on the non-dominated sorting genetic algorithm approach is used in this research. Making a decision is performed by three different decision makers comprising linear programming approaches for multidimensional analysis of preference and an approach for order of preference by comparison with ideal answer and Bellman–Zadeh. Lastly, analysis of error is employed to determine deviation of the outcomes gained from each plan.

Design of homo-organic acid producing strains using multi-objective optimization

DEFF Research Database (Denmark)

Kim, Tae Yong; Park, Jong Myoung; Kim, Hyun Uk

2015-01-01

Production of homo-organic acids without byproducts is an important challenge in bioprocess engineering to minimize operation cost for separation processes. In this study, we used multi-objective optimization to design Escherichia coli strains with the goals of maximally producing target organic ...

Fuzzy preference based interactive fuzzy physical programming and its application in multi-objective optimization

International Nuclear Information System (INIS)

Zhang, Xu; Huang, Hong Zhong; Yu, Lanfeng

2006-01-01

Interactive Fuzzy Physical Programming (IFPP) developed in this paper is a new efficient multi-objective optimization method, which retains the advantages of physical programming while considering the fuzziness of the designer's preferences. The fuzzy preference function is introduced based on the model of linear physical programming, which is used to guide the search for improved solutions by interactive decision analysis. The example of multi-objective optimization design of the spindle of internal grinder demonstrates that the improved preference conforms to the subjective desires of the designer

Multi objective optimization of line pack management of gas pipeline system

International Nuclear Information System (INIS)

Chebouba, A

2015-01-01

This paper addresses the Line Pack Management of the ''GZ1 Hassi R'mell-Arzew'' gas pipeline. For a gas pipeline system, the decision-making on the gas line pack management scenarios usually involves a delicate balance between minimization of the fuel consumption in the compression stations and maximizing gas line pack. In order to select an acceptable Line Pack Management of Gas Pipeline scenario from these two angles for ''GZ1 Hassi R'mell- Arzew'' gas pipeline, the idea of multi-objective decision-making has been introduced. The first step in developing this approach is the derivation of a numerical method to analyze the flow through the pipeline under transient isothermal conditions. In this paper, the solver NSGA-II of the modeFRONTIER, coupled with a matlab program was used for solving the multi-objective problem

Multi-Objective Bidding Strategy for Genco Using Non-Dominated Sorting Particle Swarm Optimization

Science.gov (United States)

Saksinchai, Apinat; Boonchuay, Chanwit; Ongsakul, Weerakorn

2010-06-01

This paper proposes a multi-objective bidding strategy for a generation company (GenCo) in uniform price spot market using non-dominated sorting particle swarm optimization (NSPSO). Instead of using a tradeoff technique, NSPSO is introduced to solve the multi-objective strategic bidding problem considering expected profit maximization and risk (profit variation) minimization. Monte Carlo simulation is employed to simulate rivals' bidding behavior. Test results indicate that the proposed approach can provide the efficient non-dominated solution front effectively. In addition, it can be used as a decision making tool for a GenCo compromising between expected profit and price risk in spot market.

Multi-objective optimization in the presence of practical constraints using non-dominated sorting hybrid cuckoo search algorithm

Directory of Open Access Journals (Sweden)

M. Balasubbareddy

2015-12-01

Full Text Available A novel optimization algorithm is proposed to solve single and multi-objective optimization problems with generation fuel cost, emission, and total power losses as objectives. The proposed method is a hybridization of the conventional cuckoo search algorithm and arithmetic crossover operations. Thus, the non-linear, non-convex objective function can be solved under practical constraints. The effectiveness of the proposed algorithm is analyzed for various cases to illustrate the effect of practical constraints on the objectives' optimization. Two and three objective multi-objective optimization problems are formulated and solved using the proposed non-dominated sorting-based hybrid cuckoo search algorithm. The effectiveness of the proposed method in confining the Pareto front solutions in the solution region is analyzed. The results for single and multi-objective optimization problems are physically interpreted on standard test functions as well as the IEEE-30 bus test system with supporting numerical and graphical results and also validated against existing methods.

Loading pattern optimization by multi-objective simulated annealing with screening technique

International Nuclear Information System (INIS)

Tong, K. P.; Hyun, C. L.; Hyung, K. J.; Chang, H. K.

2006-01-01

This paper presents a new multi-objective function which is made up of the main objective term as well as penalty terms related to the constraints. All the terms are represented in the same functional form and the coefficient of each term is normalized so that each term has equal weighting in the subsequent simulated annealing optimization calculations. The screening technique introduced in the previous work is also adopted in order to save computer time in 3-D neutronics evaluation of trial loading patterns. For numerical test of the new multi-objective function in the loading pattern optimization, the optimum loading patterns for the initial and the cycle 7 reload PWR core of Yonggwang Unit 4 are calculated by the simulated annealing algorithm with screening technique. A total of 10 optimum loading patterns are obtained for the initial core through 10 independent simulated annealing optimization runs. For the cycle 7 reload core one optimum loading pattern has been obtained from a single simulated annealing optimization run. More SA optimization runs will be conducted to optimum loading patterns for the cycle 7 reload core and results will be presented in the further work. (authors)

Multi-objective optimization in systematic conservation planning and the representation of genetic variability among populations.

Science.gov (United States)

Schlottfeldt, S; Walter, M E M T; Carvalho, A C P L F; Soares, T N; Telles, M P C; Loyola, R D; Diniz-Filho, J A F

2015-06-18

Biodiversity crises have led scientists to develop strategies for achieving conservation goals. The underlying principle of these strategies lies in systematic conservation planning (SCP), in which there are at least 2 conflicting objectives, making it a good candidate for multi-objective optimization. Although SCP is typically applied at the species level (or hierarchically higher), it can be used at lower hierarchical levels, such as using alleles as basic units for analysis, for conservation genetics. Here, we propose a method of SCP using a multi-objective approach. We used non-dominated sorting genetic algorithm II in order to identify the smallest set of local populations of Dipteryx alata (baru) (a Brazilian Cerrado species) for conservation, representing the known genetic diversity and using allele frequency information associated with heterozygosity and Hardy-Weinberg equilibrium. We worked in 3 variations for the problem. First, we reproduced a previous experiment, but using a multi-objective approach. We found that the smallest set of populations needed to represent all alleles under study was 7, corroborating the results of the previous study, but with more distinct solutions. In the 2nd and 3rd variations, we performed simultaneous optimization of 4 and 5 objectives, respectively. We found similar but refined results for 7 populations, and a larger portfolio considering intra-specific diversity and persistence with populations ranging from 8-22. This is the first study to apply multi-objective algorithms to an SCP problem using alleles at the population level as basic units for analysis.

Multi-objective optimization of an underwater compressed air energy storage system using genetic algorithm

International Nuclear Information System (INIS)

Cheung, Brian C.; Carriveau, Rupp; Ting, David S.K.

2014-01-01

This paper presents the findings from a multi-objective genetic algorithm optimization study on the design parameters of an underwater compressed air energy storage system (UWCAES). A 4 MWh UWCAES system was numerically simulated and its energy, exergy, and exergoeconomics were analysed. Optimal system configurations were determined that maximized the UWCAES system round-trip efficiency and operating profit, and minimized the cost rate of exergy destruction and capital expenditures. The optimal solutions obtained from the multi-objective optimization model formed a Pareto-optimal front, and a single preferred solution was selected using the pseudo-weight vector multi-criteria decision making approach. A sensitivity analysis was performed on interest rates to gauge its impact on preferred system designs. Results showed similar preferred system designs for all interest rates in the studied range. The round-trip efficiency and operating profit of the preferred system designs were approximately 68.5% and $53.5/cycle, respectively. The cost rate of the system increased with interest rates. - Highlights: • UWCAES system configurations were developed using multi-objective optimization. • System was optimized for energy efficiency, exergy, and exergoeconomics • Pareto-optimal solution surfaces were developed at different interest rates. • Similar preferred system configurations were found at all interest rates studied

Multi-objective PID Optimization for Speed Control of an Isolated Steam Turbine using Gentic Algorithm

OpenAIRE

Sanjay Kr. Singh; D. Boolchandani; S.G. Modani; Nitish Katal

2014-01-01

This study focuses on multi-objective optimization of the PID controllers for optimal speed control for an isolated steam turbine. In complex operations, optimal tuning plays an imperative role in maintaining the product quality and process safety. This study focuses on the comparison of the optimal PID tuning using Multi-objective Genetic Algorithm (NSGA-II) against normal genetic algorithm and Ziegler Nichols methods for the speed control of an isolated steam turbine. Isolated steam turbine...

MULTI-OBJECTIVE OPTIMISATION OF LASER CUTTING USING CUCKOO SEARCH ALGORITHM

Directory of Open Access Journals (Sweden)

M. MADIĆ

2015-03-01

Full Text Available Determining of optimal laser cutting conditions for improving cut quality characteristics is of great importance in process planning. This paper presents multi-objective optimisation of the CO2 laser cutting process considering three cut quality characteristics such as surface roughness, heat affected zone (HAZ and kerf width. It combines an experimental design by using Taguchi’s method, modelling the relationships between the laser cutting factors (laser power, cutting speed, assist gas pressure and focus position and cut quality characteristics by artificial neural networks (ANNs, formulation of the multiobjective optimisation problem using weighting sum method, and solving it by the novel meta-heuristic cuckoo search algorithm (CSA. The objective is to obtain optimal cutting conditions dependent on the importance order of the cut quality characteristics for each of four different case studies presented in this paper. The case studies considered in this study are: minimisation of cut quality characteristics with equal priority, minimisation of cut quality characteristics with priority given to surface roughness, minimisation of cut quality characteristics with priority given to HAZ, and minimisation of cut quality characteristics with priority given to kerf width. The results indicate that the applied CSA for solving the multi-objective optimisation problem is effective, and that the proposed approach can be used for selecting the optimal laser cutting factors for specific production requirements.

Multi-objective generation scheduling with hybrid energy resources

Science.gov (United States)

Trivedi, Manas

In economic dispatch (ED) of electric power generation, the committed generating units are scheduled to meet the load demand at minimum operating cost with satisfying all unit and system equality and inequality constraints. Generation of electricity from the fossil fuel releases several contaminants into the atmosphere. So the economic dispatch objective can no longer be considered alone due to the environmental concerns that arise from the emissions produced by fossil fueled electric power plants. This research is proposing the concept of environmental/economic generation scheduling with traditional and renewable energy sources. Environmental/economic dispatch (EED) is a multi-objective problem with conflicting objectives since emission minimization is conflicting with fuel cost minimization. Production and consumption of fossil fuel and nuclear energy are closely related to environmental degradation. This causes negative effects to human health and the quality of life. Depletion of the fossil fuel resources will also be challenging for the presently employed energy systems to cope with future energy requirements. On the other hand, renewable energy sources such as hydro and wind are abundant, inexhaustible and widely available. These sources use native resources and have the capacity to meet the present and the future energy demands of the world with almost nil emissions of air pollutants and greenhouse gases. The costs of fossil fuel and renewable energy are also heading in opposite directions. The economic policies needed to support the widespread and sustainable markets for renewable energy sources are rapidly evolving. The contribution of this research centers on solving the economic dispatch problem of a system with hybrid energy resources under environmental restrictions. It suggests an effective solution of renewable energy to the existing fossil fueled and nuclear electric utilities for the cheaper and cleaner production of electricity with hourly

A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

Science.gov (United States)

Moghaddam, Kamran S.; Usher, John S.

2011-07-01

In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.

"Slit Mask Design for the Giant Magellan Telescope Multi-object Astronomical and Cosmological Spectrograph"

Science.gov (United States)

Williams, Darius; Marshall, Jennifer L.; Schmidt, Luke M.; Prochaska, Travis; DePoy, Darren L.

2018-01-01

The Giant Magellan Telescope Multi-object Astronomical and Cosmological Spectrograph (GMACS) is currently in development for the Giant Magellan Telescope (GMT). GMACS will employ slit masks with a usable diameter of approximately 0.450 m for the purpose of multi-slit spectroscopy. Of significant importance are the design constraints and parameters of the multi-object slit masks themselves as well as the means for mapping astronomical targets to physical mask locations. Analytical methods are utilized to quantify deformation effects on a potential slit mask due to thermal expansion and vignetting of target light cones. Finite element analysis (FEA) is utilized to simulate mask flexure in changing gravity vectors. The alpha version of the mask creation program for GMACS, GMACS Mask Simulator (GMS), a derivative of the OSMOS Mask Simulator (OMS), is introduced.

An improved data acquisition system for isotopic ratio mass spectrometers

International Nuclear Information System (INIS)

Saha, T.K.; Reddy, B.; Nazare, C.K.; Handu, V.K.

1999-01-01

Isotopic ratio mass spectrometers designed and fabricated to measure the isotopic ratios with a precision of better than 0.05%. In order to achieve this precision, the measurement system consisting of ion signal to voltage converters, analog to digital converters, and data acquisition electronics should be at least one order better than the overall precision of measurement. Using state of the art components and techniques, a data acquisition system, which is an improved version of the earlier system, has been designed and developed for use with multi-collector isotopic ratio mass spectrometers

Processing Technology Selection for Municipal Sewage Treatment Based on a Multi-Objective Decision Model under Uncertainty.

Science.gov (United States)

Chen, Xudong; Xu, Zhongwen; Yao, Liming; Ma, Ning

2018-03-05

This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.

An Improved Multi-Objective Artificial Bee Colony Optimization Algorithm with Regulation Operators

Directory of Open Access Journals (Sweden)

Jiuyuan Huo

2017-02-01

Full Text Available To achieve effective and accurate optimization for multi-objective optimization problems, a multi-objective artificial bee colony algorithm with regulation operators (RMOABC inspired by the intelligent foraging behavior of honey bees was proposed in this paper. The proposed algorithm utilizes the Pareto dominance theory and takes advantage of adaptive grid and regulation operator mechanisms. The adaptive grid technique is used to adaptively assess the Pareto front maintained in an external archive and the regulation operator is used to balance the weights of the local search and the global search in the evolution of the algorithm. The performance of RMOABC was evaluated in comparison with other nature inspired algorithms includes NSGA-II and MOEA/D. The experiments results demonstrated that the RMOABC approach has better accuracy and minimal execution time.

A multi-objective chaotic particle swarm optimization for environmental/economic dispatch

International Nuclear Information System (INIS)

Cai Jiejin; Ma Xiaoqian; Li Qiong; Li Lixiang; Peng Haipeng

2009-01-01

A multi-objective chaotic particle swarm optimization (MOCPSO) method has been developed to solve the environmental/economic dipatch (EED) problems considering both economic and environmental issues. The proposed MOCPSO method has been applied in two test power systems. Compared with the conventional multi-objective particle swarm optimization (MOPSO) method, for the compromising minimum fuel cost and emission case, the fuel cost and pollutant emission obtained from MOCPSO method can be reduced about 50.08 $/h and 2.95 kg/h, respectively, in test system 1, about 0.02 $/h and 1.11 kg/h, respectively, in test system 2. The MOCPSO method also results in higher quality solutions for the minimum fuel cost case and the minimum emission case in both of the test power systems. Hence, MOCPSO method can result in great environmental and economic effects. For EED problems, the MOCPSO method is more feasible and more effective alternative approach than the conventional MOPSO method.

Optimal unit sizing for small-scale integrated energy systems using multi-objective interval optimization and evidential reasoning approach

International Nuclear Information System (INIS)

Wei, F.; Wu, Q.H.; Jing, Z.X.; Chen, J.J.; Zhou, X.X.

2016-01-01

This paper proposes a comprehensive framework including a multi-objective interval optimization model and evidential reasoning (ER) approach to solve the unit sizing problem of small-scale integrated energy systems, with uncertain wind and solar energies integrated. In the multi-objective interval optimization model, interval variables are introduced to tackle the uncertainties of the optimization problem. Aiming at simultaneously considering the cost and risk of a business investment, the average and deviation of life cycle cost (LCC) of the integrated energy system are formulated. In order to solve the problem, a novel multi-objective optimization algorithm, MGSOACC (multi-objective group search optimizer with adaptive covariance matrix and chaotic search), is developed, employing adaptive covariance matrix to make the search strategy adaptive and applying chaotic search to maintain the diversity of group. Furthermore, ER approach is applied to deal with multiple interests of an investor at the business decision making stage and to determine the final unit sizing solution from the Pareto-optimal solutions. This paper reports on the simulation results obtained using a small-scale direct district heating system (DH) and a small-scale district heating and cooling system (DHC) optimized by the proposed framework. The results demonstrate the superiority of the multi-objective interval optimization model and ER approach in tackling the unit sizing problem of integrated energy systems considering the integration of uncertian wind and solar energies. - Highlights: • Cost and risk of investment in small-scale integrated energy systems are considered. • A multi-objective interval optimization model is presented. • A novel multi-objective optimization algorithm (MGSOACC) is proposed. • The evidential reasoning (ER) approach is used to obtain the final optimal solution. • The MGSOACC and ER can tackle the unit sizing problem efficiently.

Initiative Optimization Operation Strategy and Multi-objective Energy Management Method for Combined Cooling Heating and Power

Institute of Scientific and Technical Information of China (English)

Feng Zhao; Chenghui Zhang; Bo Sun

2016-01-01

This paper proposed an initiative optimization operation strategy and multi-objective energy management method for combined cooling heating and power(CCHP) with storage systems.Initially,the initiative optimization operation strategy of CCHP system in the cooling season,the heating season and the transition season was formulated.The energy management of CCHP system was optimized by the multi-objective optimization model with maximum daily energy efficiency,minimum daily carbon emissions and minimum daily operation cost based on the proposed initiative optimization operation strategy.Furthermore,the pareto optimal solution set was solved by using the niche particle swarm multi-objective optimization algorithm.Ultimately,the most satisfactory energy management scheme was obtained by using the technique for order preference by similarity to ideal solution(TOPSIS) method.A case study of CCHP system used in a hospital in the north of China validated the effectiveness of this method.The results showed that the satisfactory energy management scheme of CCHP system was obtained based on this initiative optimization operation strategy and multi-objective energy management method.The CCHP system has achieved better energy efficiency,environmental protection and economic benefits.

Single-objective vs. multi-objective autocalibration in modelling total suspended solids and phosphorus in a small agricultural watershed with SWAT.

Science.gov (United States)

Rasolomanana, Santatriniaina Denise; Lessard, Paul; Vanrolleghem, Peter A

2012-01-01

To obtain greater precision in modelling small agricultural watersheds, a shorter simulation time step is beneficial. A daily time step better represents the dynamics of pollutants in the river and provides more realistic simulation results. However, with a daily evaluation performance, good fits are rarely obtained. With the Shuffled Complex Evolution (SCE) method embedded in the Soil and Water Assessment Tool (SWAT), two calibration approaches are available, single-objective or multi-objective optimization. The goal of the present study is to evaluate which approach can improve the daily performance with SWAT, in modelling flow (Q), total suspended solids (TSS) and total phosphorus (TP). The influence of weights assigned to the different variables included in the objective function has also been tested. The results showed that: (i) the model performance depends not only on the choice of calibration approach, but essentially on the influential parameters; (ii) the multi-objective calibration estimating at once all parameters related to all measured variables is the best approach to model Q, TSS and TP; (iii) changing weights does not improve model performance; and (iv) with a single-objective optimization, an excellent water quality modelling performance may hide a loss of performance of predicting flows and unbalanced internal model components.

A probabilistic multi objective CLSC model with Genetic algorithm-ε_Constraint approach

Directory of Open Access Journals (Sweden)

Alireza TaheriMoghadam

2014-05-01

Full Text Available In this paper an uncertain multi objective closed-loop supply chain is developed. The first objective function is maximizing the total profit. The second objective function is minimizing the use of row materials. In the other word, the second objective function is maximizing the amount of remanufacturing and recycling. Genetic algorithm is used for optimization and for finding the pareto optimal line, Epsilon-constraint method is used. Finally a numerical example is solved with proposed approach and performance of the model is evaluated in different sizes. The results show that this approach is effective and useful for managerial decisions.

The spectrometer of the High-Resolution Multi position Thomson Scattering Diagnostic for TJ-II

International Nuclear Information System (INIS)

Herranz, J.; Barth, C. J.; Castejon, F.; Lopez-Sanchez, A.; Mirones, E.; Pastor, I.; Perez, D.; Rodriguez, C.

2001-01-01

Since 1998, a high-resolution multiposition thompson scattering system is in operation at the stellarator TJ-II, combining high accuracy and excellent spatial resolution. A description of the diagnostic spectrometer is presented. The main characteristics of the spectrometer that allow YJ-II Thomson scattering diagnostic to have high spatial and spectral resolution are described in this paper. (Author)

Multi-objective optimization design method of radiation shielding

International Nuclear Information System (INIS)

Yang Shouhai; Wang Weijin; Lu Daogang; Chen Yixue

2012-01-01

Due to the shielding design goals of diversification and uncertain process of many factors, it is necessary to develop an optimization design method of intelligent shielding by which the shielding scheme selection will be achieved automatically and the uncertainties of human impact will be reduced. For economical feasibility to achieve a radiation shielding design for automation, the multi-objective genetic algorithm optimization of screening code which combines the genetic algorithm and discrete-ordinate method was developed to minimize the costs, size, weight, and so on. This work has some practical significance for gaining the optimization design of shielding. (authors)

Multi-objective optimization of GENIE Earth system models.

Science.gov (United States)

Price, Andrew R; Myerscough, Richard J; Voutchkov, Ivan I; Marsh, Robert; Cox, Simon J

2009-07-13

The tuning of parameters in climate models is essential to provide reliable long-term forecasts of Earth system behaviour. We apply a multi-objective optimization algorithm to the problem of parameter estimation in climate models. This optimization process involves the iterative evaluation of response surface models (RSMs), followed by the execution of multiple Earth system simulations. These computations require an infrastructure that provides high-performance computing for building and searching the RSMs and high-throughput computing for the concurrent evaluation of a large number of models. Grid computing technology is therefore essential to make this algorithm practical for members of the GENIE project.

Multi-objective optimization problems concepts and self-adaptive parameters with mathematical and engineering applications

CERN Document Server

Lobato, Fran Sérgio

2017-01-01

This book is aimed at undergraduate and graduate students in applied mathematics or computer science, as a tool for solving real-world design problems. The present work covers fundamentals in multi-objective optimization and applications in mathematical and engineering system design using a new optimization strategy, namely the Self-Adaptive Multi-objective Optimization Differential Evolution (SA-MODE) algorithm. This strategy is proposed in order to reduce the number of evaluations of the objective function through dynamic update of canonical Differential Evolution parameters (population size, crossover probability and perturbation rate). The methodology is applied to solve mathematical functions considering test cases from the literature and various engineering systems design, such as cantilevered beam design, biochemical reactor, crystallization process, machine tool spindle design, rotary dryer design, among others.

Thermo-economic and environmental analyses based multi-objective optimization of vapor compression–absorption cascaded refrigeration system using NSGA-II technique

International Nuclear Information System (INIS)

Jain, Vaibhav; Sachdeva, Gulshan; Kachhwaha, Surendra Singh; Patel, Bhavesh

2016-01-01

Highlights: • It addresses multi-objective optimization study on cascaded refrigeration system. • Cascaded system is a promising decarburizing and energy efficient technology. • NSGA-II technique is used for multi-objective optimization. • Total annual product cost and irreversibility rate are simultaneously optimized. - Abstract: Present work optimizes the performance of 170 kW vapor compression–absorption cascaded refrigeration system (VCACRS) based on combined thermodynamic, economic and environmental parameters using Non-dominated Sort Genetic Algorithm-II (NSGA-II) technique. Two objective functions including the total irreversibility rate (as a thermodynamic criterion) and the total product cost (as an economic criterion) of the system are considered simultaneously for multi-objective optimization of VCACRS. The capital and maintenance costs of the system components, the operational cost, and the penalty cost due to CO_2 emission are included in the total product cost of the system. Three optimized systems including a single-objective thermodynamic optimized, a single-objective economic optimized and a multi-objective optimized are analyzed and compared. The results showed that the multi-objective design considers the combined thermodynamic and total product cost criteria better than the two individual single-objective thermodynamic and total product cost optimized designs.

A multi-objective approach to improve SWAT model calibration in alpine catchments

Science.gov (United States)

Tuo, Ye; Marcolini, Giorgia; Disse, Markus; Chiogna, Gabriele

2018-04-01

Multi-objective hydrological model calibration can represent a valuable solution to reduce model equifinality and parameter uncertainty. The Soil and Water Assessment Tool (SWAT) model is widely applied to investigate water quality and water management issues in alpine catchments. However, the model calibration is generally based on discharge records only, and most of the previous studies have defined a unique set of snow parameters for an entire basin. Only a few studies have considered snow observations to validate model results or have taken into account the possible variability of snow parameters for different subbasins. This work presents and compares three possible calibration approaches. The first two procedures are single-objective calibration procedures, for which all parameters of the SWAT model were calibrated according to river discharge alone. Procedures I and II differ from each other by the assumption used to define snow parameters: The first approach assigned a unique set of snow parameters to the entire basin, whereas the second approach assigned different subbasin-specific sets of snow parameters to each subbasin. The third procedure is a multi-objective calibration, in which we considered snow water equivalent (SWE) information at two different spatial scales (i.e. subbasin and elevation band), in addition to discharge measurements. We tested these approaches in the Upper Adige river basin where a dense network of snow depth measurement stations is available. Only the set of parameters obtained with this multi-objective procedure provided an acceptable prediction of both river discharge and SWE. These findings offer the large community of SWAT users a strategy to improve SWAT modeling in alpine catchments.

A multi-objective framework to predict flows of ungauged rivers within regions of sparse hydrometeorologic observation

Science.gov (United States)

Alipour, M.; Kibler, K. M.

2017-12-01

Despite advances in flow prediction, managers of ungauged rivers located within broad regions of sparse hydrometeorologic observation still lack prescriptive methods robust to the data challenges of such regions. We propose a multi-objective streamflow prediction framework for regions of minimum observation to select models that balance runoff efficiency with choice of accurate parameter values. We supplement sparse observed data with uncertain or low-resolution information incorporated as `soft' a priori parameter estimates. The performance of the proposed framework is tested against traditional single-objective and constrained single-objective calibrations in two catchments in a remote area of southwestern China. We find that the multi-objective approach performs well with respect to runoff efficiency in both catchments (NSE = 0.74 and 0.72), within the range of efficiencies returned by other models (NSE = 0.67 - 0.78). However, soil moisture capacity estimated by the multi-objective model resonates with a priori estimates (parameter residuals of 61 cm versus 289 and 518 cm for maximum soil moisture capacity in one catchment, and 20 cm versus 246 and 475 cm in the other; parameter residuals of 0.48 versus 0.65 and 0.7 for soil moisture distribution shape factor in one catchment, and 0.91 versus 0.79 and 1.24 in the other). Thus, optimization to a multi-criteria objective function led to very different representations of soil moisture capacity as compared to models selected by single-objective calibration, without compromising runoff efficiency. These different soil moisture representations may translate into considerably different hydrological behaviors. The proposed approach thus offers a preliminary step towards greater process understanding in regions of severe data limitations. For instance, the multi-objective framework may be an adept tool to discern between models of similar efficiency to select models that provide the "right answers for the right reasons

Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Xiang Yu

2016-06-01

Full Text Available Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing hydropower generation and minimizing deviation from the outflow lower target to realize the system’s economic, environmental and social benefits during the drought season, are optimized simultaneously. Experimental results demonstrate that the optimization framework helps to robustly derive multiple feasible non-dominated solutions with satisfactory convergence, diversity and extremity in one single run for the case studied.

Multi-objective optimization of solid waste flows: environmentally sustainable strategies for municipalities.

Science.gov (United States)

Minciardi, Riccardo; Paolucci, Massimo; Robba, Michela; Sacile, Roberto

2008-11-01

An approach to sustainable municipal solid waste (MSW) management is presented, with the aim of supporting the decision on the optimal flows of solid waste sent to landfill, recycling and different types of treatment plants, whose sizes are also decision variables. This problem is modeled with a non-linear, multi-objective formulation. Specifically, four objectives to be minimized have been taken into account, which are related to economic costs, unrecycled waste, sanitary landfill disposal and environmental impact (incinerator emissions). An interactive reference point procedure has been developed to support decision making; these methods are considered appropriate for multi-objective decision problems in environmental applications. In addition, interactive methods are generally preferred by decision makers as they can be directly involved in the various steps of the decision process. Some results deriving from the application of the proposed procedure are presented. The application of the procedure is exemplified by considering the interaction with two different decision makers who are assumed to be in charge of planning the MSW system in the municipality of Genova (Italy).

An Integrated Method for Interval Multi-Objective Planning of a Water Resource System in the Eastern Part of Handan

Directory of Open Access Journals (Sweden)

Meiqin Suo

2017-07-01

Full Text Available In this study, an integrated solving method is proposed for interval multi-objective planning. The proposed method is based on fuzzy linear programming and an interactive two-step method. It cannot only provide objectively optimal values for multiple objectives at the same time, but also effectively offer a globally optimal interval solution. Meanwhile, the degree of satisfaction related to different objective functions would be obtained. Then, the integrated solving method for interval multi-objective planning is applied to a case study of planning multi-water resources joint scheduling under uncertainty in the eastern part of Handan, China. The solutions obtained are useful for decision makers in easing the contradiction between supply of multi-water resources and demand from different water users. Moreover, it can provide the optimal comprehensive benefits of economy, society, and the environment.

Construction and simulation of a multi-reflection time-of-flight mass spectrometer at the University of Notre Dame

Energy Technology Data Exchange (ETDEWEB)

Schultz, B.E., E-mail: bschult4@nd.edu; Kelly, J.M.; Nicoloff, C.; Long, J.; Ryan, S.; Brodeur, M.

2016-06-01

One of the most significant problems in the production of rare isotopes is the simultaneous production of contaminants, often time isobaric. Thus, a high-resolution beam purification method is required which needs to be compatible with both the low yield and short half-life of the desired radionuclide. A multi-reflection time-of-flight mass spectrometer meets all these criteria, in addition to boasting a smaller footprint relative to traditional separator dipole magnets. Such a device is currently under construction at the University of Notre Dame and is intended to be coupled to the IG-ISOL source of the planned cyclotron facility. The motivation and conceptual design are presented, as well as the status of simulations to determine the feasibility of using a Bradbury–Nielsen gate for bunching ion beams during initial system testing.

The AMS [Accelerator Mass Spectrometer] program at LLNL

International Nuclear Information System (INIS)

Proctor, I.D.

1988-09-01

Livermore will have an operational Accelerator Mass Spectrometer (AMS) by mid-1989 as part of its new Multi-user Tandem Laboratory. The spectrometer was designed primarily for applications in archaeology and the geosciences and was co-funded by the University of California Regents. Radiological control for personnel protection, ion sources and injection systems, the tandem and all beam handling hardware are operated with a distributed processor computer control system. The Tandem is the former University of Washington injector FN which has been upgraded with Dowlish tubes, pelletron charging and SF 6 gas. Design goals for the AMS system, computer aided operation, automated measurement capability, initial results and some of our intended applications will be presented. 5 refs., 2 figs

Cancer microarray data feature selection using multi-objective binary particle swarm optimization algorithm

Science.gov (United States)

Annavarapu, Chandra Sekhara Rao; Dara, Suresh; Banka, Haider

2016-01-01

Cancer investigations in microarray data play a major role in cancer analysis and the treatment. Cancer microarray data consists of complex gene expressed patterns of cancer. In this article, a Multi-Objective Binary Particle Swarm Optimization (MOBPSO) algorithm is proposed for analyzing cancer gene expression data. Due to its high dimensionality, a fast heuristic based pre-processing technique is employed to reduce some of the crude domain features from the initial feature set. Since these pre-processed and reduced features are still high dimensional, the proposed MOBPSO algorithm is used for finding further feature subsets. The objective functions are suitably modeled by optimizing two conflicting objectives i.e., cardinality of feature subsets and distinctive capability of those selected subsets. As these two objective functions are conflicting in nature, they are more suitable for multi-objective modeling. The experiments are carried out on benchmark gene expression datasets, i.e., Colon, Lymphoma and Leukaemia available in literature. The performance of the selected feature subsets with their classification accuracy and validated using 10 fold cross validation techniques. A detailed comparative study is also made to show the betterment or competitiveness of the proposed algorithm. PMID:27822174

Processing Technology Selection for Municipal Sewage Treatment Based on a Multi-Objective Decision Model under Uncertainty

Directory of Open Access Journals (Sweden)

Xudong Chen

2018-03-01

Full Text Available This study considers the two factors of environmental protection and economic benefits to address municipal sewage treatment. Based on considerations regarding the sewage treatment plant construction site, processing technology, capital investment, operation costs, water pollutant emissions, water quality and other indicators, we establish a general multi-objective decision model for optimizing municipal sewage treatment plant construction. Using the construction of a sewage treatment plant in a suburb of Chengdu as an example, this paper tests the general model of multi-objective decision-making for the sewage treatment plant construction by implementing a genetic algorithm. The results show the applicability and effectiveness of the multi-objective decision model for the sewage treatment plant. This paper provides decision and technical support for the optimization of municipal sewage treatment.

A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty

Science.gov (United States)

Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

2015-06-01

The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

The Science Case for Multi-Object Spectroscopy on the European ELT

NARCIS (Netherlands)

Evans, Chris; Puech, Mathieu; Afonso, Jose; Almaini, Omar; Amram, Philippe; Aussel, Hervé; Barbuy, Beatriz; Basden, Alistair; Bastian, Nate; Battaglia, Giuseppina; Biller, Beth; Bonifacio, Piercarlo; Bouché, Nicholas; Bunker, Andy; Caffau, Elisabetta; Charlot, Stephane; Cirasuolo, Michele; Clenet, Yann; Combes, Francoise; Conselice, Chris; Contini, Thierry; Cuby, Jean-Gabriel; Dalton, Gavin; Davies, Ben; de Koter, Alex; Disseau, Karen; Dunlop, Jim; Epinat, Benoît; Fiore, Fabrizio; Feltzing, Sofia; Ferguson, Annette; Flores, Hector; Fontana, Adriano; Fusco, Thierry; Gadotti, Dimitri; Gallazzi, Anna; Gallego, Jesus; Giallongo, Emanuele; Gonçalves, Thiago; Gratadour, Damien; Guenther, Eike; Hammer, Francois; Hill, Vanessa; Huertas-Company, Marc; Ibata, Roridgo; Kaper, Lex; Korn, Andreas; Larsen, Søren; Le Fèvre, Olivier; Lemasle, Bertrand; Maraston, Claudia; Mei, Simona; Mellier, Yannick; Morris, Simon; Östlin, Göran; Paumard, Thibaut; Pello, Roser; Pentericci, Laura; Peroux, Celine; Petitjean, Patrick; Rodrigues, Myriam; Rodríguez-Muñoz, Lucía; Rouan, Daniel; Sana, Hugues; Schaerer, Daniel; Telles, Eduardo; Trager, Scott; Tresse, Laurence; Welikala, Niraj; Zibetti, Stefano; Ziegler, Bodo

2015-01-01

This White Paper presents the scientific motivations for a multi-object spectrograph (MOS) on the European Extremely Large Telescope (E-ELT). The MOS case draws on all fields of contemporary astronomy, from extra-solar planets, to the study of the halo of the Milky Way and its satellites, and from

Multi-objective optimization of a series–parallel system using GPSIA

International Nuclear Information System (INIS)

Okafor, Ekene Gabriel; Sun Youchao

2012-01-01

The optimal solution of a multi-objective optimization problem (MOP) corresponds to a Pareto set that is characterized by a tradeoff between objectives. Genetic Pareto Set Identification Algorithm (GPSIA) proposed for reliability-redundant MOPs is a hybrid technique which combines genetic and heuristic principles to generate non-dominated solutions. Series–parallel system with active redundancy is studied in this paper. Reliability and cost were the research objective functions subject to cost and weight constraints. The results reveal an evenly distributed non-dominated front. The distances between successive Pareto points were used to evaluate the general performance of the method. Plots were also used to show the computational results for the type of system studied and the robustness of the technique is discussed in comparison with NSGA-II and SPEA-2.

Investigation on multi-objective performance optimization algorithm application of fan based on response surface method and entropy method

Science.gov (United States)

Zhang, Li; Wu, Kexin; Liu, Yang

2017-12-01

A multi-objective performance optimization method is proposed, and the problem that single structural parameters of small fan balance the optimization between the static characteristics and the aerodynamic noise is solved. In this method, three structural parameters are selected as the optimization variables. Besides, the static pressure efficiency and the aerodynamic noise of the fan are regarded as the multi-objective performance. Furthermore, the response surface method and the entropy method are used to establish the optimization function between the optimization variables and the multi-objective performances. Finally, the optimized model is found when the optimization function reaches its maximum value. Experimental data shows that the optimized model not only enhances the static characteristics of the fan but also obviously reduces the noise. The results of the study will provide some reference for the optimization of multi-objective performance of other types of rotating machinery.

An Efficacious Multi-Objective Fuzzy Linear Programming Approach for Optimal Power Flow Considering Distributed Generation.

Science.gov (United States)

Warid, Warid; Hizam, Hashim; Mariun, Norman; Abdul-Wahab, Noor Izzri

2016-01-01

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality.

Design optimization of axial flow hydraulic turbine runner: Part II - multi-objective constrained optimization method

Science.gov (United States)

Peng, Guoyi; Cao, Shuliang; Ishizuka, Masaru; Hayama, Shinji

2002-06-01

This paper is concerned with the design optimization of axial flow hydraulic turbine runner blade geometry. In order to obtain a better design plan with good performance, a new comprehensive performance optimization procedure has been presented by combining a multi-variable multi-objective constrained optimization model with a Q3D inverse computation and a performance prediction procedure. With careful analysis of the inverse design of axial hydraulic turbine runner, the total hydraulic loss and the cavitation coefficient are taken as optimization objectives and a comprehensive objective function is defined using the weight factors. Parameters of a newly proposed blade bound circulation distribution function and parameters describing positions of blade leading and training edges in the meridional flow passage are taken as optimization variables.The optimization procedure has been applied to the design optimization of a Kaplan runner with specific speed of 440 kW. Numerical results show that the performance of designed runner is successfully improved through optimization computation. The optimization model is found to be validated and it has the feature of good convergence. With the multi-objective optimization model, it is possible to control the performance of designed runner by adjusting the value of weight factors defining the comprehensive objective function. Copyright

Multi-objective based on parallel vector evaluated particle swarm optimization for optimal steady-state performance of power systems

DEFF Research Database (Denmark)

Vlachogiannis, Ioannis (John); Lee, K Y

2009-01-01

In this paper the state-of-the-art extended particle swarm optimization (PSO) methods for solving multi-objective optimization problems are represented. We emphasize in those, the co-evolution technique of the parallel vector evaluated PSO (VEPSO), analysed and applied in a multi-objective problem...

Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects

Science.gov (United States)

Joseph, R.; Courbin, F.; Starck, J.-L.

2016-05-01

We introduce a new algorithm for colour separation and deblending of multi-band astronomical images called MuSCADeT which is based on Morpho-spectral Component Analysis of multi-band images. The MuSCADeT algorithm takes advantage of the sparsity of astronomical objects in morphological dictionaries such as wavelets and their differences in spectral energy distribution (SED) across multi-band observations. This allows us to devise a model independent and automated approach to separate objects with different colours. We show with simulations that we are able to separate highly blended objects and that our algorithm is robust against SED variations of objects across the field of view. To confront our algorithm with real data, we use HST images of the strong lensing galaxy cluster MACS J1149+2223 and we show that MuSCADeT performs better than traditional profile-fitting techniques in deblending the foreground lensing galaxies from background lensed galaxies. Although the main driver for our work is the deblending of strong gravitational lenses, our method is fit to be used for any purpose related to deblending of objects in astronomical images. An example of such an application is the separation of the red and blue stellar populations of a spiral galaxy in the galaxy cluster Abell 2744. We provide a python package along with all simulations and routines used in this paper to contribute to reproducible research efforts. Codes can be found at http://lastro.epfl.ch/page-126973.html

Multi-objective optimization of riparian buffer networks; valuing present and future benefits

Science.gov (United States)

Multi-objective optimization has emerged as a popular approach to support water resources planning and management. This approach provides decision-makers with a suite of management options which are generated based on metrics that represent different social, economic, and environ...

Multi-objective optimization of a type of ellipse-parabola shaped superelastic flexure hinge

Directory of Open Access Journals (Sweden)

Z. Du

2016-05-01

Full Text Available Flexure hinges made of superelastic materials is a promising candidate to enhance the movability of compliant mechanisms. In this paper, we focus on the multi-objective optimization of a type of ellipse-parabola shaped superelastic flexure hinge. The objective is to determine a set of optimal geometric parameters that maximizes the motion range and the relative compliance of the flexure hinge and minimizes the relative rotation error during the deformation as well. Firstly, the paper presents a new type of ellipse-parabola shaped flexure hinge which is constructed by an ellipse arc and a parabola curve. Then, the static responses of superelastic flexure hinges are solved via non-prismatic beam elements derived by the co-rotational approach. Finite element analysis (FEA and experiment tests are performed to verify the modeling method. Finally, a multi-objective optimization is performed and the Pareto frontier is found via the NSGA-II algorithm.

Multi-objective optimization under uncertainty for sheet metal forming

Directory of Open Access Journals (Sweden)

Lafon Pascal

2016-01-01

Full Text Available Aleatory uncertainties in material properties, blank thickness and friction condition are inherent and irreducible variabilities in sheet metal forming. Optimal design configurations, which are obtained by conventional design optimization methods, are not always able to meet the desired targets due to the effect of uncertainties. This paper proposes a multi-objective robust design optimization that aims to tackle this problem. Results obtained on a U shape draw bending benchmark show that spring-back effect can be controlled by optimizing process parameters.

A sustainable manufacturing system design: A fuzzy multi-objective optimization model.

Science.gov (United States)

Nujoom, Reda; Mohammed, Ahmed; Wang, Qian

2017-08-10

In the past decade, there has been a growing concern about the environmental protection in public society as governments almost all over the world have initiated certain rules and regulations to promote energy saving and minimize the production of carbon dioxide (CO 2 ) emissions in many manufacturing industries. The development of sustainable manufacturing systems is considered as one of the effective solutions to minimize the environmental impact. Lean approach is also considered as a proper method for achieving sustainability as it can reduce manufacturing wastes and increase the system efficiency and productivity. However, the lean approach does not include environmental waste of such as energy consumption and CO 2 emissions when designing a lean manufacturing system. This paper addresses these issues by evaluating a sustainable manufacturing system design considering a measurement of energy consumption and CO 2 emissions using different sources of energy (oil as direct energy source to generate thermal energy and oil or solar as indirect energy source to generate electricity). To this aim, a multi-objective mathematical model is developed incorporating the economic and ecological constraints aimed for minimization of the total cost, energy consumption, and CO 2 emissions for a manufacturing system design. For the real world scenario, the uncertainty in a number of input parameters was handled through the development of a fuzzy multi-objective model. The study also addresses decision-making in the number of machines, the number of air-conditioning units, and the number of bulbs involved in each process of a manufacturing system in conjunction with a quantity of material flow for processed products. A real case study was used for examining the validation and applicability of the developed sustainable manufacturing system model using the fuzzy multi-objective approach.

Intersection signal control multi-objective optimization based on genetic algorithm

OpenAIRE

Zhanhong Zhou; Ming Cai

2014-01-01

A signal control intersection increases not only vehicle delay, but also vehicle emissions and fuel consumption in that area. Because more and more fuel and air pollution problems arise recently, an intersection signal control optimization method which aims at reducing vehicle emissions, fuel consumption and vehicle delay is required heavily. This paper proposed a signal control multi-object optimization method to reduce vehicle emissions, fuel consumption and vehicle delay simultaneously at ...

Multi-objective three stage design optimization for island microgrids

International Nuclear Information System (INIS)

Sachs, Julia; Sawodny, Oliver

2016-01-01

Highlights: • An enhanced multi-objective three stage design optimization for microgrids is given. • Use of an optimal control problem for the calculation of the optimal operation. • The inclusion of a detailed battery model with CC/CV charging control. • The determination of a representative profile with optimized number of days. • The proposed method finds its direct application in a design tool for microgids. - Abstract: Hybrid off-grid energy systems enable a cost efficient and reliable energy supply to rural areas around the world. The main potential for a low cost operation and uninterrupted power supply lies in the optimal sizing and operation of such microgrids. In particular, sudden variations in load demand or in the power supply from renewables underline the need for an optimally sized system. This paper presents an efficient multi-objective model based optimization approach for the optimal sizing of all components and the determination of the best power electronic layout. The presented method is divided into three optimization problems to minimize economic and environmental objectives. This design optimization includes detailed components models and an optimized energy dispatch strategy which enables the optimal design of the energy system with respect to an adequate control for the specific configuration. To significantly reduce the computation time without loss of accuracy, the presented method contains the determination of a representative load profile using a k-means clustering method. The k-means algorithm itself is embedded in an optimization problem for the calculation of the optimal number of clusters. The benefits in term of reduced computation time, inclusion of optimal energy dispatch and optimization of power electronic architecture, of the presented optimization method are illustrated using a case study.

Multi-objective optimization of water supply network rehabilitation with non-dominated sorting Genetic Algorithm-Ⅱ

Institute of Scientific and Technical Information of China (English)

Xi JIN; Jie ZHANG; Jin-liang GAO; Wen-yan WU

2008-01-01

Through the transformation of hydraulic constraints into the objective functions associated with a water supply network rehabilitation problem, a non-dominated sorting Genetic Aigorithm-Ⅱ (NSGA-Ⅱ) can be used to solve the altered multi-objective optimization model. The introduction of NSGA-Ⅱ into water supply network optimal rehabilitation problem solves the conflict between one fitness value of standard genetic algorithm (SGA) and multi-objectives of rehabilitation problem. And the uncertainties brought by using weight coefficients or punish functions in conventional methods are controlled. And also by introduction of artificial inducement mutation (AIM) operation, the convergence speed of population is accelerated; this operation not only improves the convergence speed, but also improves the rationality and feasibility of solutions.

Multi-objective particle swarm and genetic algorithm for the optimization of the LANSCE linac operation

International Nuclear Information System (INIS)

Pang, X.; Rybarcyk, L.J.

2014-01-01

Particle swarm optimization (PSO) and genetic algorithm (GA) are both nature-inspired population based optimization methods. Compared to GA, whose long history can trace back to 1975, PSO is a relatively new heuristic search method first proposed in 1995. Due to its fast convergence rate in single objective optimization domain, the PSO method has been extended to optimize multi-objective problems. In this paper, we will introduce the PSO method and its multi-objective extension, the MOPSO, apply it along with the MOGA (mainly the NSGA-II) to simulations of the LANSCE linac and operational set point optimizations. Our tests show that both methods can provide very similar Pareto fronts but the MOPSO converges faster

Multi-objective particle swarm and genetic algorithm for the optimization of the LANSCE linac operation

Energy Technology Data Exchange (ETDEWEB)

Pang, X., E-mail: xpang@lanl.gov; Rybarcyk, L.J.

2014-03-21

Particle swarm optimization (PSO) and genetic algorithm (GA) are both nature-inspired population based optimization methods. Compared to GA, whose long history can trace back to 1975, PSO is a relatively new heuristic search method first proposed in 1995. Due to its fast convergence rate in single objective optimization domain, the PSO method has been extended to optimize multi-objective problems. In this paper, we will introduce the PSO method and its multi-objective extension, the MOPSO, apply it along with the MOGA (mainly the NSGA-II) to simulations of the LANSCE linac and operational set point optimizations. Our tests show that both methods can provide very similar Pareto fronts but the MOPSO converges faster.

Benchmarking and performance enhancement framework for multi-staging object-oriented languages

Directory of Open Access Journals (Sweden)

Ahmed H. Yousef

2013-06-01

Full Text Available This paper focuses on verifying the readiness, feasibility, generality and usefulness of multi-staging programming in software applications. We present a benchmark designed to evaluate the performance gain of different multi-staging programming (MSP languages implementations of object oriented languages. The benchmarks in this suite cover different tests that range from classic simple examples (like matrix algebra to advanced examples (like encryption and image processing. The benchmark is applied to compare the performance gain of two different MSP implementations (Mint and Metaphor that are built on object oriented languages (Java and C# respectively. The results concerning the application of this benchmark on these languages are presented and analysed. The measurement technique used in benchmarking leads to the development of a language independent performance enhancement framework that allows the programmer to select which code segments need staging. The framework also enables the programmer to verify the effectiveness of staging on the application performance. The framework is applied to a real case study. The case study results showed the effectiveness of the framework to achieve significant performance enhancement.

A multi-objective genetic approach to domestic load scheduling in an energy management system

International Nuclear Information System (INIS)

Soares, Ana; Antunes, Carlos Henggeler; Oliveira, Carlos; Gomes, Álvaro

2014-01-01

In this paper a multi-objective genetic algorithm is used to solve a multi-objective model to optimize the time allocation of domestic loads within a planning period of 36 h, in a smart grid context. The management of controllable domestic loads is aimed at minimizing the electricity bill and the end-user’s dissatisfaction concerning two different aspects: the preferred time slots for load operation and the risk of interruption of the energy supply. The genetic algorithm is similar to the Elitist NSGA-II (Nondominated Sorting Genetic Algorithm II), in which some changes have been introduced to adapt it to the physical characteristics of the load scheduling problem and improve usability of results. The mathematical model explicitly considers economical, technical, quality of service and comfort aspects. Illustrative results are presented and the characteristics of different solutions are analyzed. - Highlights: • A genetic algorithm similar to the NSGA-II is used to solve a multi-objective model. • The optimized time allocation of domestic loads in a smart grid context is achieved. • A variable preference profile for the operation of the managed loads is included. • A safety margin is used to account for the quality of the energy services provided. • A non-dominated front with the solutions in the two-objective space is obtained

Multi-Objective Optimal Design of Renewable Energy Integrated CCHP System Using PICEA-g

Directory of Open Access Journals (Sweden)

Guozheng Li

2018-03-01

Full Text Available The integration of renewable energies into combined cooling, heating, and power (CCHP systems has become increasingly popular in recent years. However, the optimization of renewable energies integrated CCHP (RECCHP systems (i.e., optimal component configurations is far from being well addressed, especially in isolated mode. This study aims to fill this research gap. A multi-objective optimization model characterizing the system reliability, system cost, and environmental sustainability is constructed. In this model, the objectives include minimization of annual total cost (ATC, carbon dioxide emission (CDE, and loss of energy supply probability (LESP. The decision variables representing the configuration of the RECCHP system include the number of photovoltaic (PV panels and wind turbines (WTs, the tilt angle of PV panels, the height of WTs, the maximum fuel consumption, and the capacity of battery and heat storage tanks (HSTs. The multi-objective model is solved by a multi-objective evolutionary algorithm, namely, the preference-inspired coevolutionary algorithm (PICEA-g, resulting in a set of Pareto optimal (trade-off solutions. Then, a decision-making process is demonstrated, selecting a preferred solution amongst those trade-off solutions by further considering the decision-maker preferences. Furthermore, on the optimization of the RECCHP system, operational strategies (i.e., following electric load, FEL, and following thermal load, FTL are considered, respectively. Experimental results show that the FEL and FTL strategies lead to different optimal configurations. In general, the FTL is recommended in summer and winter, while the FEL is more suitable for spring and autumn. Compared with traditional energy systems, RECCHP has better economic and environmental advantages.

Design for Sustainability of Industrial Symbiosis based on Emergy and Multi-objective Particle Swarm Optimization

DEFF Research Database (Denmark)

Ren, Jingzheng; Liang, Hanwei; Dong, Liang

2016-01-01

approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable...... performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied...

Exploiting Higher Order and Multi-modal Features for 3D Object Detection

DEFF Research Database (Denmark)

Kiforenko, Lilita

that describe object visual appearance such as shape, colour, texture etc. This thesis focuses on robust object detection and pose estimation of rigid objects using 3D information. The thesis main contributions are novel feature descriptors together with object detection and pose estimation algorithms....... The initial work introduces a feature descriptor that uses edge categorisation in combination with a local multi-modal histogram descriptor in order to detect objects with little or no texture or surface variation. The comparison is performed with a state-of-the-art method, which is outperformed...... of the methods work well for one type of objects in a specific scenario, in another scenario or with different objects they might fail, therefore more robust solutions are required. The typical problem solution is the design of robust feature descriptors, where feature descriptors contain information...

Ultra-Wideband Optical Modulation Spectrometer (OMS) Development

Science.gov (United States)

Gardner, Jonathan (Technical Monitor); Tolls, Volker

2004-01-01

The optical modulation spectrometer (OMS) is a novel, highly efficient, low mass backend for heterodyne receiver systems. Current and future heterodyne receiver systems operating at frequencies up to a few THz require broadband spectrometer backends to achieve spectral resolutions of R approximately 10(exp 5) to 10(exp 6) to carry out many important astronomical investigations. Among these are observations of broad emission and absorption lines from extra-galactic objects at high redshifts, spectral line surveys, and observations of planetary atmospheres. Many of these lines are pressure or velocity broadened with either large half-widths or line wings extending over several GHz. Current backend systems can cover the needed bandwidth only by combining the output of several spectrometers, each with typically up to 1 GHz bandwidth, or by combining several frequency-shifted spectra taken with a single spectrometer. An ultra-wideband optical modulation spectrometer with 10 - 40 GHz bandwidth will enable broadband ob- servations without the limitations and disadvantages of hybrid spectrometers. Spectrometers like the OMS will be important for both ground-based observatories and future space missions like the Single Aperture Far-Infrared Telescope (SAFIR) which might carry IR/submm array heterodyne receiver systems requiring a spectrometer for each array pixel. Small size, low mass and small power consumption are extremely important for space missions. This report summarizes the specifications developed for the OMS and lists already identified commercial parts. The report starts with a review of the principle of operation, then describes the most important components and their specifications which were derived from theory, and finishes with a conclusion and outlook.

Application of the multi-objective cross-entropy method to the vehicle routing problem with soft time windows

Directory of Open Access Journals (Sweden)

C Hauman

2014-06-01

Full Text Available The vehicle routing problem with time windows is a widely studied problem with many real-world applications. The problem considered here entails the construction of routes that a number of identical vehicles travel to service different nodes within a certain time window. New benchmark problems with multi-objective features were recently suggested in the literature and the multi-objective optimisation cross-entropy method is applied to these problems to investigate the feasibility of the method and to determine and propose reference solutions for the benchmark problems. The application of the cross-entropy method to the multi-objective vehicle routing problem with soft time windows is investigated. The objectives that are evaluated include the minimisation of the total distance travelled, the number of vehicles and/or routes, the total waiting time and delay time of the vehicles and the makespan of a route.

Exchanging large data object in multi-agent systems

Science.gov (United States)

Al-Yaseen, Wathiq Laftah; Othman, Zulaiha Ali; Nazri, Mohd Zakree Ahmad

2016-08-01

One of the Business Intelligent solutions that is currently in use is the Multi-Agent System (MAS). Communication is one of the most important elements in MAS, especially for exchanging large low level data between distributed agents (physically). The Agent Communication Language in JADE has been offered as a secure method for sending data, whereby the data is defined as an object. However, the object cannot be used to send data to another agent in a different location. Therefore, the aim of this paper was to propose a method for the exchange of large low level data as an object by creating a proxy agent known as a Delivery Agent, which temporarily imitates the Receiver Agent. The results showed that the proposed method is able to send large-sized data. The experiments were conducted using 16 datasets ranging from 100,000 to 7 million instances. However, for the proposed method, the RAM and the CPU machine had to be slightly increased for the Receiver Agent, but the latency time was not significantly different compared to the use of the Java Socket method (non-agent and less secure). With such results, it was concluded that the proposed method can be used to securely send large data between agents.

Polar vessel hullform design based on the multi-objective optimization NSGA II

Directory of Open Access Journals (Sweden)

DUAN Fei

2017-12-01

Full Text Available [Objectives] With the increasing exploitation of the Arctic abundant oil and gas resources, a large number of ships which meet the polar navigational requirements are needed.[Methods] In this paper, the fast elitist Non-Dominated Sorting Genetic Algorithm (NSGA Ⅱ is applied to the hull optimization, and the multi-objective optimization method of polar vessel design is proposed. With the optimization goal of resistance and icebreaking resistance, filtering hull forms through the standard of polar vessel displacement and EEDI, fast ship hull optimization that satisfy the ice-ship dead weight and EEDI requirements has been achieved. Taking a 65 000 t shuttle tanker as an example, full parametric modeling method is adopted, the hull optimization of three different bow forms is conducted through the polar vessel multi-objective optimization method.[Results] The ship hull after optimization can satisfy the IA class navigation require, where the resistance in calm water decreases up to 12.94%, and the minimum propulsion power in ice field has a 27.36% reduction.[Conclusions] The feasibility and validity of the NSGA Ⅱ applying in polar vessel design is verified.

Multi-objective Analysis for a Sequencing Planning of Mixed-model Assembly Line

Science.gov (United States)

Shimizu, Yoshiaki; Waki, Toshiya; Yoo, Jae Kyu

Diversified customer demands are raising importance of just-in-time and agile manufacturing much more than before. Accordingly, introduction of mixed-model assembly lines becomes popular to realize the small-lot-multi-kinds production. Since it produces various kinds on the same assembly line, a rational management is of special importance. With this point of view, this study focuses on a sequencing problem of mixed-model assembly line including a paint line as its preceding process. By taking into account the paint line together, reducing work-in-process (WIP) inventory between these heterogeneous lines becomes a major concern of the sequencing problem besides improving production efficiency. Finally, we have formulated the sequencing problem as a bi-objective optimization problem to prevent various line stoppages, and to reduce the volume of WIP inventory simultaneously. Then we have proposed a practical method for the multi-objective analysis. For this purpose, we applied the weighting method to derive the Pareto front. Actually, the resulting problem is solved by a meta-heuristic method like SA (Simulated Annealing). Through numerical experiments, we verified the validity of the proposed approach, and discussed the significance of trade-off analysis between the conflicting objectives.

Multi-Objective Dynamic Economic Dispatch of Microgrid Systems Including Vehicle-to-Grid

Directory of Open Access Journals (Sweden)

Haitao Liu

2015-05-01

Full Text Available Based on the characteristics of electric vehicles (EVs, this paper establishes the load models of EVs under the autonomous charging mode and the coordinated charging and discharging mode. Integrating the EVs into a microgrid system which includes wind turbines (WTs, photovoltaic arrays (PVs, diesel engines (DEs, fuel cells (FCs and a storage battery (BS, this paper establishes multi-objective economic dispatch models of a microgrid, including the lowest operating cost, the least carbon dioxide emissions, and the lowest pollutant treatment cost. After converting the multi-objective functions to a single objective function by using the judgment matrix method, we analyze the dynamic economic dispatch of the microgrid system including vehicle-to-grid (V2G with an improved particle swarm optimization algorithm under different operation control strategies. With the example system, the proposed models and strategies are verified and analyzed. Simulation results show that the microgrid system with EVs under the coordinated charging and discharging mode has better operation economics than the autonomous charging mode. Meanwhile, the greater the load fluctuation is, the higher the operating cost of the microgrid system is.

Gender approaches to evolutionary multi-objective optimization using pre-selection of criteria

Science.gov (United States)

Kowalczuk, Zdzisław; Białaszewski, Tomasz

2018-01-01

A novel idea to perform evolutionary computations (ECs) for solving highly dimensional multi-objective optimization (MOO) problems is proposed. Following the general idea of evolution, it is proposed that information about gender is used to distinguish between various groups of objectives and identify the (aggregate) nature of optimality of individuals (solutions). This identification is drawn out of the fitness of individuals and applied during parental crossover in the processes of evolutionary multi-objective optimization (EMOO). The article introduces the principles of the genetic-gender approach (GGA) and virtual gender approach (VGA), which are not just evolutionary techniques, but constitute a completely new rule (philosophy) for use in solving MOO tasks. The proposed approaches are validated against principal representatives of the EMOO algorithms of the state of the art in solving benchmark problems in the light of recognized EC performance criteria. The research shows the superiority of the gender approach in terms of effectiveness, reliability, transparency, intelligibility and MOO problem simplification, resulting in the great usefulness and practicability of GGA and VGA. Moreover, an important feature of GGA and VGA is that they alleviate the 'curse' of dimensionality typical of many engineering designs.

EFFICIENT MULTI-OBJECTIVE EVOLUTIONARY ALGORITHM FOR JOB SHOP SCHEDULING

Institute of Scientific and Technical Information of China (English)

Lei Deming; Wu Zhiming

2005-01-01

A new representation method is first presented based on priority rules. According to this method, each entry in the chromosome indicates that in the procedure of the Giffler and Thompson (GT) algorithm, the conflict occurring in the corresponding machine is resolved by the corresponding priority rule. Then crowding-measure multi-objective evolutionary algorithm (CMOEA) is designed,in which both archive maintenance and fitness assignment use crowding measure. Finally the comparisons between CMOEA and SPEA in solving 15 scheduling problems demonstrate that CMOEA is suitable to job shop scheduling.

Evaluating the Efficiency of a Multi-core Aware Multi-objective Optimization Tool for Calibrating the SWAT Model

Energy Technology Data Exchange (ETDEWEB)

Zhang, X. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Izaurralde, R. C. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Zong, Z. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Zhao, K. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Thomson, A. M. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2012-08-20

The efficiency of calibrating physically-based complex hydrologic models is a major concern in the application of those models to understand and manage natural and human activities that affect watershed systems. In this study, we developed a multi-core aware multi-objective evolutionary optimization algorithm (MAMEOA) to improve the efficiency of calibrating a worldwide used watershed model (Soil and Water Assessment Tool (SWAT)). The test results show that MAMEOA can save about 1-9%, 26-51%, and 39-56% time consumed by calibrating SWAT as compared with sequential method by using dual-core, quad-core, and eight-core machines, respectively. Potential and limitations of MAMEOA for calibrating SWAT are discussed. MAMEOA is open source software.

Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning.

Science.gov (United States)

Huang, Chang-Qin; Yang, Shang-Ming; Pan, Yan; Lai, Han-Jiang

2018-09-01

Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary "mask" map that can identify the approximate locations of objects in an image, so that we use this binary "mask" map to obtain length-limited hash codes which mainly focus on an image's objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary "mask" sub-network to identify image objects' approximate locations; 3) a weighted average pooling operation based on the binary "mask" to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines.

Rational versus Emotional Reasoning in a Realistic Multi-Objective Environment

OpenAIRE

Mayboudi, Seyed Mohammad Hossein

2011-01-01

ABSTRACT: Emotional intelligence and its associated with models have recently become one of new active studies in the field of artificial intelligence. Several works have been performed on modelling of emotional behaviours such as love, hate, happiness and sadness. This study presents a comparative evaluation of rational and emotional behaviours and the effects of emotions on the decision making process of agents in a realistic multi-objective environment. NetLogo simulation environment is u...

Stability of multi-objective bi-level linear programming problems under fuzziness

Directory of Open Access Journals (Sweden)

Abo-Sinna Mahmoud A.

2013-01-01

Full Text Available This paper deals with multi-objective bi-level linear programming problems under fuzzy environment. In the proposed method, tentative solutions are obtained and evaluated by using the partial information on preference of the decision-makers at each level. The existing results concerning the qualitative analysis of some basic notions in parametric linear programming problems are reformulated to study the stability of multi-objective bi-level linear programming problems. An algorithm for obtaining any subset of the parametric space, which has the same corresponding Pareto optimal solution, is presented. Also, this paper established the model for the supply-demand interaction in the age of electronic commerce (EC. First of all, the study uses the individual objectives of both parties as the foundation of the supply-demand interaction. Subsequently, it divides the interaction, in the age of electronic commerce, into the following two classifications: (i Market transactions, with the primary focus on the supply demand relationship in the marketplace; and (ii Information service, with the primary focus on the provider and the user of information service. By applying the bi-level programming technique of interaction process, the study will develop an analytical process to explain how supply-demand interaction achieves a compromise or why the process fails. Finally, a numerical example of information service is provided for the sake of illustration.

A multi-objective approach to evolving platooning strategies in intelligent transportation systems

NARCIS (Netherlands)

Illigen, W. van; Haasdijk, E.; Kester, L.J.H.M.

2013-01-01

The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective evolutionary algorithm based on NEAT and SPEA2 that evolves highlevel

A Multi-Objective Approach to Evolving Platooning Strategies in Intelligent Transportation Systems

NARCIS (Netherlands)

van Willigen, W; Haasdijk, E; Kester, Leon

2013-01-01

The research in this paper is inspired by a vision of intelligent vehicles that autonomously move along motorways: they join and leave trains of vehicles (platoons), overtake other vehicles, etc. We propose a multi-objective evolutionary algorithm based on NEAT and SPEA2 that evolves high-level

The ANTARES recoil time-of-flight spectrometer

Energy Technology Data Exchange (ETDEWEB)

Martin, J.W.; Russell, G.J. [New South Wales Univ., Kensington, NSW (Australia); Cohen, D.D.; Dytlewski, N. [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

1996-12-31

The Australian National Tandem for Applied Research (ANTARES), is a 8MV FN tandem particle accelerator at the Australian Nuclear Science and Technology Organisation. Research on the accelerator is divided between two groups, Accelerator Mass Spectrometry (AMS) and lon Beam Analysis (IBA). The IBA group carries out a range of research projects from nuclear physics to materials characterisation. The major IBA project on the accelerator is a recoil time-of-flight spectrometer which consists of two electrostatic time pulse generators and an ion-implanted surface barrier detector. The spectrometer is ideally suited to the profiling of layered multi-element materials, and has been used to characterise materials such as metal-germanides, optoelectronics, superconductors and catalytic converters. This paper will describe the time-of-flight system as well as some recent materials characterisation results. 1 refs., 3 figs.

The ANTARES recoil time-of-flight spectrometer

Energy Technology Data Exchange (ETDEWEB)

Martin, J W; Russell, G J [New South Wales Univ., Kensington, NSW (Australia); Cohen, D D; Dytlewski, N [Australian Nuclear Science and Technology Organisation, Lucas Heights, NSW (Australia)

1997-12-31

The Australian National Tandem for Applied Research (ANTARES), is a 8MV FN tandem particle accelerator at the Australian Nuclear Science and Technology Organisation. Research on the accelerator is divided between two groups, Accelerator Mass Spectrometry (AMS) and lon Beam Analysis (IBA). The IBA group carries out a range of research projects from nuclear physics to materials characterisation. The major IBA project on the accelerator is a recoil time-of-flight spectrometer which consists of two electrostatic time pulse generators and an ion-implanted surface barrier detector. The spectrometer is ideally suited to the profiling of layered multi-element materials, and has been used to characterise materials such as metal-germanides, optoelectronics, superconductors and catalytic converters. This paper will describe the time-of-flight system as well as some recent materials characterisation results. 1 refs., 3 figs.

Chaotic improved PSO-based multi-objective optimization for minimization of power losses and L index in power systems

International Nuclear Information System (INIS)

Chen, Gonggui; Liu, Lilan; Song, Peizhu; Du, Yangwei

2014-01-01

Highlights: • New method for MOORPD problem using MOCIPSO and MOIPSO approaches. • Constrain-prior Pareto-dominance method is proposed to meet the constraints. • The limits of the apparent power flow of transmission line are considered. • MOORPD model is built up for MOORPD problem. • The achieved results by MOCIPSO and MOIPSO approaches are better than MOPSO method. - Abstract: Multi-objective optimal reactive power dispatch (MOORPD) seeks to not only minimize power losses, but also improve the stability of power system simultaneously. In this paper, the static voltage stability enhancement is achieved through incorporating L index in MOORPD problem. Chaotic improved PSO-based multi-objective optimization (MOCIPSO) and improved PSO-based multi-objective optimization (MOIPSO) approaches are proposed for solving complex multi-objective, mixed integer nonlinear problems such as minimization of power losses and L index in power systems simultaneously. In MOCIPSO and MOIPSO based optimization approaches, crossover operator is proposed to enhance PSO diversity and improve their global searching capability, and for MOCIPSO based optimization approach, chaotic sequences based on logistic map instead of random sequences is introduced to PSO for enhancing exploitation capability. In the two approaches, constrain-prior Pareto-dominance method (CPM) is proposed to meet the inequality constraints on state variables, the sorting and crowding distance methods are considered to maintain a well distributed Pareto optimal solutions, and moreover, fuzzy set theory is employed to extract the best compromise solution over the Pareto optimal curve. The proposed approaches have been examined and tested in the IEEE 30 bus and the IEEE 57 bus power systems. The performances of MOCIPSO, MOIPSO, and multi-objective PSO (MOPSO) approaches are compared with respect to multi-objective performance measures. The simulation results are promising and confirm the ability of MOCIPSO and

A multi-objective particle swarm optimization for production-distribution planning in supply chain network

Directory of Open Access Journals (Sweden)

Alireza Pourrousta

2012-04-01

Full Text Available Integrated supply chain includes different components of order, production and distribution and it plays an important role on reducing the cost of manufacturing system. In this paper, an integrated supply chain in a form of multi-objective decision-making problem is presented. The proposed model of this paper considers different parameters with uncertainty using trapezoid numbers. We first implement a ranking method to covert the fuzzy model into a crisp one and using multi-objective particle swarm optimization, we solve the resulted model. The results are compared with the performance of NSGA-II for some randomly generated problems and the preliminary results indicate that the proposed model of the paper performs better than the alternative method.

Land Use Allocation Based on a Multi-Objective Artificial Immune Optimization Model: An Application in Anlu County, China

Directory of Open Access Journals (Sweden)

Xiaoya Ma

2015-11-01

Full Text Available As the main feature of land use planning, land use allocation (LUA optimization is an important means of creating a balance between the land-use supply and demand in a region and promoting the sustainable utilization of land resources. In essence, LUA optimization is a multi-objective optimization problem under the land use supply and demand constraints in a region. In order to obtain a better sustainable multi-objective LUA optimization solution, the present study proposes a LUA model based on the multi-objective artificial immune optimization algorithm (MOAIM-LUA model. The main achievements of the present study are as follows: (a the land-use supply and demand factors are analyzed and the constraint conditions of LUA optimization problems are constructed based on the analysis framework of the balance between the land use supply and demand; (b the optimization objectives of LUA optimization problems are defined and modeled using ecosystem service value theory and land rent and price theory; and (c a multi-objective optimization algorithm is designed for solving multi-objective LUA optimization problems based on the novel immune clonal algorithm (NICA. On the basis of the aforementioned achievements, MOAIM-LUA was applied to a real case study of land-use planning in Anlu County, China. Compared to the current land use situation in Anlu County, optimized LUA solutions offer improvements in the social and ecological objective areas. Compared to the existing models, such as the non-dominated sorting genetic algorithm-II, experimental results demonstrate that the model designed in the present study can obtain better non-dominated solution sets and is superior in terms of algorithm stability.

COSMOS: Carnegie Observatories System for MultiObject Spectroscopy

Science.gov (United States)

Oemler, A.; Clardy, K.; Kelson, D.; Walth, G.; Villanueva, E.

2017-05-01

COSMOS (Carnegie Observatories System for MultiObject Spectroscopy) reduces multislit spectra obtained with the IMACS and LDSS3 spectrographs on the Magellan Telescopes. It can be used for the quick-look analysis of data at the telescope as well as for pipeline reduction of large data sets. COSMOS is based on a precise optical model of the spectrographs, which allows (after alignment and calibration) an accurate prediction of the location of spectra features. This eliminates the line search procedure which is fundamental to many spectral reduction programs, and allows a robust data pipeline to be run in an almost fully automatic mode, allowing large amounts of data to be reduced with minimal intervention.

Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi?GPU System

KAUST Repository

Charara, Ali

2014-05-04

European Extreme Large Telescope (E-ELT) is a high priority project in ground based astronomy that aims at constructing the largest telescope ever built. MOSAIC is an instrument proposed for E-ELT using Multi- Object Adaptive Optics (MOAO) technique for astronomical telescopes, which compensates for effects of atmospheric turbulence on image quality, and operates on patches across a large FoV.

Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi?GPU System

KAUST Repository

Charara, Ali; Ltaief, Hatem; Gratadour, Damien; Keyes, David E.; Sevin, Arnaud; Abdelfattah, Ahmad; Gendron, Eric; Morel, Carine; Vidal, Fabrice

2014-01-01

European Extreme Large Telescope (E-ELT) is a high priority project in ground based astronomy that aims at constructing the largest telescope ever built. MOSAIC is an instrument proposed for E-ELT using Multi- Object Adaptive Optics (MOAO) technique for astronomical telescopes, which compensates for effects of atmospheric turbulence on image quality, and operates on patches across a large FoV.

A new multi-objective reserve constrained combined heat and power dynamic economic emission dispatch

International Nuclear Information System (INIS)

Niknam, Taher; Azizipanah-Abarghooee, Rasoul; Roosta, Alireza; Amiri, Babak

2012-01-01

Combined heat and power units are playing an ever increasing role in conventional power stations due to advantages such as reduced emissions and operational cost savings. This paper investigates a more practical formulation of the complex non-convex, non-smooth and non-linear multi-objective dynamic economic emission dispatch that incorporates combined heat and power units. Integrating these types of units, and their power ramp constraints, require an efficient tool to cope with the joint characteristics of power and heat. Unlike previous approaches, the spinning reserve requirements of this system are clearly formulated in the problem. In this way, a new multi-objective optimisation based on an enhanced firefly algorithm is proposed to achieve a set of non-dominated (Pareto-optimal) solutions. A new tuning parameter based on a chaotic mechanism and novel self adaptive probabilistic mutation strategies are used to improve the overall performance of the algorithm. The numerical results demonstrate how the proposed framework was applied in real time studies. -- Highlights: ► Investigate a practical formulation of the DEED (Dynamic Economic Emission Dispatch). ► Consider combined heat and power units. ► Consider power ramp constraints. ► Consider the system spinning reserve requirements. ► Present a new multi-objective optimization firefly.

Multi-objective and multi-physics optimization methodology for SFR core: application to CFV concept

International Nuclear Information System (INIS)

Fabbris, Olivier

2014-01-01

Nuclear reactor core design is a highly multidisciplinary task where neutronics, thermal-hydraulics, fuel thermo-mechanics and fuel cycle are involved. The problem is moreover multi-objective (several performances) and highly dimensional (several tens of design parameters).As the reference deterministic calculation codes for core characterization require important computing resources, the classical design method is not well suited to investigate and optimize new innovative core concepts. To cope with these difficulties, a new methodology has been developed in this thesis. Our work is based on the development and validation of simplified neutronics and thermal-hydraulics calculation schemes allowing the full characterization of Sodium-cooled Fast Reactor core regarding both neutronics performances and behavior during thermal hydraulic dimensioning transients.The developed methodology uses surrogate models (or meta-models) able to replace the neutronics and thermal-hydraulics calculation chain. Advanced mathematical methods for the design of experiment, building and validation of meta-models allows substituting this calculation chain by regression models with high prediction capabilities.The methodology is applied on a very large design space to a challenging core called CFV (French acronym for low void effect core) with a large gain on the sodium void effect. Global sensitivity analysis leads to identify the significant design parameters on the core design and its behavior during unprotected transient which can lead to severe accidents. Multi-objective optimizations lead to alternative core configurations with significantly improved performances. Validation results demonstrate the relevance of the methodology at the pre-design stage of a Sodium-cooled Fast Reactor core. (author) [fr

Multi-objective Optimization of a Solar Humidification Dehumidification Desalination Unit

Science.gov (United States)

Rafigh, M.; Mirzaeian, M.; Najafi, B.; Rinaldi, F.; Marchesi, R.

2017-11-01

In the present paper, a humidification-dehumidification desalination unit integrated with solar system is considered. In the first step mathematical model of the whole plant is represented. Next, taking into account the logical constraints, the performance of the system is optimized. On one hand it is desired to have higher energetic efficiency, while on the other hand, higher efficiency results in an increment in the required area for each subsystem which consequently leads to an increase in the total cost of the plant. In the present work, the optimum solution is achieved when the specific energy of the solar heater and also the areas of humidifier and dehumidifier are minimized. Due to the fact that considered objective functions are in conflict, conventional optimization methods are not applicable. Hence, multi objective optimization using genetic algorithm which is an efficient tool for dealing with problems with conflicting objectives has been utilized and a set of optimal solutions called Pareto front each of which is a tradeoff between the mentioned objectives is generated.

Determination of radial profile of ICF hot spot's state by multi-objective parameters optimization

International Nuclear Information System (INIS)

Dong Jianjun; Deng Bo; Cao Zhurong; Ding Yongkun; Jiang Shaoen

2014-01-01

A method using multi-objective parameters optimization is presented to determine the radial profile of hot spot temperature and density. And a parameter space which contain five variables: the temperatures at center and the interface of fuel and remain ablator, the maximum model density of remain ablator, the mass ratio of remain ablator to initial ablator and the position of interface between fuel and the remain ablator, is used to described the hot spot radial temperature and density. Two objective functions are set as the variances of normalized intensity profile from experiment X-ray images and the theory calculation. Another objective function is set as the variance of experiment average temperature of hot spot and the average temperature calculated by theoretical model. The optimized parameters are obtained by multi-objective genetic algorithm searching for the five dimension parameter space, thereby the optimized radial temperature and density profiles can be determined. The radial temperature and density profiles of hot spot by experiment data measured by KB microscope cooperating with X-ray film are presented. It is observed that the temperature profile is strongly correlated to the objective functions. (authors)

[Location selection for Shenyang urban parks based on GIS and multi-objective location allocation model].

Science.gov (United States)

Zhou, Yuan; Shi, Tie-Mao; Hu, Yuan-Man; Gao, Chang; Liu, Miao; Song, Lin-Qi

2011-12-01

Based on geographic information system (GIS) technology and multi-objective location-allocation (LA) model, and in considering of four relatively independent objective factors (population density level, air pollution level, urban heat island effect level, and urban land use pattern), an optimized location selection for the urban parks within the Third Ring of Shenyang was conducted, and the selection results were compared with the spatial distribution of existing parks, aimed to evaluate the rationality of the spatial distribution of urban green spaces. In the location selection of urban green spaces in the study area, the factor air pollution was most important, and, compared with single objective factor, the weighted analysis results of multi-objective factors could provide optimized spatial location selection of new urban green spaces. The combination of GIS technology with LA model would be a new approach for the spatial optimizing of urban green spaces.

Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction

International Nuclear Information System (INIS)

Dong, Feifei; Liu, Yong; Su, Han; Zou, Rui; Guo, Huaicheng

2015-01-01

Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely “optimal” solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multi-objective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions. - Highlights: • Reliability-oriented multi-objective (ROMO) optimal decision approach was proposed. • The approach can avoid specifying reliability levels prior to optimization modeling. • Multiple reliability objectives can be systematically balanced using Pareto fronts. • Neural network model was used to

Reliability-oriented multi-objective optimal decision-making approach for uncertainty-based watershed load reduction

Energy Technology Data Exchange (ETDEWEB)

Dong, Feifei [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Liu, Yong, E-mail: yongliu@pku.edu.cn [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Institute of Water Sciences, Peking University, Beijing 100871 (China); Su, Han [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China); Zou, Rui [Tetra Tech, Inc., 10306 Eaton Place, Ste 340, Fairfax, VA 22030 (United States); Yunnan Key Laboratory of Pollution Process and Management of Plateau Lake-Watershed, Kunming 650034 (China); Guo, Huaicheng [College of Environmental Science and Engineering, Key Laboratory of Water and Sediment Sciences (MOE), Peking University, Beijing 100871 (China)

2015-05-15

Water quality management and load reduction are subject to inherent uncertainties in watershed systems and competing decision objectives. Therefore, optimal decision-making modeling in watershed load reduction is suffering due to the following challenges: (a) it is difficult to obtain absolutely “optimal” solutions, and (b) decision schemes may be vulnerable to failure. The probability that solutions are feasible under uncertainties is defined as reliability. A reliability-oriented multi-objective (ROMO) decision-making approach was proposed in this study for optimal decision making with stochastic parameters and multiple decision reliability objectives. Lake Dianchi, one of the three most eutrophic lakes in China, was examined as a case study for optimal watershed nutrient load reduction to restore lake water quality. This study aimed to maximize reliability levels from considerations of cost and load reductions. The Pareto solutions of the ROMO optimization model were generated with the multi-objective evolutionary algorithm, demonstrating schemes representing different biases towards reliability. The Pareto fronts of six maximum allowable emission (MAE) scenarios were obtained, which indicated that decisions may be unreliable under unpractical load reduction requirements. A decision scheme identification process was conducted using the back propagation neural network (BPNN) method to provide a shortcut for identifying schemes at specific reliability levels for decision makers. The model results indicated that the ROMO approach can offer decision makers great insights into reliability tradeoffs and can thus help them to avoid ineffective decisions. - Highlights: • Reliability-oriented multi-objective (ROMO) optimal decision approach was proposed. • The approach can avoid specifying reliability levels prior to optimization modeling. • Multiple reliability objectives can be systematically balanced using Pareto fronts. • Neural network model was used to

Performance of PC-based charged particle multi-channel spectrometer utilising particle identification

International Nuclear Information System (INIS)

Palla, G.; Sziklai, J.; Trajber, Cs.

1993-12-01

A collaterally expandable charged particle spectrometer based on PC control and particle identification is described. A typical system configuration consisting of two channels are used to test the system performance. (author) 7 refs.; 5 figs

Multi-objective optimisation for spacecraft design for demise and survivability

OpenAIRE

Trisolini, Mirko; Colombo, Camilla; Lewis, Hugh

2017-01-01

The paper presents the development of a multi-objective optimisation framework to study the effects that preliminary design choices have on the demisability and the survivability of a spacecraft. Building a spacecraft such that most of it will demise during the re-entry through design-for-demise strategies may lead to design that are more vulnerable to space debris impacts, thus compromising the reliability of the mission. The two models developed to analyse the demisability and the survivabi...

Multi-objective Calibration of DHSVM Based on Hydrologic Key Elements in Jinhua River Basin, East China

Science.gov (United States)

Pan, S.; Liu, L.; Xu, Y. P.

2017-12-01

Abstract: In physically based distributed hydrological model, large number of parameters, representing spatial heterogeneity of watershed and various processes in hydrologic cycle, are involved. For lack of calibration module in Distributed Hydrology Soil Vegetation Model, this study developed a multi-objective calibration module using Epsilon-Dominance Non-Dominated Sorted Genetic Algorithm II (ɛ-NSGAII) and based on parallel computing of Linux cluster for DHSVM (ɛP-DHSVM). In this study, two hydrologic key elements (i.e., runoff and evapotranspiration) are used as objectives in multi-objective calibration of model. MODIS evapotranspiration obtained by SEBAL is adopted to fill the gap of lack of observation for evapotranspiration. The results show that good performance of runoff simulation in single objective calibration cannot ensure good simulation performance of other hydrologic key elements. Self-developed ɛP-DHSVM model can make multi-objective calibration more efficiently and effectively. The running speed can be increased by more than 20-30 times via applying ɛP-DHSVM. In addition, runoff and evapotranspiration can be simulated very well simultaneously by ɛP-DHSVM, with superior values for two efficiency coefficients (0.74 for NS of runoff and 0.79 for NS of evapotranspiration, -10.5% and -8.6% for PBIAS of runoff and evapotranspiration respectively).

Multi-objective optimization of Stirling engine using Finite Physical Dimensions Thermodynamics (FPDT) method

International Nuclear Information System (INIS)

Li, Ruijie; Grosu, Lavinia; Queiros-Conde, Diogo

2016-01-01

Highlights: • A gamma Stirling engine has been optimized using FPDT method by multi-objective criteria. • Genetic algorithm and decision making methods were used to get Pareto frontier and optimum points. • It shows: total thermal conductance, hot temperature, stroke and diameter ratios can be improved. - Abstract: In this paper, a solar energy powered gamma type SE has been optimized using Finite Physical Dimensions Thermodynamics (FPDT) method by multi-objective criteria. Genetic algorithm was used to get the Pareto frontier, and optimum points were obtained using the decision making methods of LINMAP and TOPSIS. The optimization results have been compared with those obtained using the ecological method. It was shown that the multi-objective optimization in this paper has a better balance among the optimizing criteria (maximum mechanical power, maximum thermal efficiency and minimum entropy generation flow). The effects of the hot source temperature and the total thermal conductance of the engine on the Pareto frontier have been also studied. This sensibility study shows that an increase in the hot reservoir temperature can increase the output mechanical power, the thermal efficiency of the engine, but also the entropy generation rate. In addition to this, an increase of the total thermal conductance of the engine can strongly increase the output mechanical power and only slightly increase the thermal efficiency. These results allow us to improve the engine performance after some modifications as geometrical dimensions (diameter, stroke, heat exchange surface, etc.) and physical parameters (temperature, thermal conductivity).

Incorporating social benefits in multi-objective optimization of forest-based bioenergy and biofuel supply chains

International Nuclear Information System (INIS)

Cambero, Claudia; Sowlati, Taraneh

2016-01-01

Highlights: • Quantified social benefits of forest- based biomass supply chain. • Developed multi-objective optimization model. • Incorporated social benefits into multi-objective model. • Solved the model using the AUGMECON method. • Applied the model to a case study in Canada. - Abstract: Utilization of forest and wood residues to produce bioenergy and biofuels could generate additional revenue streams for forestry companies, reduce their environmental impacts and generate new development opportunities for forest-dependent communities. Further development of forest-based biorefineries entails addressing complexities and challenges related to biomass procurement, logistics, technologies, and sustainability. Numerous optimization models have been proposed for the economic and environmental design of biomass-to-bioenergy or biofuel supply chains. A few of them also maximized the job creation potential of the supply chain through the use of employment multipliers. The use of a total job creation indicator as the social optimization objective implies that all new jobs generate the same level of social benefit. In this paper, we quantify the potential social benefit of new forest-based biorefinery supply chains considering different impacts of new jobs based on their type and location. This social benefit is incorporated into a multi-objective mixed integer linear programming model that maximizes the social benefit, net present value and greenhouse gas emission saving potential of a forest-based biorefinery supply chain. The applicability of the model is illustrated through a case study in the interior region of British Columbia, Canada where different utilization paths for available forest and wood residues are investigated. The multi-objective optimization model is solved using a Pareto-generating method. The analysis of the generated set of Pareto-optimal solutions show a trade-off between the net present value of the supply chain and the other two

Performance of a genetic algorithm for solving the multi-objective, multimodel transportation network design problem

NARCIS (Netherlands)

Brands, Ties; van Berkum, Eric C.

2014-01-01

The optimization of infrastructure planning in a multimodal network is defined as a multi-objective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. Decision variables are the location of park and ride facilities, train

High resolution Thomson Parabola Spectrometer for full spectral capture of multi-species ion beams

International Nuclear Information System (INIS)

Alejo, A.; Kar, S.; Ahmed, H.; Doria, D.; Borghesi, M.; Tebartz, A.; Ding, J.; Neumann, N.; Astbury, S.; Carroll, D. C.; Scott, G. G.; Higginson, A.; McKenna, P.; Wagner, F.; Roth, M.

2016-01-01

We report on the experimental characterisation of laser-driven ion beams using a Thomson Parabola Spectrometer (TPS) equipped with trapezoidally shaped electric plates, proposed by Gwynne et al. [Rev. Sci. Instrum. 85, 033304 (2014)]. While a pair of extended (30 cm long) electric plates was able to produce a significant increase in the separation between neighbouring ion species at high energies, deploying a trapezoidal design circumvented the spectral clipping at the low energy end of the ion spectra. The shape of the electric plate was chosen carefully considering, for the given spectrometer configuration, the range of detectable ion energies and species. Analytical tracing of the ion parabolas matches closely with the experimental data, which suggests a minimal effect of fringe fields on the escaping ions close to the wedged edge of the electrode. The analytical formulae were derived considering the relativistic correction required for the high energy ions to be characterised using such spectrometer.

High resolution Thomson Parabola Spectrometer for full spectral capture of multi-species ion beams

Energy Technology Data Exchange (ETDEWEB)

Alejo, A.; Kar, S., E-mail: s.kar@qub.ac.uk; Ahmed, H.; Doria, D.; Borghesi, M. [Centre for Plasma Physics, School of Mathematics and Physics, Queen’s University Belfast, Belfast BT7 1NN (United Kingdom); Tebartz, A.; Ding, J.; Neumann, N. [Institut für Kernphysik, Technische Universität Darmstadt, Schloßgartenstrasse 9, D-64289 Darmstadt (Germany); Astbury, S.; Carroll, D. C.; Scott, G. G. [Central Laser Facility, Rutherford Appleton Laboratory, Didcot, Oxfordshire OX11 0QX (United Kingdom); Higginson, A.; McKenna, P. [Department of Physics, SUPA, University of Strathclyde, Glasgow G4 0NG (United Kingdom); Wagner, F. [GSI Helmholtzzentrum für Schwerionenforschung GmbH, Planckstraße 1, 64291 Darmstadt (Germany); Roth, M. [Institut für Kernphysik, Technische Universität Darmstadt, Schloßgartenstraße 9, D-64289 Darmstadt (Germany)

2016-08-15

We report on the experimental characterisation of laser-driven ion beams using a Thomson Parabola Spectrometer (TPS) equipped with trapezoidally shaped electric plates, proposed by Gwynne et al. [Rev. Sci. Instrum. 85, 033304 (2014)]. While a pair of extended (30 cm long) electric plates was able to produce a significant increase in the separation between neighbouring ion species at high energies, deploying a trapezoidal design circumvented the spectral clipping at the low energy end of the ion spectra. The shape of the electric plate was chosen carefully considering, for the given spectrometer configuration, the range of detectable ion energies and species. Analytical tracing of the ion parabolas matches closely with the experimental data, which suggests a minimal effect of fringe fields on the escaping ions close to the wedged edge of the electrode. The analytical formulae were derived considering the relativistic correction required for the high energy ions to be characterised using such spectrometer.

High resolution Thomson Parabola Spectrometer for full spectral capture of multi-species ion beams

Science.gov (United States)

Alejo, A.; Kar, S.; Tebartz, A.; Ahmed, H.; Astbury, S.; Carroll, D. C.; Ding, J.; Doria, D.; Higginson, A.; McKenna, P.; Neumann, N.; Scott, G. G.; Wagner, F.; Roth, M.; Borghesi, M.

2016-08-01

We report on the experimental characterisation of laser-driven ion beams using a Thomson Parabola Spectrometer (TPS) equipped with trapezoidally shaped electric plates, proposed by Gwynne et al. [Rev. Sci. Instrum. 85, 033304 (2014)]. While a pair of extended (30 cm long) electric plates was able to produce a significant increase in the separation between neighbouring ion species at high energies, deploying a trapezoidal design circumvented the spectral clipping at the low energy end of the ion spectra. The shape of the electric plate was chosen carefully considering, for the given spectrometer configuration, the range of detectable ion energies and species. Analytical tracing of the ion parabolas matches closely with the experimental data, which suggests a minimal effect of fringe fields on the escaping ions close to the wedged edge of the electrode. The analytical formulae were derived considering the relativistic correction required for the high energy ions to be characterised using such spectrometer.

Spherical grating spectrometers

Science.gov (United States)

O'Donoghue, Darragh; Clemens, J. Christopher

2014-07-01

We describe designs for spectrometers employing convex dispersers. The Offner spectrometer was the first such instrument; it has almost exclusively been employed on satellite platforms, and has had little impact on ground-based instruments. We have learned how to fabricate curved Volume Phase Holographic (VPH) gratings and, in contrast to the planar gratings of traditional spectrometers, describe how such devices can be used in optical/infrared spectrometers designed specifically for curved diffraction gratings. Volume Phase Holographic gratings are highly efficient compared to conventional surface relief gratings; they have become the disperser of choice in optical / NIR spectrometers. The advantage of spectrometers with curved VPH dispersers is the very small number of optical elements used (the simplest comprising a grating and a spherical mirror), as well as illumination of mirrors off axis, resulting in greater efficiency and reduction in size. We describe a "Half Offner" spectrometer, an even simpler version of the Offner spectrometer. We present an entirely novel design, the Spherical Transmission Grating Spectrometer (STGS), and discuss exemplary applications, including a design for a double-beam spectrometer without any requirement for a dichroic. This paradigm change in spectrometer design offers an alternative to all-refractive astronomical spectrometer designs, using expensive, fragile lens elements fabricated from CaF2 or even more exotic materials. The unobscured mirror layout avoids a major drawback of the previous generation of catadioptric spectrometer designs. We describe laboratory measurements of the efficiency and image quality of a curved VPH grating in a STGS design, demonstrating, simultaneously, efficiency comparable to planar VPH gratings along with good image quality. The stage is now set for construction of a prototype instrument with impressive performance.

Multi-objective scheduling of electric vehicles in smart distribution system

International Nuclear Information System (INIS)

Zakariazadeh, Alireza; Jadid, Shahram; Siano, Pierluigi

2014-01-01

Highlights: • Environmental/economic operational scheduling of electric vehicles. • The Vehicle to Grid capability and the actual patterns of drivers are considered. • A novel conceptual model for an electric vehicle management system is proposed. - Abstract: When preparing for the widespread adoption of Electric Vehicles (EVs), an important issue is to use a proper EVs’ charging/discharging scheduling model that is able to simultaneously consider economic and environmental goals as well as technical constraints of distribution networks. This paper proposes a multi-objective operational scheduling method for charging/discharging of EVs in a smart distribution system. The proposed multi-objective framework, based on augmented ε-constraint method, aims at minimizing the total operational costs and emissions. The Vehicle to Grid (V2G) capability as well as the actual patterns of drivers are considered in order to generate the Pareto-optimal solutions. The Benders decomposition technique is used in order to solve the proposed optimization model and to convert the large scale mixed integer nonlinear problem into mixed-integer linear programming and nonlinear programming problems. The effectiveness of the proposed resources scheduling approach is tested on a 33-bus distribution test system over a 24-h period. The results show that the proposed EVs’ charging/discharging method can reduce both of operation cost and air pollutant emissions

Multi-Objective Aerodynamic and Structural Optimization of Horizontal-Axis Wind Turbine Blades

Directory of Open Access Journals (Sweden)

Jie Zhu

2017-01-01

Full Text Available A procedure based on MATLAB combined with ANSYS is presented and utilized for the multi-objective aerodynamic and structural optimization of horizontal-axis wind turbine (HAWT blades. In order to minimize the cost of energy (COE and improve the overall performance of the blades, materials of carbon fiber reinforced plastic (CFRP combined with glass fiber reinforced plastic (GFRP are applied. The maximum annual energy production (AEP, the minimum blade mass and the minimum blade cost are taken as three objectives. Main aerodynamic and structural characteristics of the blades are employed as design variables. Various design requirements including strain, deflection, vibration and buckling limits are taken into account as constraints. To evaluate the aerodynamic performances and the structural behaviors, the blade element momentum (BEM theory and the finite element method (FEM are applied in the procedure. Moreover, the non-dominated sorting genetic algorithm (NSGA II, which constitutes the core of the procedure, is adapted for the multi-objective optimization of the blades. To prove the efficiency and reliability of the procedure, a commercial 1.5 MW HAWT blade is used as a case study, and a set of trade-off solutions is obtained. Compared with the original scheme, the optimization results show great improvements for the overall performance of the blade.

Multi-objective robust optimization method for the modified epoxy resin sheet molding compounds of the impeller

Directory of Open Access Journals (Sweden)

Xiaozhang Qu

2016-07-01

Full Text Available A kind of modified epoxy resin sheet molding compounds of the impeller has been designed. Through the test, the non-metal impeller has a better environmental aging performance, but must do the waterproof processing design. In order to improve the stability of the impeller vibration design, the influence of uncertainty factors is considered, and a multi-objective robust optimization method is proposed to reduce the weight of the impeller. Firstly, based on the fluid-structure interaction，the analysis model of the impeller vibration is constructed. Secondly, the optimal approximate model of the impeller is constructed by using the Latin hypercube and radial basis function, and the fitting and optimization accuracy of the approximate model is improved by increasing the sample points. Finally, the micro multi-objective genetic algorithm is applied to the robust optimization of approximate model, and the Monte Carlo simulation and Sobol sampling techniques are used for reliability analysis. By comparing the results of the deterministic, different sigma levels and different materials, the multi-objective optimization of the SMC molding impeller can meet the requirements of engineering stability and lightweight. And the effectiveness of the proposed multi-objective robust optimization method is verified by the error analysis. After the SMC molding and the robust optimization of the impeller, the optimized rate reached 42.5%, which greatly improved the economic benefit, and greatly reduce the vibration of the ventilation system.

Multi-objective random search algorithm for simultaneously optimizing wind farm layout and number of turbines

DEFF Research Database (Denmark)

Feng, Ju; Shen, Wen Zhong; Xu, Chang

2016-01-01

A new algorithm for multi-objective wind farm layout optimization is presented. It formulates the wind turbine locations as continuous variables and is capable of optimizing the number of turbines and their locations in the wind farm simultaneously. Two objectives are considered. One is to maximi...

Multi-objective unit commitment with wind penetration and emission concerns under stochastic and fuzzy uncertainties

International Nuclear Information System (INIS)

Wang, Bo; Wang, Shuming; Zhou, Xianzhong; Watada, Junzo

2016-01-01

Recent years have witnessed the ever increasing renewable penetration in power generation systems, which entails modern unit commitment problems with modelling and computation burdens. This study aims to simulate the impacts of manifold uncertainties on system operation with emission concerns. First, probability theory and fuzzy set theory are applied to jointly represent the uncertainties such as wind generation, load fluctuation and unit outage that interleaved in unit commitment problems. Second, a Value-at-Risk-based multi-objective approach is developed as a bridge of existing stochastic and robust unit commitment optimizations, which not only captures the inherent conflict between operation cost and supply reliability, but also provides easy-to-adjust robustness against worst-case scenarios. Third, a multi-objective algorithm that integrates fuzzy simulation and particle swarm optimization is developed to achieve approximate Pareto-optimal solutions. The research effectiveness is exemplified by two case studies: The comparison between test systems with and without generation uncertainty demonstrates that this study is practicable and can suggest operational insights of generation mix systems. The sensitivity analysis on Value-at-Risk proves that our method can achieve adequate tradeoff between performance optimality and robustness, thus help system operators in making informed decisions. Finally, the model and algorithm comparisons also justify the superiority of this research. - Highlights: • Probability theory and fuzzy set theory are used to describe different uncertainties. • A Value-at-Risk-based multi-objective unit commitment model is proposed. • An improved multi-objective particle swarm optimization algorithm is developed. • The model achieves adequate trade-off between performance optimality and robustness. • The algorithm can obtain convergent and diversified Pareto fronts.

An Adaptive Multi-Objective Particle Swarm Optimization Algorithm for Multi-Robot Path Planning

Directory of Open Access Journals (Sweden)

Nizar Hadi Abbas

2016-07-01

Full Text Available This paper discusses an optimal path planning algorithm based on an Adaptive Multi-Objective Particle Swarm Optimization Algorithm (AMOPSO for two case studies. First case, single robot wants to reach a goal in the static environment that contain two obstacles and two danger source. The second one, is improving the ability for five robots to reach the shortest way. The proposed algorithm solves the optimization problems for the first case by finding the minimum distance from initial to goal position and also ensuring that the generated path has a maximum distance from the danger zones. And for the second case, finding the shortest path for every robot and without any collision between them with the shortest time. In order to evaluate the proposed algorithm in term of finding the best solution, six benchmark test functions are used to make a comparison between AMOPSO and the standard MOPSO. The results show that the AMOPSO has a better ability to get away from local optimums with a quickest convergence than the MOPSO. The simulation results using Matlab 2014a, indicate that this methodology is extremely valuable for every robot in multi-robot framework to discover its own particular proper pa‌th from the start to the destination position with minimum distance and time.

Solving binary-state multi-objective reliability redundancy allocation series-parallel problem using efficient epsilon-constraint, multi-start partial bound enumeration algorithm, and DEA

International Nuclear Information System (INIS)

Khalili-Damghani, Kaveh; Amiri, Maghsoud

2012-01-01

In this paper, a procedure based on efficient epsilon-constraint method and data envelopment analysis (DEA) is proposed for solving binary-state multi-objective reliability redundancy allocation series-parallel problem (MORAP). In first module, a set of qualified non-dominated solutions on Pareto front of binary-state MORAP is generated using an efficient epsilon-constraint method. In order to test the quality of generated non-dominated solutions in this module, a multi-start partial bound enumeration algorithm is also proposed for MORAP. The performance of both procedures is compared using different metrics on well-known benchmark instance. The statistical analysis represents that not only the proposed efficient epsilon-constraint method outperform the multi-start partial bound enumeration algorithm but also it improves the founded upper bound of benchmark instance. Then, in second module, a DEA model is supplied to prune the generated non-dominated solutions of efficient epsilon-constraint method. This helps reduction of non-dominated solutions in a systematic manner and eases the decision making process for practical implementations. - Highlights: ► A procedure based on efficient epsilon-constraint method and DEA was proposed for solving MORAP. ► The performance of proposed procedure was compared with a multi-start PBEA. ► Methods were statistically compared using multi-objective metrics.

Low emittance lattice optimization using a multi-objective evolutionary algorithm

International Nuclear Information System (INIS)

Gao Weiwei; Wang Lin; Li Weimin; He Duohui

2011-01-01

A low emittance lattice design and optimization procedure are systematically studied with a non-dominated sorting-based multi-objective evolutionary algorithm which not only globally searches the low emittance lattice, but also optimizes some beam quantities such as betatron tunes, momentum compaction factor and dispersion function simultaneously. In this paper the detailed algorithm and lattice design procedure are presented. The Hefei light source upgrade project storage ring lattice, with fixed magnet layout, is designed to illustrate this optimization procedure. (authors)

MOONS: a multi-object optical and near-infrared spectrograph for the VLT

NARCIS (Netherlands)

Cirasuolo, M.; Afonso, J.; Bender, R.; Bonifacio, P.; Evans, C.; Kaper, L.; Oliva, Ernesto; Vanzi, Leonardo; Abreu, Manuel; Atad-Ettedgui, Eli; Babusiaux, Carine; Bauer, Franz E.; Best, Philip; Bezawada, Naidu; Bryson, Ian R.; Cabral, Alexandre; Caputi, Karina; Centrone, Mauro; Chemla, Fanny; Cimatti, Andrea; Cioni, Maria-Rosa; Clementini, Gisella; Coelho, João.; Daddi, Emanuele; Dunlop, James S.; Feltzing, Sofia; Ferguson, Annette; Flores, Hector; Fontana, Adriano; Fynbo, Johan; Garilli, Bianca; Glauser, Adrian M.; Guinouard, Isabelle; Hammer, Jean-François; Hastings, Peter R.; Hess, Hans-Joachim; Ivison, Rob J.; Jagourel, Pascal; Jarvis, Matt; Kauffman, G.; Lawrence, A.; Lee, D.; Li Causi, G.; Lilly, S.; Lorenzetti, D.; Maiolino, R.; Mannucci, F.; McLure, R.; Minniti, D.; Montgomery, D.; Muschielok, B.; Nandra, K.; Navarro, R.; Norberg, P.; Origlia, L.; Padilla, N.; Peacock, J.; Pedicini, F.; Pentericci, L.; Pragt, J.; Puech, M.; Randich, S.; Renzini, A.; Ryde, N.; Rodrigues, M.; Royer, F.; Saglia, R.; Sánchez, A.; Schnetler, H.; Sobral, D.; Speziali, R.; Todd, S.; Tolstoy, E.; Torres, M.; Venema, L.; Vitali, F.; Wegner, M.; Wells, M.; Wild, V.; Wright, G.

MOONS is a new conceptual design for a Multi-Object Optical and Near-infrared Spectrograph for the Very Large Telescope (VLT), selected by ESO for a Phase A study. The baseline design consists of ~1000 fibers deployable over a field of view of ~500 square arcmin, the largest patrol field offered by

Effective mass trigger at the Brookhaven Multi-Particle Spectrometer (MPS)

International Nuclear Information System (INIS)

Willen, E.H.

1980-01-01

An effective mass trigger for use at the Brookhaven Multiparticle Spectrometer (MPS) is described. It is a microprocessor based device using extensive fast memory attached to proportional wire chambers in the MPS magnetic field. It will select kinematic quantities unique to the reaction being studied, thereby permitting higher sensitivities and a reduction in data-processing cost for MPS experiments. The principles of operation for this trigger, and the results of simulations to assess its performance, are presented

Multi-Objective Optimization for Solid Amine CO2 Removal Assembly in Manned Spacecraft

Directory of Open Access Journals (Sweden)

Rong A

2017-07-01

Full Text Available Carbon Dioxide Removal Assembly (CDRA is one of the most important systems in the Environmental Control and Life Support System (ECLSS for a manned spacecraft. With the development of adsorbent and CDRA technology, solid amine is increasingly paid attention due to its obvious advantages. However, a manned spacecraft is launched far from the Earth, and its resources and energy are restricted seriously. These limitations increase the design difficulty of solid amine CDRA. The purpose of this paper is to seek optimal design parameters for the solid amine CDRA. Based on a preliminary structure of solid amine CDRA, its heat and mass transfer models are built to reflect some features of the special solid amine adsorbent, Polyethylenepolyamine adsorbent. A multi-objective optimization for the design of solid amine CDRA is discussed further in this paper. In this study, the cabin CO2 concentration, system power consumption and entropy production are chosen as the optimization objectives. The optimization variables consist of adsorption cycle time, solid amine loading mass, adsorption bed length, power consumption and system entropy production. The Improved Non-dominated Sorting Genetic Algorithm (NSGA-II is used to solve this multi-objective optimization and to obtain optimal solution set. A design example of solid amine CDRA in a manned space station is used to show the optimal procedure. The optimal combinations of design parameters can be located on the Pareto Optimal Front (POF. Finally, Design 971 is selected as the best combination of design parameters. The optimal results indicate that the multi-objective optimization plays a significant role in the design of solid amine CDRA. The final optimal design parameters for the solid amine CDRA can guarantee the cabin CO2 concentration within the specified range, and also satisfy the requirements of lightweight and minimum energy consumption.

Availability allocation to repairable systems with genetic algorithms: a multi-objective formulation

International Nuclear Information System (INIS)

Elegbede, Charles; Adjallah, Kondo

2003-01-01

This paper describes a methodology based on genetic algorithms (GA) and experiments plan to optimize the availability and the cost of reparable parallel-series systems. It is a NP-hard problem of multi-objective combinatorial optimization, modeled with continuous and discrete variables. By using the weighting technique, the problem is transformed into a single-objective optimization problem whose constraints are then relaxed by the exterior penalty technique. We then propose a search of solution through GA, whose parameters are adjusted using experiments plan technique. A numerical example is used to assess the method

Accelerating solving the dynamic multi-objective nework design problem using response surface methods

NARCIS (Netherlands)

Wismans, Luc Johannes Josephus; van Berkum, Eric C.; Bliemer, Michiel C.J.; Viti, F.; Immers, B.; Tampere, C.

2011-01-01

Multi objective optimization of externalities of traffic solving a network design problem in which Dynamic Traffic Management measures are used, is time consuming while heuristics are needed and solving the lower level requires solving the dynamic user equilibrium problem. Use of response surface

A Multi-objective Optimization Application in Friction Stir Welding: Considering Thermo-mechanical Aspects

DEFF Research Database (Denmark)

Tutum, Cem Celal; Hattel, Jesper Henri

2010-01-01

speed and traverse welding speed have been sought in order to achieve the goals mentioned above using an evolutionary multi-objective optimization (MOO) algorithm, i.e. non-dominated sorting genetic algorithm (NSGA-II), integrated with a transient, 2-dimensional sequentially coupled thermomechanical...

An object-oriented and quadrilateral-mesh based solution adaptive algorithm for compressible multi-fluid flows

Science.gov (United States)

Zheng, H. W.; Shu, C.; Chew, Y. T.

2008-07-01

In this paper, an object-oriented and quadrilateral-mesh based solution adaptive algorithm for the simulation of compressible multi-fluid flows is presented. The HLLC scheme (Harten, Lax and van Leer approximate Riemann solver with the Contact wave restored) is extended to adaptively solve the compressible multi-fluid flows under complex geometry on unstructured mesh. It is also extended to the second-order of accuracy by using MUSCL extrapolation. The node, edge and cell are arranged in such an object-oriented manner that each of them inherits from a basic object. A home-made double link list is designed to manage these objects so that the inserting of new objects and removing of the existing objects (nodes, edges and cells) are independent of the number of objects and only of the complexity of O( 1). In addition, the cells with different levels are further stored in different lists. This avoids the recursive calculation of solution of mother (non-leaf) cells. Thus, high efficiency is obtained due to these features. Besides, as compared to other cell-edge adaptive methods, the separation of nodes would reduce the memory requirement of redundant nodes, especially in the cases where the level number is large or the space dimension is three. Five two-dimensional examples are used to examine its performance. These examples include vortex evolution problem, interface only problem under structured mesh and unstructured mesh, bubble explosion under the water, bubble-shock interaction, and shock-interface interaction inside the cylindrical vessel. Numerical results indicate that there is no oscillation of pressure and velocity across the interface and it is feasible to apply it to solve compressible multi-fluid flows with large density ratio (1000) and strong shock wave (the pressure ratio is 10,000) interaction with the interface.

Multi-Objective Design Optimization of an Over-Constrained Flexure-Based Amplifier

Directory of Open Access Journals (Sweden)

Yuan Ni

2015-07-01

Full Text Available The optimizing design for enhancement of the micro performance of manipulator based on analytical models is investigated in this paper. By utilizing the established uncanonical linear homogeneous equations, the quasi-static analytical model of the micro-manipulator is built, and the theoretical calculation results are tested by FEA simulations. To provide a theoretical basis for a micro-manipulator being used in high-precision engineering applications, this paper investigates the modal property based on the analytical model. Based on the finite element method, with multipoint constraint equations, the model is built and the results have a good match with the simulation. The following parametric influences studied show that the influences of other objectives on one objective are complicated.  Consequently, the multi-objective optimization by the derived analytical models is carried out to find out the optimal solutions of the manipulator. Besides the inner relationships among these design objectives during the optimization process are discussed.

Combining multi-objective optimization and bayesian model averaging to calibrate forecast ensembles of soil hydraulic models

Energy Technology Data Exchange (ETDEWEB)

Vrugt, Jasper A [Los Alamos National Laboratory; Wohling, Thomas [NON LANL

2008-01-01

Most studies in vadose zone hydrology use a single conceptual model for predictive inference and analysis. Focusing on the outcome of a single model is prone to statistical bias and underestimation of uncertainty. In this study, we combine multi-objective optimization and Bayesian Model Averaging (BMA) to generate forecast ensembles of soil hydraulic models. To illustrate our method, we use observed tensiometric pressure head data at three different depths in a layered vadose zone of volcanic origin in New Zealand. A set of seven different soil hydraulic models is calibrated using a multi-objective formulation with three different objective functions that each measure the mismatch between observed and predicted soil water pressure head at one specific depth. The Pareto solution space corresponding to these three objectives is estimated with AMALGAM, and used to generate four different model ensembles. These ensembles are post-processed with BMA and used for predictive analysis and uncertainty estimation. Our most important conclusions for the vadose zone under consideration are: (1) the mean BMA forecast exhibits similar predictive capabilities as the best individual performing soil hydraulic model, (2) the size of the BMA uncertainty ranges increase with increasing depth and dryness in the soil profile, (3) the best performing ensemble corresponds to the compromise (or balanced) solution of the three-objective Pareto surface, and (4) the combined multi-objective optimization and BMA framework proposed in this paper is very useful to generate forecast ensembles of soil hydraulic models.

A hybrid input–output multi-objective model to assess economic–energy–environment trade-offs in Brazil

International Nuclear Information System (INIS)

Carvalho, Ariovaldo Lopes de; Antunes, Carlos Henggeler; Freire, Fausto; Henriques, Carla Oliveira

2015-01-01

A multi-objective linear programming (MOLP) model based on a hybrid Input–Output (IO) framework is presented. This model aims at assessing the trade-offs between economic, energy, environmental (E3) and social objectives in the Brazilian economic system. This combination of multi-objective models with Input–Output Analysis (IOA) plays a supplementary role in understanding the interactions between the economic and energy systems, and the corresponding impacts on the environment, offering a consistent framework for assessing the effects of distinct policies on these systems. Firstly, the System of National Accounts (SNA) is reorganized to include the National Energy Balance, creating a hybrid IO framework that is extended to assess Greenhouse Gas (GHG) emissions and the employment level. The objective functions considered are the maximization of GDP (gross domestic product) and employment levels, as well as the minimization of energy consumption and GHG emissions. An interactive method enabling a progressive and selective search of non-dominated solutions with distinct characteristics and underlying trade-offs is utilized. Illustrative results indicate that the maximization of GDP and the employment levels lead to an increase of both energy consumption and GHG emissions, while the minimization of either GHG emissions or energy consumption cause negative impacts on GDP and employment. - Highlights: • A hybrid Input–Output multi-objective model is applied to the Brazilian economy. • Objective functions are GDP, employment level, energy consumption and GHG emissions. • Interactive search process identifies trade-offs between the competing objectives. • Positive correlations between GDP growth and employment. • Positive correlations between energy consumption and GHG emissions

Multi-class geospatial object detection based on a position-sensitive balancing framework for high spatial resolution remote sensing imagery

Science.gov (United States)

Zhong, Yanfei; Han, Xiaobing; Zhang, Liangpei

2018-04-01

Multi-class geospatial object detection from high spatial resolution (HSR) remote sensing imagery is attracting increasing attention in a wide range of object-related civil and engineering applications. However, the distribution of objects in HSR remote sensing imagery is location-variable and complicated, and how to accurately detect the objects in HSR remote sensing imagery is a critical problem. Due to the powerful feature extraction and representation capability of deep learning, the deep learning based region proposal generation and object detection integrated framework has greatly promoted the performance of multi-class geospatial object detection for HSR remote sensing imagery. However, due to the translation caused by the convolution operation in the convolutional neural network (CNN), although the performance of the classification stage is seldom influenced, the localization accuracies of the predicted bounding boxes in the detection stage are easily influenced. The dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage has not been addressed for HSR remote sensing imagery, and causes position accuracy problems for multi-class geospatial object detection with region proposal generation and object detection. In order to further improve the performance of the region proposal generation and object detection integrated framework for HSR remote sensing imagery object detection, a position-sensitive balancing (PSB) framework is proposed in this paper for multi-class geospatial object detection from HSR remote sensing imagery. The proposed PSB framework takes full advantage of the fully convolutional network (FCN), on the basis of a residual network, and adopts the PSB framework to solve the dilemma between translation-invariance in the classification stage and translation-variance in the object detection stage. In addition, a pre-training mechanism is utilized to accelerate the training procedure

Multi-Objective Optimization of an In situ Bioremediation Technology to Treat Perchlorate-Contaminated Groundwater

Science.gov (United States)

The presentation shows how a multi-objective optimization method is integrated into a transport simulator (MT3D) for estimating parameters and cost of in-situ bioremediation technology to treat perchlorate-contaminated groundwater.

DATA QUALITY EVALUATION AND APPLICATION POTENTIAL ANALYSIS OF TIANGONG-2 WIDE-BAND IMAGING SPECTROMETER

Directory of Open Access Journals (Sweden)

B. Qin

2018-04-01

Full Text Available Tiangong-2 is the first space laboratory in China, which launched in September 15, 2016. Wide-band Imaging Spectrometer is a medium resolution multispectral imager on Tiangong-2. In this paper, the authors introduced the indexes and parameters of Wideband Imaging Spectrometer, and made an objective evaluation about the data quality of Wide-band Imaging Spectrometer in radiation quality, image sharpness and information content, and compared the data quality evaluation results with that of Landsat-8. Although the data quality of Wide-band Imager Spectrometer has a certain disparity with Landsat-8 OLI data in terms of signal to noise ratio, clarity and entropy. Compared with OLI, Wide-band Imager Spectrometer has more bands, narrower bandwidth and wider swath, which make it a useful remote sensing data source in classification and identification of large and medium scale ground objects. In the future, Wide-band Imaging Spectrometer data will be widely applied in land cover classification, ecological environment assessment, marine and coastal zone monitoring, crop identification and classification, and other related areas.

Data Quality Evaluation and Application Potential Analysis of TIANGONG-2 Wide-Band Imaging Spectrometer

Science.gov (United States)

Qin, B.; Li, L.; Li, S.

2018-04-01

Tiangong-2 is the first space laboratory in China, which launched in September 15, 2016. Wide-band Imaging Spectrometer is a medium resolution multispectral imager on Tiangong-2. In this paper, the authors introduced the indexes and parameters of Wideband Imaging Spectrometer, and made an objective evaluation about the data quality of Wide-band Imaging Spectrometer in radiation quality, image sharpness and information content, and compared the data quality evaluation results with that of Landsat-8. Although the data quality of Wide-band Imager Spectrometer has a certain disparity with Landsat-8 OLI data in terms of signal to noise ratio, clarity and entropy. Compared with OLI, Wide-band Imager Spectrometer has more bands, narrower bandwidth and wider swath, which make it a useful remote sensing data source in classification and identification of large and medium scale ground objects. In the future, Wide-band Imaging Spectrometer data will be widely applied in land cover classification, ecological environment assessment, marine and coastal zone monitoring, crop identification and classification, and other related areas.

Multi-objective optimization of the reactor coolant system

International Nuclear Information System (INIS)

Chen Lei; Yan Changqi; Wang Jianjun

2014-01-01

Background: Weight and size are important criteria in evaluating the performance of a nuclear power plant. It is of great theoretical value and engineering significance to reduce the weight and volume of the components for a nuclear power plant by the optimization methodology. Purpose: In order to provide a new method for the optimization of nuclear power plant multi-objective, the concept of the non-dominated solution was introduced. Methods: Based on the parameters of Qinshan I nuclear power plant, the mathematical models of the reactor core, the reactor vessel, the main pipe, the pressurizer and the steam generator were built and verified. The sensitivity analyses were carried out to study the influences of the design variables on the objectives. A modified non-dominated sorting genetic algorithm was proposed and employed to optimize the weight and the volume of the reactor coolant system. Results: The results show that the component mathematical models are reliable, the modified non-dominated sorting generic algorithm is effective, and the reactor inlet temperature is the most important variable which influences the distribution of the non-dominated solutions. Conclusion: The optimization results could provide a reference to the design of such reactor coolant system. (authors)

Optimal allocation of SVC and TCSC using quasi-oppositional chemical reaction optimization for solving multi-objective ORPD problem

Directory of Open Access Journals (Sweden)

Susanta Dutta

2018-05-01

Full Text Available This paper presents an efficient quasi-oppositional chemical reaction optimization (QOCRO technique to find the feasible optimal solution of the multi objective optimal reactive power dispatch (RPD problem with flexible AC transmission system (FACTS device. The quasi-oppositional based learning (QOBL is incorporated in conventional chemical reaction optimization (CRO, to improve the solution quality and the convergence speed. To check the superiority of the proposed method, it is applied on IEEE 14-bus and 30-bus systems and the simulation results of the proposed approach are compared to those reported in the literature. The computational results reveal that the proposed algorithm has excellent convergence characteristics and is superior to other multi objective optimization algorithms. Keywords: Quasi-oppositional chemical reaction optimization (QOCRO, Reactive power dispatch (RPD, TCSC, SVC, Multi-objective optimization

Correlation spectrometer

Science.gov (United States)

Sinclair, Michael B [Albuquerque, NM; Pfeifer, Kent B [Los Lunas, NM; Flemming, Jeb H [Albuquerque, NM; Jones, Gary D [Tijeras, NM; Tigges, Chris P [Albuquerque, NM

2010-04-13

A correlation spectrometer can detect a large number of gaseous compounds, or chemical species, with a species-specific mask wheel. In this mode, the spectrometer is optimized for the direct measurement of individual target compounds. Additionally, the spectrometer can measure the transmission spectrum from a given sample of gas. In this mode, infrared light is passed through a gas sample and the infrared transmission signature of the gasses present is recorded and measured using Hadamard encoding techniques. The spectrometer can detect the transmission or emission spectra in any system where multiple species are present in a generally known volume.

Multi-objective portfolio optimization of mutual funds under downside risk measure using fuzzy theory

Directory of Open Access Journals (Sweden)

M. Amiri

2012-10-01

Full Text Available Mutual fund is one of the most popular techniques for many people to invest their funds where a professional fund manager invests people's funds based on some special predefined objectives; therefore, performance evaluation of mutual funds is an important problem. This paper proposes a multi-objective portfolio optimization to offer asset allocation. The proposed model clusters mutual funds with two methods based on six characteristics including rate of return, variance, semivariance, turnover rate, Treynor index and Sharpe index. Semivariance is used as a downside risk measure. The proposed model of this paper uses fuzzy variables for return rate and semivariance. A multi-objective fuzzy mean-semivariance portfolio optimization model is implemented and fuzzy programming technique is adopted to solve the resulted problem. The proposed model of this paper has gathered the information of mutual fund traded on Nasdaq from 2007 to 2009 and Pareto optimal solutions are obtained considering different weights for objective functions. The results of asset allocation, rate of return and risk of each cluster are also determined and they are compared with the results of two clustering methods.

EMIR, the GTC NIR multi-object imager-spectrograph

Science.gov (United States)

Garzón, F.; Abreu, D.; Barrera, S.; Becerril, S.; Cairós, L. M.; Díaz, J. J.; Fragoso, A. B.; Gago, F.; Grange, R.; González, C.; López, P.; Patrón, J.; Pérez, J.; Rasilla, J. L.; Redondo, P.; Restrepo, R.; Saavedra, P.; Sánchez, V.; Tenegi, F.; Vallbé, M.

2007-06-01

EMIR, currently entering into its fabrication and AIV phase, will be one of the first common user instruments for the GTC, the 10 meter telescope under construction by GRANTECAN at the Roque de los Muchachos Observatory (Canary Islands, Spain). EMIR is being built by a Consortium of Spanish and French institutes led by the Instituto de Astrofísica de Canarias (IAC). EMIR is designed to realize one of the central goals of 10m class telescopes, allowing observers to obtain spectra for large numbers of faint sources in a time-efficient manner. EMIR is primarily designed to be operated as a MOS in the K band, but offers a wide range of observing modes, including imaging and spectroscopy, both long slit and multi-object, in the wavelength range 0.9 to 2.5 μm. It is equipped with two innovative subsystems: a robotic reconfigurable multi-slit mask and dispersive elements formed by the combination of high quality diffraction grating and conventional prisms, both at the heart of the instrument. The present status of development, expected performances, schedule and plans for scientific exploitation are described and discussed. The development and fabrication of EMIR is funded by GRANTECAN and the Plan Nacional de Astronomía y Astrofísica (National Plan for Astronomy and Astrophysics, Spain).

Energy, exergy, economic (3E) analyses and multi-objective optimization of vapor absorption heat transformer using NSGA-II technique

International Nuclear Information System (INIS)

Jain, Vaibhav; Sachdeva, Gulshan

2017-01-01

Highlights: • Study includes energy, exergy and economic analyses of absorption heat transformer. • It addresses multi-objective optimization study using NSGA-II technique. • Total annual cost and total exergy destruction are simultaneously optimized. • Results with multi-objective optimized design are more acceptable than other. - Abstract: Present paper addresses the energy, exergy and economic (3E) analyses of absorption heat transformer (AHT) working with LiBr-H 2 O fluid pair. The heat exchangers namely absorber, condenser, evaporator, generator and solution heat exchanger are designed for the size and cost estimation of AHT. Later, the effect of operating variables is examined on the system performance, size and cost. Simulation studies showed a conflict between thermodynamic and economic performance of the system. The heat exchangers with lower investment cost showed high irreversible losses and vice versa. Thus, the operating variables of systems are determined economically as well as thermodynamically by implementing non-dominated sort genetic algorithm-II (NSGA-II) technique of multi-objective optimization. In present work, if the cost based optimized design is chosen, total exergy destruction is 2.4% higher than its minimum possible value; whereas, if total exergy based optimized design is chosen, total annual cost is 6.1% higher than its minimum possible value. On the other hands, total annual cost and total exergy destruction are only 1.0% and 0.8%, respectively more from their minimum possible values with multi-objective optimized design. Thus, the multi-objective optimized design of the AHT is best outcome than any other single-objective optimized designs.

A risk-based multi-objective model for optimal placement of sensors in water distribution system

Science.gov (United States)

Naserizade, Sareh S.; Nikoo, Mohammad Reza; Montaseri, Hossein

2018-02-01

In this study, a new stochastic model based on Conditional Value at Risk (CVaR) and multi-objective optimization methods is developed for optimal placement of sensors in water distribution system (WDS). This model determines minimization of risk which is caused by simultaneous multi-point contamination injection in WDS using CVaR approach. The CVaR considers uncertainties of contamination injection in the form of probability distribution function and calculates low-probability extreme events. In this approach, extreme losses occur at tail of the losses distribution function. Four-objective optimization model based on NSGA-II algorithm is developed to minimize losses of contamination injection (through CVaR of affected population and detection time) and also minimize the two other main criteria of optimal placement of sensors including probability of undetected events and cost. Finally, to determine the best solution, Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), as a subgroup of Multi Criteria Decision Making (MCDM) approach, is utilized to rank the alternatives on the trade-off curve among objective functions. Also, sensitivity analysis is done to investigate the importance of each criterion on PROMETHEE results considering three relative weighting scenarios. The effectiveness of the proposed methodology is examined through applying it to Lamerd WDS in the southwestern part of Iran. The PROMETHEE suggests 6 sensors with suitable distribution that approximately cover all regions of WDS. Optimal values related to CVaR of affected population and detection time as well as probability of undetected events for the best optimal solution are equal to 17,055 persons, 31 mins and 0.045%, respectively. The obtained results of the proposed methodology in Lamerd WDS show applicability of CVaR-based multi-objective simulation-optimization model for incorporating the main uncertainties of contamination injection in order to evaluate extreme value

Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

Science.gov (United States)

Meng, Qing-chun; Rong, Xiao-xia; Zhang, Yi-min; Wan, Xiao-le; Liu, Yuan-yuan; Wang, Yu-zhi

2016-01-01

CO2 emission influences not only global climate change but also international economic and political situations. Thus, reducing the emission of CO2, a major greenhouse gas, has become a major issue in China and around the world as regards preserving the environmental ecology. Energy consumption from coal, oil, and natural gas is primarily responsible for the production of greenhouse gases and air pollutants such as SO2 and NOX, which are the main air pollutants in China. In this study, a mathematical multi-objective optimization method was adopted to analyze the collaborative emission reduction of three kinds of gases on the basis of their common restraints in different ways of energy consumption to develop an economic, clean, and efficient scheme for energy distribution. The first part introduces the background research, the collaborative emission reduction for three kinds of gases, the multi-objective optimization, the main mathematical modeling, and the optimization method. The second part discusses the four mathematical tools utilized in this study, which include the Granger causality test to analyze the causality between air quality and pollutant emission, a function analysis to determine the quantitative relation between energy consumption and pollutant emission, a multi-objective optimization to set up the collaborative optimization model that considers energy consumption, and an optimality condition analysis for the multi-objective optimization model to design the optimal-pole algorithm and obtain an efficient collaborative reduction scheme. In the empirical analysis, the data of pollutant emission and final consumption of energies of Tianjin in 1996-2012 was employed to verify the effectiveness of the model and analyze the efficient solution and the corresponding dominant set. In the last part, several suggestions for collaborative reduction are recommended and the drawn conclusions are stated.

Game of Objects: vicarious causation and multi-modal media

Directory of Open Access Journals (Sweden)

Aaron Pedinotti

2013-09-01

Full Text Available This paper applies philosopher Graham Harman's object-oriented theory of "vicarious causation" to an analysis of the multi-modal media phenomenon known as "Game of Thrones." Examining the manner in which George R.R. Martin's best-selling series of fantasy novels has been adapted into a board game, a video game, and a hit HBO television series, it uses the changes entailed by these processes to trace the contours of vicariously generative relations. In the course of the resulting analysis, it provides new suggestions concerning the eidetic dimensions of Harman's causal model, particularly with regard to causation in linear networks and in differing types of game systems.

Multi-objective group scheduling optimization integrated with preventive maintenance

Science.gov (United States)

Liao, Wenzhu; Zhang, Xiufang; Jiang, Min

2017-11-01

This article proposes a single-machine-based integration model to meet the requirements of production scheduling and preventive maintenance in group production. To describe the production for identical/similar and different jobs, this integrated model considers the learning and forgetting effects. Based on machine degradation, the deterioration effect is also considered. Moreover, perfect maintenance and minimal repair are adopted in this integrated model. The multi-objective of minimizing total completion time and maintenance cost is taken to meet the dual requirements of delivery date and cost. Finally, a genetic algorithm is developed to solve this optimization model, and the computation results demonstrate that this integrated model is effective and reliable.

Interfacing an aspiration ion mobility spectrometer to a triple quadrupole mass spectrometer

International Nuclear Information System (INIS)

Adamov, Alexey; Viidanoja, Jyrki; Kaerpaenoja, Esko; Paakkanen, Heikki; Ketola, Raimo A.; Kostiainen, Risto; Sysoev, Alexey; Kotiaho, Tapio

2007-01-01

This article presents the combination of an aspiration-type ion mobility spectrometer with a mass spectrometer. The interface between the aspiration ion mobility spectrometer and the mass spectrometer was designed to allow for quick mounting of the aspiration ion mobility spectrometer onto a Sciex API-300 triple quadrupole mass spectrometer. The developed instrumentation is used for gathering fundamental information on aspiration ion mobility spectrometry. Performance of the instrument is demonstrated using 2,6-di-tert-butyl pyridine and dimethyl methylphosphonate

DMD-based multi-object spectrograph on Galileo telescope

Science.gov (United States)

Zamkotsian, Frederic; Spano, Paolo; Lanzoni, Patrick; Bon, William; Riva, Marco; Nicastro, Luciano; Molinari, Emilio; Di Marcantonio, Paolo; Zerbi, Filippo; Valenziano, Luca

2013-03-01

Next-generation infrared astronomical instrumentation for ground-based and space telescopes could be based on MOEMS programmable slit masks for multi-object spectroscopy (MOS). This astronomical technique is used extensively to investigate the formation and evolution of galaxies. We propose to develop a 2048x1080 DMD-based MOS instrument to be mounted on the Galileo telescope and called BATMAN. A two-arm instrument has been designed for providing in parallel imaging and spectroscopic capabilities. The two arms with F/4 on the DMD are mounted on a common bench, and an upper bench supports the detectors thanks to two independent hexapods. Very good optical quality on the DMD and the detectors will be reached. ROBIN, a BATMAN demonstrator, has been designed, realized and integrated. It permits to determine the instrument integration procedure, including optics and mechanics integration, alignment procedure and optical quality. First images have been obtained and measured. A DMD pattern manager has been developed in order to generate any slit mask according to the list of objects to be observed; spectra have been generated and measured. Observation strategies will be studied and demonstrated for the scientific optimization strategy over the whole FOV. BATMAN on the sky is of prime importance for characterizing the actual performance of this new family of MOS instruments, as well as investigating the operational procedures on astronomical objects. This instrument will be placed on the Telescopio Nazionale Galileo at the beginning of next year, in 2014.

A Global Multi-Objective Optimization Tool for Design of Mechatronic Components using Generalized Differential Evolution

DEFF Research Database (Denmark)

Bech, Michael Møller; Nørgård, Christian; Roemer, Daniel Beck

2016-01-01

This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri-objectiv......This paper illustrates how the relatively simple constrained multi-objective optimization algorithm Generalized Differential Evolution 3 (GDE3), can assist with the practical sizing of mechatronic components used in e.g. digital displacement fluid power machinery. The studied bi- and tri...... different optimization control parameter settings and it is concluded that GDE3 is a reliable optimization tool that can assist mechatronic engineers in the design and decision making process....

Multi-objective optimisation of wastewater treatment plant control to reduce greenhouse gas emissions.

Science.gov (United States)

Sweetapple, Christine; Fu, Guangtao; Butler, David

2014-05-15

This study investigates the potential of control strategy optimisation for the reduction of operational greenhouse gas emissions from wastewater treatment in a cost-effective manner, and demonstrates that significant improvements can be realised. A multi-objective evolutionary algorithm, NSGA-II, is used to derive sets of Pareto optimal operational and control parameter values for an activated sludge wastewater treatment plant, with objectives including minimisation of greenhouse gas emissions, operational costs and effluent pollutant concentrations, subject to legislative compliance. Different problem formulations are explored, to identify the most effective approach to emissions reduction, and the sets of optimal solutions enable identification of trade-offs between conflicting objectives. It is found that multi-objective optimisation can facilitate a significant reduction in greenhouse gas emissions without the need for plant redesign or modification of the control strategy layout, but there are trade-offs to consider: most importantly, if operational costs are not to be increased, reduction of greenhouse gas emissions is likely to incur an increase in effluent ammonia and total nitrogen concentrations. Design of control strategies for a high effluent quality and low costs alone is likely to result in an inadvertent increase in greenhouse gas emissions, so it is of key importance that effects on emissions are considered in control strategy development and optimisation. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Novel Population-based Multi-Objective CMA-ES and the Impact of Different Constraint Handling Techniques

NARCIS (Netherlands)

S.F. Rodrigues; P. Bauer (Pavol); P.A.N. Bosman (Peter)

2014-01-01

htmlabstractThe Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) is a well-known, state-of-the-art optimization algorithm for single-objective real-valued problems, especially in black-box settings. Although several extensions of CMA-ES to multi-objective (MO) optimization exist, no

Multi-objective mean-variance-skewness model for generation portfolio allocation in electricity markets

Energy Technology Data Exchange (ETDEWEB)

Pindoriya, N.M.; Singh, S.N. [Department of Electrical Engineering, Indian Institute of Technology Kanpur, Kanpur 208016 (India); Singh, S.K. [Indian Institute of Management Lucknow, Lucknow 226013 (India)

2010-10-15

This paper proposes an approach for generation portfolio allocation based on mean-variance-skewness (MVS) model which is an extension of the classical mean-variance (MV) portfolio theory, to deal with assets whose return distribution is non-normal. The MVS model allocates portfolios optimally by considering the maximization of both the expected return and skewness of portfolio return while simultaneously minimizing the risk. Since, it is competing and conflicting non-smooth multi-objective optimization problem, this paper employed a multi-objective particle swarm optimization (MOPSO) based meta-heuristic technique to provide Pareto-optimal solution in a single simulation run. Using a case study of the PJM electricity market, the performance of the MVS portfolio theory based method and the classical MV method is compared. It has been found that the MVS portfolio theory based method can provide significantly better portfolios in the situation where non-normally distributed assets exist for trading. (author)

Probing optimal measurement configuration for optical scatterometry by the multi-objective genetic algorithm

Science.gov (United States)

Chen, Xiuguo; Gu, Honggang; Jiang, Hao; Zhang, Chuanwei; Liu, Shiyuan

2018-04-01

Measurement configuration optimization (MCO) is a ubiquitous and important issue in optical scatterometry, whose aim is to probe the optimal combination of measurement conditions, such as wavelength, incidence angle, azimuthal angle, and/or polarization directions, to achieve a higher measurement precision for a given measuring instrument. In this paper, the MCO problem is investigated and formulated as a multi-objective optimization problem, which is then solved by the multi-objective genetic algorithm (MOGA). The case study on the Mueller matrix scatterometry for the measurement of a Si grating verifies the feasibility of the MOGA in handling the MCO problem in optical scatterometry by making a comparison with the Monte Carlo simulations. Experiments performed at the achieved optimal measurement configuration also show good agreement between the measured and calculated best-fit Mueller matrix spectra. The proposed MCO method based on MOGA is expected to provide a more general and practical means to solve the MCO problem in the state-of-the-art optical scatterometry.

Multi-objective mean-variance-skewness model for generation portfolio allocation in electricity markets

International Nuclear Information System (INIS)

Pindoriya, N.M.; Singh, S.N.; Singh, S.K.

2010-01-01

This paper proposes an approach for generation portfolio allocation based on mean-variance-skewness (MVS) model which is an extension of the classical mean-variance (MV) portfolio theory, to deal with assets whose return distribution is non-normal. The MVS model allocates portfolios optimally by considering the maximization of both the expected return and skewness of portfolio return while simultaneously minimizing the risk. Since, it is competing and conflicting non-smooth multi-objective optimization problem, this paper employed a multi-objective particle swarm optimization (MOPSO) based meta-heuristic technique to provide Pareto-optimal solution in a single simulation run. Using a case study of the PJM electricity market, the performance of the MVS portfolio theory based method and the classical MV method is compared. It has been found that the MVS portfolio theory based method can provide significantly better portfolios in the situation where non-normally distributed assets exist for trading. (author)

Multi-objective optimal power flow for active distribution network considering the stochastic characteristic of photovoltaic

Science.gov (United States)

Zhou, Bao-Rong; Liu, Si-Liang; Zhang, Yong-Jun; Yi, Ying-Qi; Lin, Xiao-Ming

2017-05-01

To mitigate the impact on the distribution networks caused by the stochastic characteristic and high penetration of photovoltaic, a multi-objective optimal power flow model is proposed in this paper. The regulation capability of capacitor, inverter of photovoltaic and energy storage system embedded in active distribution network are considered to minimize the expected value of active power the T loss and probability of voltage violation in this model. Firstly, a probabilistic power flow based on cumulant method is introduced to calculate the value of the objectives. Secondly, NSGA-II algorithm is adopted for optimization to obtain the Pareto optimal solutions. Finally, the best compromise solution can be achieved through fuzzy membership degree method. By the multi-objective optimization calculation of IEEE34-node distribution network, the results show that the model can effectively improve the voltage security and economy of the distribution network on different levels of photovoltaic penetration.

A new multi objective optimization model for designing a green supply chain network under uncertainty

Directory of Open Access Journals (Sweden)

Mohammad Mahdi Saffar

2015-01-01

Full Text Available Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with ε-constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE is applied.

A modified multi-objective particle swarm optimization approach and its application to the design of a deepwater composite riser

Science.gov (United States)

Zheng, Y.; Chen, J.

2017-09-01

A modified multi-objective particle swarm optimization method is proposed for obtaining Pareto-optimal solutions effectively. Different from traditional multi-objective particle swarm optimization methods, Kriging meta-models and the trapezoid index are introduced and integrated with the traditional one. Kriging meta-models are built to match expensive or black-box functions. By applying Kriging meta-models, function evaluation numbers are decreased and the boundary Pareto-optimal solutions are identified rapidly. For bi-objective optimization problems, the trapezoid index is calculated as the sum of the trapezoid's area formed by the Pareto-optimal solutions and one objective axis. It can serve as a measure whether the Pareto-optimal solutions converge to the Pareto front. Illustrative examples indicate that to obtain Pareto-optimal solutions, the method proposed needs fewer function evaluations than the traditional multi-objective particle swarm optimization method and the non-dominated sorting genetic algorithm II method, and both the accuracy and the computational efficiency are improved. The proposed method is also applied to the design of a deepwater composite riser example in which the structural performances are calculated by numerical analysis. The design aim was to enhance the tension strength and minimize the cost. Under the buckling constraint, the optimal trade-off of tensile strength and material volume is obtained. The results demonstrated that the proposed method can effectively deal with multi-objective optimizations with black-box functions.

Design of the multi-reflection time-of-flight mass spectrometer for the RAON facility

International Nuclear Information System (INIS)

Yoon, J.W.; Park, Y.H.; Park, S.J.; Kim, G.D.; Kim, Y.K.

2014-01-01

A multi-reflection time-of-flight mass spectrometer (MR-TOF-MS) has been proposed for high precision mass measurements on the future Korean heavy ion accelerator called RAON. MR-TOF-MS will allow us to reach very high mass resolving power (> 10 5 ) with extremely short measurement times (several ms) in a compact device. The MR-TOF-MS is composed of two electrostatic ion mirrors in combination with einzel lenses. The principle is that the injected ions travel for hundreds of revolutions inside MR-TOF-MS and ions with different masses are temporally separated. When temporal separation becomes larger than the ion bunch width, ions are extracted from the MR-TOF-MS by switching off the mirror voltages, and then arrive at a detector plane located at time focus, where an MCP detector for the mass measurement or an ion gate for the isobar separation is deployed. In this paper, simulation results for the MR-TOF-MS design using SIMION code are presented. Temporal broadenings, caused by the kinetic energy spread and the transverse emittance, were minimized by optimization of the electrode potentials, and it was demonstrated that the mass resolving power of 10 5 is achievable for the condition of an energy spread of ±30 eV and an emittance of 0.75 π*mm*mrad

Adaptive multi-objective Optimization scheme for cognitive radio resource management

KAUST Repository

Alqerm, Ismail

2014-12-01

Cognitive Radio is an intelligent Software Defined Radio that is capable to alter its transmission parameters according to predefined objectives and wireless environment conditions. Cognitive engine is the actuator that performs radio parameters configuration by exploiting optimization and machine learning techniques. In this paper, we propose an Adaptive Multi-objective Optimization Scheme (AMOS) for cognitive radio resource management to improve spectrum operation and network performance. The optimization relies on adapting radio transmission parameters to environment conditions using constrained optimization modeling called fitness functions in an iterative manner. These functions include minimizing power consumption, Bit Error Rate, delay and interference. On the other hand, maximizing throughput and spectral efficiency. Cross-layer optimization is exploited to access environmental parameters from all TCP/IP stack layers. AMOS uses adaptive Genetic Algorithm in terms of its parameters and objective weights as the vehicle of optimization. The proposed scheme has demonstrated quick response and efficiency in three different scenarios compared to other schemes. In addition, it shows its capability to optimize the performance of TCP/IP layers as whole not only the physical layer.

Multi-objective flexible job shop scheduling problem using variable neighborhood evolutionary algorithm

Science.gov (United States)

Wang, Chun; Ji, Zhicheng; Wang, Yan

2017-07-01

In this paper, multi-objective flexible job shop scheduling problem (MOFJSP) was studied with the objects to minimize makespan, total workload and critical workload. A variable neighborhood evolutionary algorithm (VNEA) was proposed to obtain a set of Pareto optimal solutions. First, two novel crowded operators in terms of the decision space and object space were proposed, and they were respectively used in mating selection and environmental selection. Then, two well-designed neighborhood structures were used in local search, which consider the problem characteristics and can hold fast convergence. Finally, extensive comparison was carried out with the state-of-the-art methods specially presented for solving MOFJSP on well-known benchmark instances. The results show that the proposed VNEA is more effective than other algorithms in solving MOFJSP.

A two-stage approach for multi-objective decision making with applications to system reliability optimization

International Nuclear Information System (INIS)

Li Zhaojun; Liao Haitao; Coit, David W.

2009-01-01

This paper proposes a two-stage approach for solving multi-objective system reliability optimization problems. In this approach, a Pareto optimal solution set is initially identified at the first stage by applying a multiple objective evolutionary algorithm (MOEA). Quite often there are a large number of Pareto optimal solutions, and it is difficult, if not impossible, to effectively choose the representative solutions for the overall problem. To overcome this challenge, an integrated multiple objective selection optimization (MOSO) method is utilized at the second stage. Specifically, a self-organizing map (SOM), with the capability of preserving the topology of the data, is applied first to classify those Pareto optimal solutions into several clusters with similar properties. Then, within each cluster, the data envelopment analysis (DEA) is performed, by comparing the relative efficiency of those solutions, to determine the final representative solutions for the overall problem. Through this sequential solution identification and pruning process, the final recommended solutions to the multi-objective system reliability optimization problem can be easily determined in a more systematic and meaningful way.

Self-corrected chip-based dual-comb spectrometer.

Science.gov (United States)

Hébert, Nicolas Bourbeau; Genest, Jérôme; Deschênes, Jean-Daniel; Bergeron, Hugo; Chen, George Y; Khurmi, Champak; Lancaster, David G

2017-04-03

We present a dual-comb spectrometer based on two passively mode-locked waveguide lasers integrated in a single Er-doped ZBLAN chip. This original design yields two free-running frequency combs having a high level of mutual stability. We developed in parallel a self-correction algorithm that compensates residual relative fluctuations and yields mode-resolved spectra without the help of any reference laser or control system. Fluctuations are extracted directly from the interferograms using the concept of ambiguity function, which leads to a significant simplification of the instrument that will greatly ease its widespread adoption and commercial deployment. Comparison with a correction algorithm relying on a single-frequency laser indicates discrepancies of only 50 attoseconds on optical timings. The capacities of this instrument are finally demonstrated with the acquisition of a high-resolution molecular spectrum covering 20 nm. This new chip-based multi-laser platform is ideal for the development of high-repetition-rate, compact and fieldable comb spectrometers in the near- and mid-infrared.

Multi-modal distribution crossover method based on two crossing segments bounded by selected parents applied to multi-objective design optimization

Energy Technology Data Exchange (ETDEWEB)

Ariyarit, Atthaphon; Kanazaki, Masahiro [Tokyo Metropolitan University, Tokyo (Japan)

2015-04-15

This paper discusses airfoil design optimization using a genetic algorithm (GA) with multi-modal distribution crossover (MMDX). The proposed crossover method creates four segments from four parents, of which two segments are bounded by selected parents and two segments are bounded by one parent and another segment. After these segments are defined, four offsprings are generated. This study applied the proposed optimization to a real-world, multi-objective airfoil design problem using class-shape function transformation parameterization, which is an airfoil representation that uses polynomial function, to investigate the effectiveness of this algorithm. The results are compared with the results of the blend crossover (BLX) and unimodal normal distribution crossover (UNDX) algorithms. The objective of these airfoil design problems is to successfully find the optimal design. The outcome of using this algorithm is superior to that of the BLX and UNDX crossover methods because the proposed method can maintain higher diversity than the BLX and UNDX methods. This advantage is desirable for real-world problems.

Multi-modal distribution crossover method based on two crossing segments bounded by selected parents applied to multi-objective design optimization

International Nuclear Information System (INIS)

Ariyarit, Atthaphon; Kanazaki, Masahiro

2015-01-01

This paper discusses airfoil design optimization using a genetic algorithm (GA) with multi-modal distribution crossover (MMDX). The proposed crossover method creates four segments from four parents, of which two segments are bounded by selected parents and two segments are bounded by one parent and another segment. After these segments are defined, four offsprings are generated. This study applied the proposed optimization to a real-world, multi-objective airfoil design problem using class-shape function transformation parameterization, which is an airfoil representation that uses polynomial function, to investigate the effectiveness of this algorithm. The results are compared with the results of the blend crossover (BLX) and unimodal normal distribution crossover (UNDX) algorithms. The objective of these airfoil design problems is to successfully find the optimal design. The outcome of using this algorithm is superior to that of the BLX and UNDX crossover methods because the proposed method can maintain higher diversity than the BLX and UNDX methods. This advantage is desirable for real-world problems.

Flexible feature-space-construction architecture and its VLSI implementation for multi-scale object detection

Science.gov (United States)

Luo, Aiwen; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Huang, Zunkai; Jürgen Mattausch, Hans

2018-04-01

Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection systems is often degraded by, e.g., changes in the illumination intensity of the light source, foreground-background contrast variations or automatic gain control from the camera. In order to avoid such degradation effects, we present a block-based L1-norm-circuit architecture which is configurable for different image-cell sizes, cell-based feature descriptors and image resolutions according to customization parameters from the circuit input. The incorporated flexibility in both the image resolution and the cell size for multi-scale image pyramids leads to lower computational complexity and power consumption. Additionally, an object-detection prototype for performance evaluation in 65 nm CMOS implements the proposed L1-norm circuit together with a histogram of oriented gradients (HOG) descriptor and a support vector machine (SVM) classifier. The proposed parallel architecture with high hardware efficiency enables real-time processing, high detection robustness, small chip-core area as well as low power consumption for multi-scale object detection.

An inexact multi-objective programming approach for strategic environmental assessment on regional development plan

Institute of Scientific and Technical Information of China (English)

WANG Jihua; GUO Huaicheng; LIU Lei; HAO Mingjia; ZHANG Ming; LU Xiaojian; XING Kexia

2004-01-01

This paper presents the development of an inexact multi-objective programming (IMOP) model and its application to the strategic environmental assessment (SEA) for the regional development plan for the Hunnan New Zone (HNZ) in Shenyang City, China. Inexact programming and multi-objective programming methods are employed to effectively account for extensive uncertainties in the study system and to reflect various interests from different stakeholders, respectively. In the case study, balancing-economy-and-environment scenario and focusing-industry-development scenario are analyzed by the interactive solution process for addressing the preferences from local authorities and compromises among different objectives. Through interpreting the model solutions under both scenarios, analysis of industrial structure, waste water treatment plant(WWTP) expansion, water consumption and pollution generation and treatment are undertaken for providing a solid base to justify and evaluate the HNZ regional development plan. The study results show that the developed IMOP-SEA framework is feasible and applicable in carrying comprehensive environmental impact assessments for development plan in a more effective and efficient manner.

Constrained non-linear multi-objective optimisation of preventive maintenance scheduling for offshore wind farms

Science.gov (United States)

Zhong, Shuya; Pantelous, Athanasios A.; Beer, Michael; Zhou, Jian

2018-05-01

Offshore wind farm is an emerging source of renewable energy, which has been shown to have tremendous potential in recent years. In this blooming area, a key challenge is that the preventive maintenance of offshore turbines should be scheduled reasonably to satisfy the power supply without failure. In this direction, two significant goals should be considered simultaneously as a trade-off. One is to maximise the system reliability and the other is to minimise the maintenance related cost. Thus, a non-linear multi-objective programming model is proposed including two newly defined objectives with thirteen families of constraints suitable for the preventive maintenance of offshore wind farms. In order to solve our model effectively, the nondominated sorting genetic algorithm II, especially for the multi-objective optimisation is utilised and Pareto-optimal solutions of schedules can be obtained to offer adequate support to decision-makers. Finally, an example is given to illustrate the performances of the devised model and algorithm, and explore the relationships of the two targets with the help of a contrast model.

UV Resonant Raman Spectrometer with Multi-Line Laser Excitation

Science.gov (United States)

Lambert, James L.; Kohel, James M.; Kirby, James P.; Morookian, John Michael; Pelletier, Michael J.

2013-01-01

A Raman spectrometer employs two or more UV (ultraviolet) laser wavel engths to generate UV resonant Raman (UVRR) spectra in organic sampl es. Resonant Raman scattering results when the laser excitation is n ear an electronic transition of a molecule, and the enhancement of R aman signals can be several orders of magnitude. In addition, the Ra man cross-section is inversely proportional to the fourth power of t he wavelength, so the UV Raman emission is increased by another fact or of 16, or greater, over visible Raman emissions. The Raman-scatter ed light is collected using a high-resolution broadband spectrograph . Further suppression of the Rayleigh-scattered laser light is provi ded by custom UV notch filters.

Geomorphological change detection using object-based feature extraction from multi-temporal LIDAR data

NARCIS (Netherlands)

Seijmonsbergen, A.C.; Anders, N.S.; Bouten, W.; Feitosa, R.Q.; da Costa, G.A.O.P.; de Almeida, C.M.; Fonseca, L.M.G.; Kux, H.J.H.

2012-01-01

Multi-temporal LiDAR DTMs are used for the development and testing of a method for geomorphological change analysis in western Austria. Our test area is located on a mountain slope in the Gargellen Valley in western Austria. Six geomorphological features were mapped by using stratified Object-Based

Culture belief based multi-objective hybrid differential evolutionary algorithm in short term hydrothermal scheduling

International Nuclear Information System (INIS)

Zhang Huifeng; Zhou Jianzhong; Zhang Yongchuan; Lu Youlin; Wang Yongqiang

2013-01-01

Highlights: ► Culture belief is integrated into multi-objective differential evolution. ► Chaotic sequence is imported to improve evolutionary population diversity. ► The priority of convergence rate is proved in solving hydrothermal problem. ► The results show the quality and potential of proposed algorithm. - Abstract: A culture belief based multi-objective hybrid differential evolution (CB-MOHDE) is presented to solve short term hydrothermal optimal scheduling with economic emission (SHOSEE) problem. This problem is formulated for compromising thermal cost and emission issue while considering its complicated non-linear constraints with non-smooth and non-convex characteristics. The proposed algorithm integrates a modified multi-objective differential evolutionary algorithm into the computation model of culture algorithm (CA) as well as some communication protocols between population space and belief space, three knowledge structures in belief space are redefined according to these problem-solving characteristics, and in the differential evolution a chaotic factor is embedded into mutation operator for avoiding the premature convergence by enlarging the search scale when the search trajectory reaches local optima. Furthermore, a new heuristic constraint-handling technique is utilized to handle those complex equality and inequality constraints of SHOSEE problem. After the application on hydrothermal scheduling system, the efficiency and stability of the proposed CB-MOHDE is verified by its more desirable results in comparison to other method established recently, and the simulation results also reveal that CB-MOHDE can be a promising alternative for solving SHOSEE.

Development of a multi-objective PBIL evolutionary algorithm applied to a nuclear reactor core reload optimization problem

International Nuclear Information System (INIS)

Machado, Marcelo D.; Dchirru, Roberto

2005-01-01

The nuclear reactor core reload optimization problem consists in finding a pattern of partially burned-up and fresh fuels that optimizes the plant's next operation cycle. This optimization problem has been traditionally solved using an expert's knowledge, but recently artificial intelligence techniques have also been applied successfully. The artificial intelligence optimization techniques generally have a single objective. However, most real-world engineering problems, including nuclear core reload optimization, have more than one objective (multi-objective) and these objectives are usually conflicting. The aim of this work is to develop a tool to solve multi-objective problems based on the Population-Based Incremental Learning (PBIL) algorithm. The new tool is applied to solve the Angra 1 PWR core reload optimization problem with the purpose of creating a Pareto surface, so that a pattern selected from this surface can be applied for the plant's next operation cycle. (author)

Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

Energy Technology Data Exchange (ETDEWEB)

Hashim, M.; Nazam, M.; Yao, L.; Baig, S.A.; Abrar, M.; Zia-ur-Rehman, M.

2017-07-01

The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world

Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

Directory of Open Access Journals (Sweden)

Muhammad Hashim

2017-05-01

Full Text Available Purpose:  The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM. Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM with fuzzy coefficients constructed in present research should be helpful for

Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

International Nuclear Information System (INIS)

Hashim, M.; Nazam, M.; Yao, L.; Baig, S.A.; Abrar, M.; Zia-ur-Rehman, M.

2017-01-01

The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem. Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis. Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability. Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world

Exergoeconomic analysis and multi-objective optimization of an ejector refrigeration cycle powered by an internal combustion (HCCI) engine

International Nuclear Information System (INIS)

Sadeghi, Mohsen; Mahmoudi, S.M.S.; Khoshbakhti Saray, R.

2015-01-01

Highlights: • Ejector refrigeration systems powered by HCCI engine is proposed. • A new two-dimensional model is developed for the ejector. • Multi-objective optimization is performed for the proposed system. • Pareto frontier is plotted for multi-objective optimization. - Abstract: Ejector refrigeration systems powered by low-grade heat sources have been an attractive research subject for a lot of researchers. In the present work the waste heat from exhaust gases of a HCCI (homogeneous charge compression ignition) engine is utilized to drive the ejector refrigeration system. Considering the frictional effects on the ejector wall, a new two-dimensional model is developed for the ejector. Energy, exergy and exergoeconomic analysis performed for the proposed system using the MATLAB software. In addition, considering the exergy efficiency and the product unit cost of the system as objective functions, a multi-objective optimization is performed for the system to find the optimum design variables including the generator, condenser and evaporator temperatures. The product unit cost is minimized while the exergy efficiency is maximized using the genetic algorithm. The optimization results are obtained as a set of optimal points and the Pareto frontier is plotted for multi-objective optimization. The results of the optimization show that ejector refrigeration cycle is operating at optimum state based on exergy efficiency and product unit cost when generator, condenser and evaporator work at 94.54 °C, 33.44 °C and 0.03 °C, respectively

Near-Earth object hazardous impact: A Multi-Criteria Decision Making approach.

Science.gov (United States)

Sánchez-Lozano, J M; Fernández-Martínez, M

2016-11-16

The impact of a near-Earth object (NEO) may release large amounts of energy and cause serious damage. Several NEO hazard studies conducted over the past few years provide forecasts, impact probabilities and assessment ratings, such as the Torino and Palermo scales. These high-risk NEO assessments involve several criteria, including impact energy, mass, and absolute magnitude. The main objective of this paper is to provide the first Multi-Criteria Decision Making (MCDM) approach to classify hazardous NEOs. Our approach applies a combination of two methods from a widely utilized decision making theory. Specifically, the Analytic Hierarchy Process (AHP) methodology is employed to determine the criteria weights, which influence the decision making, and the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to obtain a ranking of alternatives (potentially hazardous NEOs). In addition, NEO datasets provided by the NASA Near-Earth Object Program are utilized. This approach allows the classification of NEOs by descending order of their TOPSIS ratio, a single quantity that contains all of the relevant information for each object.

Development and application of a living probabilistic safety assessment tool: Multi-objective multi-dimensional optimization of surveillance requirements in NPPs considering their ageing

International Nuclear Information System (INIS)

Kančev, Duško; Čepin, Marko; Gjorgiev, Blaže

2014-01-01

The benefits of utilizing the probabilistic safety assessment towards improvement of nuclear power plant safety are presented in this paper. Namely, a nuclear power plant risk reduction can be achieved by risk-informed optimization of the deterministically-determined surveillance requirements. A living probabilistic safety assessment tool for time-dependent risk analysis on component, system and plant level is developed. The study herein focuses on the application of this living probabilistic safety assessment tool as a computer platform for multi-objective multi-dimensional optimization of the surveillance requirements of selected safety equipment seen from the aspect of the risk-informed reasoning. The living probabilistic safety assessment tool is based on a newly developed model for calculating time-dependent unavailability of ageing safety equipment within nuclear power plants. By coupling the time-dependent unavailability model with a commercial software used for probabilistic safety assessment modelling on plant level, the frames of the new platform i.e. the living probabilistic safety assessment tool are established. In such way, the time-dependent core damage frequency is obtained and is further on utilized as first objective function within a multi-objective multi-dimensional optimization case study presented within this paper. The test and maintenance costs are designated as the second and the incurred dose due to performing the test and maintenance activities as the third objective function. The obtained results underline, in general, the usefulness and importance of a living probabilistic safety assessment, seen as a dynamic probabilistic safety assessment tool opposing the conventional, time-averaged unavailability-based, probabilistic safety assessment. The results of the optimization, in particular, indicate that test intervals derived as optimal differ from the deterministically-determined ones defined within the existing technical specifications

Multi-physics and multi-objective design of heterogeneous SFR core: development of an optimization method under uncertainty

International Nuclear Information System (INIS)

Ammar, Karim

2014-01-01

Since Phenix shutting down in 2010, CEA does not have Sodium Fast Reactor (SFR) in operating condition. According to global energetic challenge and fast reactor abilities, CEA launched a program of industrial demonstrator called ASTRID (Advanced Sodium Technological Reactor for Industrial Demonstration), a reactor with electric power capacity equal to 600 MW. Objective of the prototype is, in first to be a response to environmental constraints, in second demonstrates the industrial viability of SFR reactor. The goal is to have a safety level at least equal to 3. generation reactors. ASTRID design integrates Fukushima feedback; Waste reprocessing (with minor actinide transmutation) and it linked industry. Installation safety is the priority. In all cases, no radionuclide should be released into environment. To achieve this objective, it is imperative to predict the impact of uncertainty sources on reactor behaviour. In this context, this thesis aims to develop new optimization methods for SFR cores. The goal is to improve the robustness and reliability of reactors in response to existing uncertainties. We will use ASTRID core as reference to estimate interest of new methods and tools developed. The impact of multi-Physics uncertainties in the calculation of the core performance and the use of optimization methods introduce new problems: How to optimize 'complex' cores (i.e. associated with design spaces of high dimensions with more than 20 variable parameters), taking into account the uncertainties? What is uncertainties behaviour for optimization core compare to reference core? Taking into account uncertainties, optimization core are they still competitive? Optimizations improvements are higher than uncertainty margins? The thesis helps to develop and implement methods necessary to take into account uncertainties in the new generation of simulation tools. Statistical methods to ensure consistency of complex multi-Physics simulation results are also

Contribution to the evaluation and to the improvement of multi-objective optimization methods: application to the optimization of nuclear fuel reloading pattern

International Nuclear Information System (INIS)

Collette, Y.

2002-01-01

In this thesis, we study the general problem of the selection of a multi-objective optimization method, then we study the improvement so as to efficiently solve a problem. The pertinent selection of a method presume the existence of a methodology: we have built tools to perform evaluation of performances and we propose an original method dedicated to the classification of know optimization methods. Our step has been applied to the elaboration of new methods for solving a very difficult problem: the nuclear core reload pattern optimization. First, we looked for a non usual approach of performances measurement: we have 'measured' the behavior of a method. To reach this goal, we have introduced several metrics. We have proposed to evaluate the 'aesthetic' of a distribution of solutions by defining two new metrics: a 'spacing metric' and a metric that allow us to measure the size of the biggest hole in the distribution of solutions. Then, we studied the convergence of multi-objective optimization methods by using some metrics defined in scientific literature and by proposing some more metrics: the 'Pareto ratio' which computes a ratio of solution production. Lastly, we have defined new metrics intended to better apprehend the behavior of optimization methods: the 'speed metric', which allows to compute the speed profile and a 'distribution metric' which allows to compute statistical distribution of solutions along the Pareto frontier. Next, we have studied transformations of a multi-objective problem and defined news methods: the modified Tchebychev method, or the penalized weighted sum of objective functions. We have elaborated new techniques to choose the initial point. These techniques allow to produce new initial points closer and closer to the Pareto frontier and, thanks to the 'proximal optimality concept', allowing dramatic improvements in the convergence of a multi-objective optimization method. Lastly, we have defined new vectorial multi-objective optimization

Upgrading of the triple axis neutron spectrometer TKSN-400 installed at Pakistan research reactor-1

International Nuclear Information System (INIS)

Bashir, J; Khan, R.T.A.; Khan, M.M.; Iqbal, N.; Waheed, A.; Hussain, A.; Khan, A.U.; Zaman, Q.

2000-01-01

The triple axis neutron spectrometer TKSN-400 installed at 10MW Pakistan Research Reactor has been upgraded. The upgrading included the development of a multi-counter assembly, a PC based spectrometer control and data acquisition system. The multi-counter assembly consists of an embankment of 8 pairs of /sup 3/He counter and 40' collimator. This system is more auspicious than the original arrangement because each of the collimator/counter pair is virtually identical, permitting automatic addition of the intensities and consequently a single high resolution profile as obtained with single counter machine. With these modifications, it has now become possible to measure the complete diffraction pattern in about 24 hours. Finally, the results obtained from the latest system are compared with the results reported in literature. (author)

The Science Case for Multi-Object Spectroscopy on the European ELT

OpenAIRE

Evans, Chris; Puech, Mathieu; Afonso, Jose; Almaini, Omar; Amram, Philippe; Aussel, Hervé; Barbuy, Beatriz; Basden, Alistair; Bastian, Nate; Battaglia, Giuseppina; Biller, Beth; Bonifacio, Piercarlo; Bouché, Nicholas; Bunker, Andy; Caffau, Elisabetta

2015-01-01

This White Paper presents the scientific motivations for a multi-object spectrograph (MOS) on the European Extremely Large Telescope (E-ELT). The MOS case draws on all fields of contemporary astronomy, from extra-solar planets, to the study of the halo of the Milky Way and its satellites, and from resolved stellar populations in nearby galaxies out to observations of the earliest 'first-light' structures in the partially-reionised Universe. The material presented here results from thorough di...

Energy resource allocation using multi-objective goal programming: the case of Lebanon

International Nuclear Information System (INIS)

Mezher, T.; Chedid, R.; Zahabi, W.

1998-01-01

The traditional energy-resources allocation problem is concerned with the allocation of limited resources among the end-uses such that the overall return is maximized. In the past, several techniques have been used to deal with such a problem. In this paper, the energy allocation process is looked at from two points of view: economy and environment. The economic objectives include costs, efficiency, energy conservation, and employment generation. The environmental objectives consider environmental friendliness factors. The objective functions are first quantified and then transformed into mathematical language to obtain a multi-objective allocation model based upon pre-emptive goal programming techniques. The proposed method allows decision-makers to encourage or discourage specific energy resources for the various household end-uses. The case of Lebanon is examined to illustrate the usefulness of the proposed technique. (Copyright (c) 1998 Elsevier Science B.V., Amsterdam. All rights reserved.)

Two-lens spectrometer for. beta. polarimetry

Energy Technology Data Exchange (ETDEWEB)

Holdsworth, D; Paul, D [Toronto Univ., Ontario (Canada). Dept. of Physics

1984-06-01

A test spectrometer has been built having four degrees of freedom which have been varied to optimize the transmission in a configuration in which the image distance is large compared to the object distance. Iron-clad magnets of 15 cm effective inside diameter were used. Within the limits of the primary magnet field, the optimized transmission T (%. of 4..pi.. sr) increases as the source is advanced through the magnet towards the polarimeter, approximately as T=5.1 exp(zsub(s)/10.5), where zsub(s) is in cm, measured from the position of peak axial field. When the source-image distance is 1.8 m the optimum separation of the peak axial fields of the magnets is 67+-1 cm and the spectrometer transmission is the same for parallel or antiparallel fields. When operating in the optimum configuration, the spectrometer accepts particles with initial trajectories from 18/sup 0/ to 42/sup 0/ relative to the axis and delivers them at the target with half-angle of convergence less than 4.1/sup 0/.

An off Axis Cavity Enhanced Absorption Spectrometer and a Rapid Scan Spectrometer with a Room-Temperature External Cavity Quantum Cascade Laser

Science.gov (United States)

Liu, Xunchen; Kang, Cheolhwa; Xu, Yunjie

2009-06-01

Quantum cascade laser (QCL) is a new type of mid-infrared tunable diode lasers with superior output power and mode quality. Recent developments, such as room temperature operation, wide frequency tunability, and narrow line width, make QCLs an ideal light source for high resolution spectroscopy. Two slit jet infrared spectrometers, namely an off-axis cavity enhanced absorption (CEA) spectrometer and a rapid scan spectrometer with an astigmatic multi-pass cell assembly, have been coupled with a newly purchased room temperature tunable mod-hop-free QCL with a frequency coverage from 1592 cm^{-1} to 1698 cm^{-1} and a scan rate of 0.1 cm^{-1}/ms. Our aim is to utilize these two sensitive spectrometers, that are equipped with a molecular jet expansion, to investigate the chiral molecules-(water)_n clusters. To demonstrate the resolution and sensitivity achieved, the rovibrational transitions of the static N_2O gas and the bending rovibrational transitions of the Ar-water complex, a test system, at 1634 cm^{-1} have been measured. D. Hofstetter and J. Faist in High performance quantum cascade lasers and their applications, Vol.89 Springer-Verlag Berlin & Heidelberg, 2003, pp. 61-98. Y. Xu, X. Liu, Z. Su, R. M. Kulkarni, W. S. Tam, C. Kang, I. Leonov and L. D'Agostino, Proc. Spie, 2009, 722208 (1-11). M. J. Weida and D. J. Nesbitt, J. Chem. Phys. 1997, 106, 3078-3089.

Design for sustainability of industrial symbiosis based on emergy and multi-objective particle swarm optimization.

Science.gov (United States)

Ren, Jingzheng; Liang, Hanwei; Dong, Liang; Sun, Lu; Gao, Zhiqiu

2016-08-15

Industrial symbiosis provides novel and practical pathway to the design for the sustainability. Decision support tool for its verification is necessary for practitioners and policy makers, while to date, quantitative research is limited. The objective of this work is to present an innovative approach for supporting decision-making in the design for the sustainability with the implementation of industrial symbiosis in chemical complex. Through incorporating the emergy theory, the model is formulated as a multi-objective approach that can optimize both the economic benefit and sustainable performance of the integrated industrial system. A set of emergy based evaluation index are designed. Multi-objective Particle Swarm Algorithm is proposed to solve the model, and the decision-makers are allowed to choose the suitable solutions form the Pareto solutions. An illustrative case has been studied by the proposed method, a few of compromises between high profitability and high sustainability can be obtained for the decision-makers/stakeholders to make decision. Copyright © 2016 Elsevier B.V. All rights reserved.

Multi-objective Extremum Seeking Control for Enhancement of Wind Turbine Power Capture with Load Reduction

Science.gov (United States)

Xiao, Yan; Li, Yaoyu; Rotea, Mario A.

2016-09-01

The primary objective in below rated wind speed (Region 2) is to maximize the turbine's energy capture. Due to uncertainty, variability of turbine characteristics and lack of inexpensive but precise wind measurements, model-free control strategies that do not use wind measurements such as Extremum Seeking Control (ESC) have received significant attention. Based on a dither-demodulation scheme, ESC can maximize the wind power capture in real time despite uncertainty, variabilities and lack of accurate wind measurements. The existing work on ESC based wind turbine control focuses on power capture only. In this paper, a multi-objective extremum seeking control strategy is proposed to achieve nearly optimum wind energy capture while decreasing structural fatigue loads. The performance index of the ESC combines the rotor power and penalty terms of the standard deviations of selected fatigue load variables. Simulation studies of the proposed multi-objective ESC demonstrate that the damage-equivalent loads of tower and/or blade loads can be reduced with slight compromise in energy capture.

Catchment variability and parameter estimation in multi-objective regionalisation of a rainfall-runoff model

NARCIS (Netherlands)

Deckers, Dave L.E.H.; Booij, Martijn J.; Rientjes, T.H.M.; Krol, Martinus S.

2010-01-01

This study attempts to examine if catchment variability favours regionalisation by principles of catchment similarity. Our work combines calibration of a simple conceptual model for multiple objectives and multi-regression analyses to establish a regional model between model sensitive parameters and

An observation planning algorithm applied to multi-objective astronomical observations and its simulation in COSMOS field

Science.gov (United States)

Jin, Yi; Gu, Yonggang; Zhai, Chao

2012-09-01

Multi-Object Fiber Spectroscopic sky surveys are now booming, such as LAMOST already built by China, BIGBOSS project put forward by the U.S. Lawrence Berkeley National Lab and GTC (Gran Telescopio Canarias) telescope developed by the United States, Mexico and Spain. They all use or will use this approach and each fiber can be moved within a certain area for one astrology target, so observation planning is particularly important for this Sky Surveys. One observation planning algorithm used in multi-objective astronomical observations is developed. It can avoid the collision and interference between the fiber positioning units in the focal plane during the observation in one field of view, and the interested objects can be ovserved in a limited round with the maximize efficiency. Also, the observation simulation can be made for wide field of view through multi-FOV observation. After the observation planning is built ,the simulation is made in COSMOS field using GTC telescope. Interested galaxies, stars and high-redshift LBG galaxies are selected after the removal of the mask area, which may be bright stars. Then 9 FOV simulation is completed and observation efficiency and fiber utilization ratio for every round are given. Otherwise,allocating a certain number of fibers for background sky, giving different weights for different objects and how to move the FOV to improve the overall observation efficiency are discussed.

Mass spectrometer with two ion sources

International Nuclear Information System (INIS)

Glickman, L.G.; Mit', A.G.

2002-01-01

Static mass spectrometer with mid-plane near which ions are moving is considered in this article. Two ion sources are used, their exit slits are perpendicular to the mid-plane. The simple method of the replacement of source is offered. Two concave two-electrode transaxial mirrors with two-plate electrodes are used for this aim. The mid-plane of these mirrors coincides with the mid-plane of the device. The exit slit of each source is located in the principal plane of the object space. The principal planes of the image space of the both mirrors coincide. The images of the exit slits of the sources are in these planes and coincide too. We used the mirrors making stigmatic images with the magnification one to one, in which the dispersion on energy and spherical aberrations of the second order are equal to zero. These images are the objects on which the ion-optical system of the mass spectrometer is tuned. When you choose one from two ion sources it is enough to switch the corresponding mirror

Multi-objective portfolio optimization of mutual funds under downside risk measure using fuzzy theory

OpenAIRE

M. Amiri; M. Zandieh; A. Alimi

2012-01-01

Mutual fund is one of the most popular techniques for many people to invest their funds where a professional fund manager invests people's funds based on some special predefined objectives; therefore, performance evaluation of mutual funds is an important problem. This paper proposes a multi-objective portfolio optimization to offer asset allocation. The proposed model clusters mutual funds with two methods based on six characteristics including rate of return, variance, semivariance, turnove...

Intelligent multi-objective optimization for building energy and comfort management

Directory of Open Access Journals (Sweden)

Pervez Hameed Shaikh

2018-04-01

Full Text Available The rapid economic and population growth in developing countries, effective and efficient energy usage has turned out to be crucial due to the rising concern of depleting fossil fuels, of which, one-third of primary energy is consumed in buildings and expected to rise by 53% up to 2030. This roaring sector posing a challenge, due to 90% of people spend most of their time in buildings, requires enhanced well-being of indoor environment and living standards. Therefore, building operations require more energy because most of the energy is consumed to make the indoor environment comfortable. Consequently, there is the need of improved energy efficiency to decrease energy consumption in buildings. In relation to this, the primary challenge of building control systems is the energy consumption and comfort level are generally conflicting to each other. Therefore, an important problem of sustainable smart buildings is to effectively manage the energy consumption and comfort and attain the trade-off between the two. Thus, smart buildings are becoming a trend of future construction that facilitates intelligent control in buildings for the fulfillment of occupant’s comfort level. In this study, an intelligent multi-objective system has been developed with evolutionary multi-objective genetic algorithm (MOGA optimization method. The corresponding case study simulation results for the effective management of users’ comfort and energy efficiency have been carried out. The case study results show the management of energy supply for each comfort parameter and maintain high comfort index achieving balance between the energy consumption and comfort level. Keywords: Energy, Buildings, Comfort, Management, Optimization, Trade-off

Scientific objectives and selection of targets for the SMART-1 Infrared Spectrometer (SIR)

Science.gov (United States)

Basilevsky, A. T.; Keller, H. U.; Nathues, A.; Mall, U.; Hiesinger, H.; Rosiek, M.

2004-12-01

The European SMART-1 mission to the Moon, primarily a testbed for innovative technologies, was launched in September 2003 and will reach the Moon in 2005. On board are several scientific instruments, including the point-spectrometer SMART-1 Infrared Spectrometer (SIR). Taking into account the capabilities of the SMART-1 mission and the SIR instrument in particular, as well as the open questions in lunar science, a selection of targets for SIR observations has been compiled. SIR can address at least five topics: (1) Surface/regolith processes; (2) Lunar volcanism; (3) Lunar crust structure; (4) Search for spectral signatures of ices at the lunar poles; and (5) Ground truth and study of geometric effects on the spectral shape. For each topic we will discuss specific observation modes, necessary to achieve our scientific goals. The majority of SIR targets will be observed in the nadir-tracking mode. More than 100 targets, which require off-nadir pointing and off-nadir tracking, are planned. It is expected that results of SIR observations will significantly increase our understanding of the Moon. Since the exact arrival date and the orbital parameters of the SMART-1 spacecraft are not known yet, a more detailed planning of the scientific observations will follow in the near future.

Multi-Objective Optimization Control for the Aerospace Dual-Active Bridge Power Converter

Directory of Open Access Journals (Sweden)

Tao Lei

2018-05-01

Full Text Available With the development of More Electrical Aircraft (MEA, the electrification of secondary power systems in aircraft is becoming more and more common. As the key power conversion device, the dual active bridge (DAB converter is the power interface for the energy storage system with the high voltage direct current (HVDC bus in aircraft electrical power systems. In this paper, a DAB DC-DC converter is designed to meet aviation requirements. The extended dual phase shifted control strategy is adopted, and a multi-objective genetic algorithm is applied to optimize its operating performance. Considering the three indicators of inductance current root mean square root (RMS value, negative reverse power and direct current (DC bias component of the current for the high frequency transformer as the optimization objectives, the DAB converter’s optimization model is derived to achieve soft switching as the main constraint condition. Optimized methods of controlling quantity for the DAB based on the evolution and genetic algorithm is used to solve the model, and a number of optimal control parameters are obtained under different load conditions. The results of digital, hard-in-loop simulation and hardware prototype experiments show that the three performance indexes are all suppressed greatly, and the optimization method proposed in this paper is reasonable. The work of this paper provides a theoretical basis and researching method for the multi-objective optimization of the power converter in the aircraft electrical power system.

Monolithic spectrometer

Energy Technology Data Exchange (ETDEWEB)

Rajic, Slobodan (Knoxville, TN); Egert, Charles M. (Oak Ridge, TN); Kahl, William K. (Knoxville, TN); Snyder, Jr., William B. (Knoxville, TN); Evans, III, Boyd M. (Oak Ridge, TN); Marlar, Troy A. (Knoxville, TN); Cunningham, Joseph P. (Oak Ridge, TN)

1998-01-01

A monolithic spectrometer is disclosed for use in spectroscopy. The spectrometer is a single body of translucent material with positioned surfaces for the transmission, reflection and spectral analysis of light rays.

Pipelining Computational Stages of the Tomographic Reconstructor for Multi-Object Adaptive Optics on a Multi-GPU System

KAUST Repository

Charara, Ali

2014-11-01

The European Extremely Large Telescope project (E-ELT) is one of Europe\\'s highest priorities in ground-based astronomy. ELTs are built on top of a variety of highly sensitive and critical astronomical instruments. In particular, a new instrument called MOSAIC has been proposed to perform multi-object spectroscopy using the Multi-Object Adaptive Optics (MOAO) technique. The core implementation of the simulation lies in the intensive computation of a tomographic reconstruct or (TR), which is used to drive the deformable mirror in real time from the measurements. A new numerical algorithm is proposed (1) to capture the actual experimental noise and (2) to substantially speed up previous implementations by exposing more concurrency, while reducing the number of floating-point operations. Based on the Matrices Over Runtime System at Exascale numerical library (MORSE), a dynamic scheduler drives all computational stages of the tomographic reconstruct or simulation and allows to pipeline and to run tasks out-of order across different stages on heterogeneous systems, while ensuring data coherency and dependencies. The proposed TR simulation outperforms asymptotically previous state-of-the-art implementations up to 13-fold speedup. At more than 50000 unknowns, this appears to be the largest-scale AO problem submitted to computation, to date, and opens new research directions for extreme scale AO simulations. © 2014 IEEE.

A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems

Directory of Open Access Journals (Sweden)

R. Venkata Rao

2014-01-01

Full Text Available The present work proposes a multi-objective improved teaching-learning based optimization (MO-ITLBO algorithm for unconstrained and constrained multi-objective function optimization. The MO-ITLBO algorithm is the improved version of basic teaching-learning based optimization (TLBO algorithm adapted for multi-objective problems. The basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self-motivated learning. The MO-ITLBO algorithm uses a grid-based approach to adaptively assess the non-dominated solutions (i.e. Pareto front maintained in an external archive. The performance of the MO-ITLBO algorithm is assessed by implementing it on unconstrained and constrained test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009 competition. The performance assessment is done by using the inverted generational distance (IGD measure. The IGD measures obtained by using the MO-ITLBO algorithm are compared with the IGD measures of the other state-of-the-art algorithms available in the literature. Finally, Lexicographic ordering is used to assess the overall performance of competitive algorithms. Results have shown that the proposed MO-ITLBO algorithm has obtained the 1st rank in the optimization of unconstrained test functions and the 3rd rank in the optimization of constrained test functions.

Selection of security system design via games of imperfect information and multi-objective genetic algorithm

International Nuclear Information System (INIS)

Lins, Isis Didier; Rêgo, Leandro Chaves; Moura, Márcio das Chagas

2013-01-01

This work analyzes the strategic interaction between a defender and an intelligent attacker by means of a game and reliability framework involving a multi-objective approach and imperfect information so as to support decision-makers in choosing efficiently designed security systems. A multi-objective genetic algorithm is used to determine the optimal security system's configurations representing the tradeoff between the probability of a successful defense and the acquisition and operational costs. Games with imperfect information are considered, in which the attacker has limited knowledge about the actual security system. The types of security alternatives are readily observable, but the number of redundancies actually implemented in each security subsystem is not known. The proposed methodology is applied to an illustrative example considering power transmission lines in the Northeast of Brazil, which are often targets for attackers who aims at selling the aluminum conductors. The empirical results show that the framework succeeds in handling this sort of strategic interaction. -- Highlights: ► Security components must have feasible costs and must be reliable. ► The optimal design of security systems considers a multi-objective approach. ► Games of imperfect information enable the choice of non-dominated configurations. ► MOGA, reliability and games support the entire defender's decision process. ► The selection of effective security systems may discourage attacker's actions

Evolution strategies and multi-objective optimization of permanent magnet motor

DEFF Research Database (Denmark)

Andersen, Søren Bøgh; Santos, Ilmar

2012-01-01

When designing a permanent magnet motor, several geometry and material parameters are to be defined. This is not an easy task, as material properties and magnetic fields are highly non-linear and the design of a motor is therefore often an iterative process. From an engineering point of view, we...... of evolution strategies, ES to effectively design and optimize parameters of permanent magnet motors. Single as well as multi-objective optimization procedures are carried out. A modified way of creating the strategy parameters for the ES algorithm is also proposed and has together with the standard ES...

Grey Relational Analyses for Multi-Objective Optimization of Turning S45C Carbon Steel

International Nuclear Information System (INIS)

Shah, A.H.A.; Azmi, A.I.; Khalil, A.N.M.

2016-01-01

The optimization of performance characteristics in turning process can be achieved through selection of proper machining parameters. It is well known that many researchers have successfully reported the optimization of single performance characteristic. Nevertheless, the multi-objective optimization can be difficult and challenging to be studied due to its complexity in analysis. This is because an improvement of one performance characteristic may lead to degradation of other performance characteristic. As a result, the study of multi-objective optimization in CNC turning of S45C carbon steel has been attempted in this paper through Taguchi and Grey Relational Analysis (GRA) method. Through this methodology, the multiple performance characteristics, namely; surface roughness, material removal rate (MRR), tool wear, and power consumption; can be optimized simultaneously. It appears from the experimental results that the multiple performance characteristics in CNC turning was achieved and improved through the methodology employed. (paper)

Evolutionary algorithms for multi-objective energetic and economic optimization in thermal system design