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Sample records for coordinated multi-objective control

  1. Computing Convex Coverage Sets for Multi-Objective Coordination Graphs

    NARCIS (Netherlands)

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

    2013-01-01

    Many real-world decision problems require making trade-offs between multiple objectives. However, in some cases, the relative importance of the objectives is not known when the problem is solved, precluding the use of single-objective methods. Instead, multi-objective methods, which compute the set

  2. MULTI-OBJECTIVE PREDICTIVE CONTROL: A SOLUTION USING METAHEURISTICS

    Directory of Open Access Journals (Sweden)

    Halim Merabti

    2014-12-01

    Full Text Available The application of multi objective model predictive control approaches is significantly limited with computation time associated with optimization algorithms. Metaheuristics are general purpose heuristics that have been successfully used in solving difficult optimization problems in a reasonable computation time. In this work , we use and compare two multi objective metaheuristics, Multi-Objective Particle swarm Optimization, MOPSO, and Multi-Objective Gravitational Search Algorithm, MOGSA, to generate a set of approximately Pareto-optimal solutions in a single run. Two examples are studied, a nonlinear system consisting of two mobile robots tracking trajectories and avoiding obstacles and a linear multi variable system. The computation times and the quality of the solution in terms of the smoothness of the control signals and precision of tracking show that MOPSO can be an alternative for real time applications.

  3. Coordinated Multi-Objective Control of Regulating Resources in Multi-Area Power Systems with Large Penetration of Wind Power Generation

    DEFF Research Database (Denmark)

    Nyeng, Preben; Yang, Bo; Ma, Jian

    2008-01-01

    This paper describes a control algorithm for a Wide Area Energy Storage and Management System (WAEMS). The WAEMS is designed to meet the demand for fast, accurate and reliable regulation services in multi-area power systems with a significant share of wind power and other intermittent generation....... The means are utilization of flywheel energy storage units, hydro power generation, and energy exchange among the participating control areas. The objective of the control algorithm is to respond to the control signals from the different system operators, whilst optimizing the hydro power plant operation...... by reducing the tear and wear on the mechanical parts and improving the energy efficiency of the plant. The performance of the WAEMS is simulated using a mathematical model, including hydro power plant and flywheel energy storage models. ACE measurements from the California ISO and Bonneville Power...

  4. Towards Automatic Controller Design using Multi-Objective Evolutionary Algorithms

    DEFF Research Database (Denmark)

    Pedersen, Gerulf

    of evolutionary computation, a choice was made to use multi-objective algorithms for the purpose of aiding in automatic controller design. More specifically, the choice was made to use the Non-dominated Sorting Genetic Algorithm II (NSGAII), which is one of the most potent algorithms currently in use......, as the foundation for achieving the desired goal. While working with the algorithm, some issues arose which limited the use of the algorithm for unknown problems. These issues included the relative scale of the used fitness functions and the distribution of solutions on the optimal Pareto front. Some work has...

  5. Multi-objective intelligent coordinating optimization blending system based on qualitative and quantitative synthetic model

    Institute of Scientific and Technical Information of China (English)

    WANG Ya-lin; MA Jie; GUI Wei-hua; YANG Chun-hua; ZHANG Chuan-fu

    2006-01-01

    A multi-objective intelligent coordinating optimization strategy based on qualitative and quantitative synthetic model for Pb-Zn sintering blending process was proposed to obtain optimal mixture ratio. The mechanism and neural network quantitative models for predicting compositions and rule models for expert reasoning were constructed based on statistical data and empirical knowledge. An expert reasoning method based on these models were proposed to solve blending optimization problem, including multi-objective optimization for the first blending process and area optimization for the second blending process, and to determine optimal mixture ratio which will meet the requirement of intelligent coordination. The results show that the qualified rates of agglomerate Pb, Zn and S compositions are increased by 7.1%, 6.5% and 6.9%, respectively, and the fluctuation of sintering permeability is reduced by 7.0 %, which effectively stabilizes the agglomerate compositions and the permeability.

  6. Multi-objective Coordinated Control of Reactive Compensation Devices Among Multiple Substations%多站点无功补偿装置的多目标协调控制

    Institute of Scientific and Technical Information of China (English)

    董萍; 徐良德; 刘明波

    2014-01-01

    Being different from the separate compensation control mode used widely in 500 kV transformer substations currently, the multi-objective reactive power coordination control model was presented in this paper to overcome the drawback that reactive power compensation devices lack in coordination and have high active power loss. In this model, the minimum bus voltage deviation and total loss of reactive power compensation devices were taken as objective functions by bringing the lower-voltage reactors and capacitors into the SVC control system and considering the reactive power compensation devices interaction between different transformer substations. Due to the different sensitivity of control variables in control model and lack of the local search ability, control variables are divided into sensitive variables and non-sensitive variables, and then, the improved NSGA-II algorithm with secondary search ability was used to search the Pareto optimal solution set. Two substations in large power grid with strong voltage coupling were coordinated in different load level, the results obtained by proposed NSGA-II algorithm can provide a variety of optimal control strategies. Compared to the conventional NSGA-II algorithm and normal boundary intersection method, the improved NSGA-II algorithm has better convergence curve and distribution of the Pareto solution sets.%针对目前500 kV变电站中无功补偿装置所采用的单独补偿控制方式,提出一种多目标协调控制方式来克服无功补偿装置缺乏协调且损耗较大的不足。该方法将变电站内低容/低抗装置纳入SVC的控制体系,并考虑站与站之间无功补偿装置的相互影响,以节点电压偏差和无功补偿装置总损耗最小为目标建立多目标无功协调控制模型。根据无功协调控制中变量敏感度不同、局部搜索能力不足的特点,将控制变量划分为敏感变量和非敏感变量,采用具有二级搜索的改进NSGA-II算法

  7. Multi-Objective Hybrid Optimal Control for Interplanetary Mission Planning

    Science.gov (United States)

    Englander, Jacob

    2015-01-01

    Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. Because low-thrust trajectory design is tightly coupled with systems design, power and propulsion characteristics must be chosen as well. In addition, a time-history of control variables must be chosen which defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The methods is demonstrated on hypothetical mission to the main asteroid belt and to Deimos.

  8. Fault Detection and Isolation using Multi Objective Controller Design Techniques

    DEFF Research Database (Denmark)

    Stoustrup, Jakob; Niemann, Hans Henrik

    1996-01-01

    Abstract: This paper describes a method for designing fault detectionand isolation filters. The method is multi objective in the sense thatit follows optimization with arbitrarily mixed criteria specified ine.g. the QTR H-infinity or the QTR H^2 norm. Moreover,the involved optimization yields less...

  9. Electric Vehicle Charging and Discharging Coordination on Distribution Network Using Multi-Objective Particle Swarm Optimization and Fuzzy Decision Making

    Directory of Open Access Journals (Sweden)

    Dongqi Liu

    2016-03-01

    Full Text Available This paper proposed a optimal strategy for coordinated operation of electric vehicles (EVs charging and discharging with wind-thermal system. By aggregating a large number of EVs, the huge total battery capacity is sufficient to stabilize the disturbance of the transmission grid. Hence, a dynamic environmental dispatch model which coordinates a cluster of charging and discharging controllable EV units with wind farms and thermal plants is proposed. A multi-objective particle swarm optimization (MOPSO algorithm and a fuzzy decision maker are put forward for the simultaneous optimization of grid operating cost, CO2 emissions, wind curtailment, and EV users’ cost. Simulations are done in a 30 node system containing three traditional thermal plants, two carbon capture and storage (CCS thermal plants, two wind farms, and six EV aggregations. Contrast of strategies under different EV charging/discharging price is also discussed. The results are presented to prove the effectiveness of the proposed strategy.

  10. Synergy methodology for multi-objective operational control of reservoirs in Yellow River basin

    Institute of Scientific and Technical Information of China (English)

    HUANG; Qiang; CHANG; Jianxia; WANG; Yimin; PENG; Shaoming

    2004-01-01

    This paper presents an application of synergy methodology to a multiobjective operational control of reservoirs. This methodology enables a comprehensive consideration of multi-objectives which may be conflicting and non commensurate such as municipal and industrial water supply, flood protection, and hydroelectric power generation etc. On the basis of the synergy theory, a harmony degree model of subsystem was established to describe the coordination magnitude. Combined with information entropy, a harmony degree entropy was proposed to determine the water resources evolvement direction. While implementing the control, an initial scheme for reservoir operation was obtained from simulation first, then control was carried out according to the harmony degree and harmony degree entropy by applying synergy theory. The application of the methodology to reservoir system in the Yellow River was reported in this paper through a case study.

  11. Design of multi-objective damping controller for gate-controlled series capacitor

    Indian Academy of Sciences (India)

    Amin Safari; Navid Rezaei

    2014-04-01

    This paper proposes an optimization procedure based on eigenvalues to carry out the stabilization function of the Gate-Controlled Series Capacitor (GCSC) in a power system. It is aimed to provide a reliable damping framework by means of a GCSC based multi-objective damping controller. The proposed method employs Particle Swarm Optimization (PSO) to search for optimal parameter settings of a widely used multi-objective lead-lag damping controller. The eigenvalue analysis is considered as the cornerstone of the performed studies in order to investigate the multi-objective methodology in which the unstable or lightly damped modes are scheduled to effectively shift to some prescribed stability zones in the s-plane. The effectiveness of the suggested approach in damping local and interarea oscillations modes in a multi-machine power system, over a wide range of loading conditions, is confirmed through eigenvalue analysis and time simulation.

  12. Multi-objective optimization framework for networked predictive controller design.

    Science.gov (United States)

    Das, Sourav; Das, Saptarshi; Pan, Indranil

    2013-01-01

    Networked Control Systems (NCSs) often suffer from random packet dropouts which deteriorate overall system's stability and performance. To handle the ill effects of random packet losses in feedback control systems, closed over communication network, a state feedback controller with predictive gains has been designed. To achieve improved performance, an optimization based controller design framework has been proposed in this paper with Linear Matrix Inequality (LMI) constraints, to ensure guaranteed stability. Different conflicting objective functions have been optimized with Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The methodology proposed in this paper not only gives guaranteed closed loop stability in the sense of Lyapunov, even in the presence of random packet losses, but also gives an optimization trade-off between two conflicting time domain control objectives.

  13. Multi-objective robust controller synthesis for discrete-time systems with convex polytopic uncertain domain

    Institute of Scientific and Technical Information of China (English)

    ZHANG Yan-hu; YAN Wen-jun; LU Jian-ning; ZHAO Guang-zhou

    2005-01-01

    Multi-objective robust state-feedback controller synthesis problems for linear discrete-time uncertain systems are addressed. Based on parameter-dependent Lyapunov functions, the Gl2 and GH2 norm expressed in terms of LMI (Linear Matrix Inequality) characterizations are further generalized to cope with the robust analysis for convex polytopic uncertain system.Robust state-feedback controller synthesis conditions are also derived for this class of uncertain systems. Using the above results,multi-objective state-feedback controller synthesis procedures which involve the LMI optimization technique are developed and less conservative than the existing one. An illustrative example verified the validity of the approach.

  14. Ensemble-based multi-objective optimization of on-off control devices under geological uncertainty

    NARCIS (Netherlands)

    Fonseca, R.M.; Leeuwenburgh, O.; Rossa, E.D.; Hof, P.M.J. van den; Jansen, J.D.

    2015-01-01

    We consider robust ensemble-based (EnOpt) multi-objective production optimization of on-off inflow control devices (ICDs) for a sector model inspired on a real-field case. The use of on-off valves as optimization variables leads to a discrete control problem. We propose a re-parameterization of such

  15. Multi Objective Optimization of Coordinated Scheduling of Cranes and Vehicles at Container Terminals

    Directory of Open Access Journals (Sweden)

    Seyed Mahdi Homayouni

    2013-01-01

    Full Text Available According to previous researches, automated guided vehicles and quay cranes in container terminals have a high potential synergy. In this paper, a mixed integer programming model is formulated to optimize the coordinated scheduling of cranes and vehicles in container terminals. Objectives of the model are to minimize total traveling time of the vehicles and delays in tasks of cranes. A genetic algorithm is developed to solve the problem in reasonable computational time. The most appropriate control parameters for the proposed genetic algorithm are investigated in a medium size numerical test case. It is shown that balanced crossover and mutation rates have the best performance in finding a near optimal solution for the problem. Then, ten small size test cases are solved to evaluate the performance of the proposed optimization methods. The results show the applicability of the genetic algorithm since it can find near optimal solutions, precisely and accurately.

  16. Multi-objective evolutionary optimization of biological pest control with impulsive dynamics in soybean crops.

    Science.gov (United States)

    Cardoso, Rodrigo T N; da Cruz, André R; Wanner, Elizabeth F; Takahashi, Ricardo H C

    2009-08-01

    The biological pest control in agriculture, an environment-friendly practice, maintains the density of pests below an economic injury level by releasing a suitable quantity of their natural enemies. This work proposes a multi-objective numerical solution to biological pest control for soybean crops, considering both the cost of application of the control action and the cost of economic damages. The system model is nonlinear with impulsive control dynamics, in order to cope more effectively with the actual control action to be applied, which should be performed in a finite number of discrete time instants. The dynamic optimization problem is solved using the NSGA-II, a fast and trustworthy multi-objective genetic algorithm. The results suggest a dual pest control policy, in which the relative price of control action versus the associated additional harvest yield determines the usage of either a low control action strategy or a higher one.

  17. COORDINATED LOCATION, DISTRIBUTION AND INVENTORY DECISIONS IN SUPPLY CHAIN NETWORK DESIGN: A MULTI-OBJECTIVE APPROACH

    Directory of Open Access Journals (Sweden)

    G. Reza Nasiri

    2012-01-01

    Full Text Available

    ENGLISH ABSTRACT: This research presents an integrated multi-objective distribution model for use in simultaneous strategic and operational food supply chain (SC planning. The proposed method is adopted to allow use of a performance measurement system that includes conflicting objectives such as distribution costs, customer service level (safety stock holding, resource utilisation, and the total delivery time, with reference to multiple warehouse capacities and uncertain forecast demands. To deal with these objectives and enable the decision makers (DMs to evaluate a greater number of alternative solutions, three different approaches are implemented in the proposed solution procedure. A detailed case study derived from food industrial data is used to illustrate the preference of the proposed approach. The proposed method yields an efficient solution and an overall degree of DMs’ satisfaction with the determined objective values.

    AFRIKAANSE OPSOMMING: Die navorsing behandel ’n geïntegreerde multidoelwit distribusiemodel vir strategiese beplanning van ’n voedseltoevoerketting. Om met die model doelmatig te werk, moet ’n versameling van randvoorwaardes hanteer word om die saamgestelde optimiseringsdoelwit te bereik teen ’n agtergrond van uiteenlopende sienings.

  18. Multi-objective control for active vehicle suspension with wheelbase preview

    Science.gov (United States)

    Li, Panshuo; Lam, James; Cheung, Kie Chung

    2014-10-01

    This paper presents a multi-objective control method with wheelbase preview for active vehicle suspension. A four-degree-of-freedom half-car model with active suspension is considered in this study. H∞ norm and generalized H2 norm are used to improve ride quality and ensure that hard constraints are satisfied. Disturbances at the front wheel are obtained as preview information for the rear wheel. Static output-feedback is utilized in designing controllers, the solution is derived by iterative linear matrix inequality (ILMI) and cone complementarity linearization (CCL) algorithms. Simulation results confirm that multi-objective control with wheelbase preview achieves a significant improvement of ride quality (a maximum 27 percent and 60 percent improvement on vertical and angular acceleration, respectively) comparing with that of control without preview, while suspension deflections, tyre deflections and actuator forces remaining within given bounds. The extent of the improvement on the ride quality for different amount of preview information used is also illustrated.

  19. Robust multi-objective optimization of state feedback controllers for heat exchanger system with probabilistic uncertainty

    Science.gov (United States)

    Lotfi, Babak; Wang, Qiuwang

    2013-07-01

    The performance of thermal control systems has, in recent years, improved in numerous ways due to developments in control theory and information technology. The shell-and-tube heat exchanger (STHX) is a medium where heat transfer process occurred. The accuracy of the heat exchanger depends on the performance of both elements. Therefore, both components need to be controlled in order to achieve a substantial result in the process. For this purpose, the actual dynamics of both shell and tube of the heat exchanger is crucial. In this paper, optimal reliability-based multi-objective Pareto design of robust state feedback controllers for a STHX having parameters with probabilistic uncertainties. Accordingly, the probabilities of failure of those objective functions are also considered in the reliability-based design optimization (RBDO) approach. A new multi-objective uniform-diversity genetic algorithm (MUGA) is presented and used for Pareto optimum design of linear state feedback controllers for STHX problem. In this way, Pareto front of optimum controllers is first obtained for the nominal deterministic STHX using the conflicting objective functions in time domain. Such Pareto front is then obtained for STHX having probabilistic uncertainties in its parameters using the statistical moments of those objective functions through a Hammersley Sequence Sampling (HSS) approach. It is shown that multi-objective reliability-based Pareto optimization of the robust state feedback controllers using MUGA includes those that may be obtained by various crisp threshold values of probability of failures and, thus, remove the difficulty of selecting suitable crisp values. Besides, the multi-objective Pareto optimization of such robust feedback controllers using MUGA unveils some very important and informative trade-offs among those objective functions. Consequently, some optimum robust state feedback controllers can be compromisingly chosen from the Pareto frontiers.

  20. Performance Optimizing Multi-Objective Adaptive Control with Time-Varying Model Reference Modification

    Science.gov (United States)

    Nguyen, Nhan T.; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

    2017-01-01

    This paper presents a new adaptive control approach that involves a performance optimization objective. The problem is cast as a multi-objective optimal control. The control synthesis involves the design of a performance optimizing controller from a subset of control inputs. The effect of the performance optimizing controller is to introduce an uncertainty into the system that can degrade tracking of the reference model. An adaptive controller from the remaining control inputs is designed to reduce the effect of the uncertainty while maintaining a notion of performance optimization in the adaptive control system.

  1. Collision-free coordination of fiber positioners in multi-object spectrographs

    Science.gov (United States)

    Makarem, Laleh; Kneib, Jean-Paul; Gillet, Denis

    2016-07-01

    Many fiber-fed spectroscopic survey projects, such as DESI, PFS and MOONS, will use thousands of fiber positioners packed at a focal plane. To maximize observation time, the positioners need to move simultaneously and reach their targets swiftly. We have previously presented a motion planning method based on a decentralized navigation function for the collision-free coordination of the fiber positioners in DESI. In MOONS, the end effector of each positioner handling the fiber can reach the centre of its neighbours. There is therefore a risk of collision with up to 18 surrounding positioners in the chosen dense hexagonal configuration. Moreover, the length of the second arm of the positioner is almost twice the length of the first one. As a result, the geometry of the potential collision zone between two positioners is not limited to the extremity of their end-effector, but surrounds the second arm. In this paper, we modify the navigation function to take into account the larger collision zone resulting from the extended geometrical shape of the positioners. The proposed navigation function takes into account the configuration of the positioners as well as the constraints on the actuators, such as their maximal velocity and their mechanical clearance. Considering the fact that all the positioners' bases are fixed to the focal plane, collisions can occur locally and the risk of collision is limited to the 18 surrounding positioners. The decentralizing motion planning and trajectory generation takes advantage of this limited number of positioners and the locality of collisions, hence significantly reduces the complexity of the algorithm to a linear order. The linear complexity ensures short computation time. In addition, the time needed to move all the positioners to their targets is independent of the number of positioners. These two key advantages of the chosen decentralization approach turn this method to a promising solution for the collision-free motion

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

    Directory of Open Access Journals (Sweden)

    Sanjay Kr. Singh

    2014-05-01

    Full Text Available 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 not being connected to the grid; hence is usually used in refineries as steam turbine, where a hydraulic governor is used for the speed control. The PID controller for the system has been designed and implemented using MATLAB and SIMULINK and the results of the design methods have been compared, analysed and conclusions indicates that the significant improvement of results have been obtained by the Multi-Objective GA based optimization of PID as much faster response is obtained as compared to the ordinary GA and Ziegler Nichols method.

  3. Development of a multi-objective coagulation system for long-term fouling control in dead-end ultrafiltration

    NARCIS (Netherlands)

    Zondervan, Edwin; Blankert, Bastiaan; Betlem, Ben H.L.; Roffel, Brian

    2008-01-01

    In this paper, a multi-objective control system has been developed and experimentally tested. The multi-objective control system can be effectively used to control short-term fouling as well as long-term fouling. In an earlier study it was found that coagulant dosing in ultrafiltration can be used e

  4. Development of a multi-objective coagulation system for long-term fouling control in dead-end ultrafiltration

    NARCIS (Netherlands)

    Zondervan, Edwin; Blankert, Bastiaan; Betlem, Ben H. L.; Roffel, Brian

    2008-01-01

    In this paper, a multi-objective control system has been developed and experimentally tested. The multi-objective control system can be effectively used to control short-term fouling as well as long-term fouling. In an earlier Study it was found that coagulant dosing in ultrafiltration can be used e

  5. Multi-Objective Coordinated Path Planning for a Team of UAVs in a Dynamic Environment

    Science.gov (United States)

    2014-06-01

    fuel. The need to coordinate multiple UAVs temporally and/or spatially while avoiding collisions with static obstacles (e.g., mountainous terrain...geographic location loc (l), UAV requirements sgna l , start time start lt , processing time process lt , and deadline deadline lt . Each UAV...arrive k depart travel arrive ki k ijkt kj ijkt depart arrive ki ki depart arrive a ki ki l t k a t t x t M x k i j t b t t i loc l k c t t i loc

  6. Coordination strategies for distribution grid congestion management in a Multi-Actor, Multi-Objective Setting

    DEFF Research Database (Denmark)

    Andersen, Peter Bach; Hu, Junjie; Heussen, Kai

    2012-01-01

    and the handling of real-time events for reliable grid operation. This paper presents an analysis of key stakeholders in terms of their objectives and key operations. Three potential strategies for congestion management are presented and evaluated based on their complexity of implementation, the value and benefits...... the interactions between the stakeholders involved, mainly considering the distribution grid congestion problem, and conceptualize several approaches by which their diverse, potentially conflicting, objectives can be coordinated. A key aspect to be considered is the relationship between the operational planning...

  7. Method of Designing Missile Controller Based on Multi-Objective Optimization

    Institute of Scientific and Technical Information of China (English)

    LIN Bo; MENG Xiu-yun; LIU Zao-zhen

    2006-01-01

    A method of designing robust controller based on genetic algorithm is presented in order to overcome the drawback of manual modification and trial in designing the control system of missile. Specification functions which reflect the dynamic performance in time domain and robustness in frequency domain are presented,then dynamic/static performance, control cost and robust stability are incorporated into a multi-objective optimization problem. Genetic algorithm is used to solve the problem and achieve the optimal controller directly.Simulation results show that the controller provides a good stability and offers a good dynamic performance in a large flight envelope. The results also validate the effectiveness of the method.

  8. 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.

  9. Multi-objective coordinated control for STATCOM and generator excitation based on objective holographic feedbacks%基于目标全息反馈的STATCOM与发电机励磁的多目标非线性协调控制

    Institute of Scientific and Technical Information of China (English)

    李啸骢; 谢醉冰; 肖明; 赵亚楠

    2013-01-01

    针对以往静止同步补偿器(STATCOM)数学模型具有较多约束条件的问题,以 STATCOM 内部并联电容的直流电压Udc为切入点,采用STATCOM的脉冲控制角θ和STATCOM超前系统电压角α为控制量,提出一种新型的STATCOM数学模型。借助非线性多目标控制设计理念,选取6个跟踪状态目标;基于目标全息反馈控制原理,将非线性系统的多目标控制问题转换到线性系统中,得到包含全部控制目标反馈信息的非线性控制律u ,完成了凸极式发电机励磁与STATCOM协调控制策略的设计,并通过单机无穷大系统对其进行暂态仿真。仿真结果表明:利用目标全息反馈非线性控制设计方法设计的协调控制律能较好地改善发电机的输出特性,有效地抑制系统电压的静态偏移,改善电力系统的暂态稳定性,提升系统协调控制的动、静态性能。%In view of the previous mathematical model of static synchronous compensator (STATCOM) with many constraint conditions, taking the internal shunt capacitance U dc of STATCOM as the breakthrough point, using STATCOM’s impulse control angleθand its advanced system voltage angleαas control quantities, a new type of mathematical model of STATCOM is put forward. Using nonlinear multi objective control design concept, six tracking targets are selected. According to object holographic feedback control principle, in order to get the nonlinear control law containing all target control feedback information, the nonlinear system multi objective control problem is switched to a linear system. The design of coordination and control strategy is completed which contain salient-pole type generator excitation and STATCOM. The simulation results show that the coordination control law which is designed based on the objective holographic feedbacks can better improve the generator output characteristics, effectively inhibit the system voltage offset, and improve

  10. 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

    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...

  11. 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.

  12. Integrated fuzzy logic and genetic algorithms for multi-objective control of structures using MR dampers

    Science.gov (United States)

    Yan, Gang; Zhou, Lily L.

    2006-09-01

    This study presents a design strategy based on genetic algorithms (GA) for semi-active fuzzy control of structures that have magnetorheological (MR) dampers installed to prevent damage from severe dynamic loads such as earthquakes. The control objective is to minimize both the maximum displacement and acceleration responses of the structure. Interactive relationships between structural responses and input voltages of MR dampers are established by using a fuzzy controller. GA is employed as an adaptive method for design of the fuzzy controller, which is here known as a genetic adaptive fuzzy (GAF) controller. The multi-objectives are first converted to a fitness function that is used in standard genetic operations, i.e. selection, crossover, and mutation. The proposed approach generates an effective and reliable fuzzy logic control system by powerful searching and self-learning adaptive capabilities of GA. Numerical simulations for single and multiple damper cases are given to show the effectiveness and efficiency of the proposed intelligent control strategy.

  13. 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.

  14. Multi-objective Genetic Algorithm for System Identification and Controller Optimization of Automated Guided Vehicle

    Directory of Open Access Journals (Sweden)

    Xing Wu

    2011-07-01

    Full Text Available This paper presents a multi-objective genetic algorithm (MOGA with Pareto optimality and elitist tactics for the control system design of automated guided vehicle (AGV. The MOGA is used to identify AGV driving system model and optimize its servo control system sequentially. In system identification, the model identified by least square method is adopted as an evolution tutor who selects the individuals having balanced performances in all objectives as elitists. In controller optimization, the velocity regulating capability required by AGV path tracking is employed as decision-making preferences which select Pareto optimal solutions as elitists. According to different objectives and elitist tactics, several sub-populations are constructed and they evolve concurrently by using independent reproduction, neighborhood mutation and heuristic crossover. The lossless finite precision method and the multi-objective normalized increment distance are proposed to keep the population diversity with a low computational complexity. Experiment results show that the cascaded MOGA have the capability to make the system model consistent with AGV driving system both in amplitude and phase, and to make its servo control system satisfy the requirements on dynamic performance and steady-state accuracy in AGV path tracking.

  15. Multi-objective H ∞ control for vehicle active suspension systems with random actuator delay

    Science.gov (United States)

    Li, Hongyi; Liu, Honghai; Hand, Steve; Hilton, Chris

    2012-12-01

    This article is concerned with the problem of multi-objective H ∞ control for vehicle active suspension systems with random actuator delay, which can be represented by signal probability distribution. First, the dynamical equations of a quarter-car suspension model are established for the control design purpose. Secondly, when taking into account vehicle performance requirements, namely, ride comfort, suspension deflection and the probability distributed actuator delay, we present the corresponding dynamic system, which will be transformed to the stochastic system for the problem of multi-objective H ∞ controller design. Third, based on the stochastic stability theory, the state feedback controller is proposed to render that the closed-loop system is exponentially stable in mean-square while simultaneously satisfying H ∞ performance and the output constraint requirement. The presented condition is expressed in the form of convex optimisation problems so that it can be efficiently solved via standard numerical software. Finally, a practical design example is given to demonstrate the effectiveness of the proposed method.

  16. Multi-objective optimization based on Genetic Algorithm for PID controller tuning

    Institute of Scientific and Technical Information of China (English)

    WANG Guo-liang; YAN Wei-wu; SHAO Hui-he

    2009-01-01

    To get the satisfying performance of a PID controller, this paper presents a novel Pareto - based multi-objective genetic algorithm ( MOGA), which can be used to find the appropriate setting of the PID controller by analyzing the pareto optimal surfaces. Rated settings of the controller by two criteria, the error between output and reference signals and control moves, are listed on the pareto surface. Appropriate setting can be chosen under a balance between two criteria for different control purposes. A controller tuning problem for a plant with high order and time delay is chosen as an example. Simulation results show that the method of MOGA is more efficient compared with traditional tuning methods.

  17. Mixed Gl2/GH2 multi-channel multi-objective control synthesis for discrete time systems

    Institute of Scientific and Technical Information of China (English)

    颜文俊; 张森林

    2004-01-01

    This paper proposes a new approach for multi-objective robust control.The approach extends the standard generalized l2(Gl2)and generalized H2(GH2)conditions to a set of new linear matrix inequality(LMI)constraints based on a new stability condition.A technique for variable parameterization is introduced to the multi-objective control problem to preserve the linearity of the synthesis variables.Consequently,the multi-channel multi-objective mixed Gl2/GH2 control problem can be solved less conservatively using computationally tractable algorithms developed in the paper.

  18. Multi-objective Optimization of Controller for Process with Reverse Response and Dead Time

    Institute of Scientific and Technical Information of China (English)

    WANG Guo-liang; SHAO Hui-he

    2009-01-01

    Due to the difficulty of controlling the process with inverse response and dead time, a Multi-objective Optimization based on Genetic Algorithm (MOGA) method for tuning of proportional-integral-derivative (PID) controller is proposed. The settings of the controller are valued by two criteria, the error between output and reference signals and control moves. An appropriate set of Pareto optimal setting of the PID controller is founded by analyzing the results of Pareto optimal surfaces for balancing the two criteria. A high order process with inverse response and dead time is used to illustrate the results of the proposed method. And the efficiency and robustness of the tuning method are evident compared with methods in recent literature.

  19. Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks

    Institute of Scientific and Technical Information of China (English)

    Jie Zhang

    2006-01-01

    In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a "global" nonlinear long-range prediction model through the fuzzy conjunction of a number of "local" linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.

  20. Multi-objective control of nonlinear boiler-turbine dynamics with actuator magnitude and rate constraints.

    Science.gov (United States)

    Chen, Pang-Chia

    2013-01-01

    This paper investigates multi-objective controller design approaches for nonlinear boiler-turbine dynamics subject to actuator magnitude and rate constraints. System nonlinearity is handled by a suitable linear parameter varying system representation with drum pressure as the system varying parameter. Variation of the drum pressure is represented by suitable norm-bounded uncertainty and affine dependence on system matrices. Based on linear matrix inequality algorithms, the magnitude and rate constraints on the actuator and the deviations of fluid density and water level are formulated while the tracking abilities on the drum pressure and power output are optimized. Variation ranges of drum pressure and magnitude tracking commands are used as controller design parameters, determined according to the boiler-turbine's operation range.

  1. Multi-Objective Hybrid Optimal Control for Multiple-Flyby Low-Thrust Mission Design

    Science.gov (United States)

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

    2015-01-01

    Preliminary design of low-thrust interplanetary missions is a highly complex process. The mission designer must choose discrete parameters such as the number of flybys, the bodies at which those flybys are performed, and in some cases the final destination. In addition, a time-history of control variables must be chosen that defines the trajectory. There are often many thousands, if not millions, of possible trajectories to be evaluated. The customer who commissions a trajectory design is not usually interested in a point solution, but rather the exploration of the trade space of trajectories between several different objective functions. This can be a very expensive process in terms of the number of human analyst hours required. An automated approach is therefore very desirable. This work presents such an approach by posing the mission design problem as a multi-objective hybrid optimal control problem. The method is demonstrated on a hypothetical mission to the main asteroid belt.

  2. A multi-objective dynamic programming approach to constrained discrete-time optimal control

    Energy Technology Data Exchange (ETDEWEB)

    Driessen, B.J.; Kwok, K.S.

    1997-09-01

    This work presents a multi-objective differential dynamic programming approach to constrained discrete-time optimal control. In the backward sweep of the dynamic programming in the quadratic sub problem, the sub problem input at a stage or time step is solved for in terms of the sub problem state entering that stage so as to minimize the summed immediate and future cost subject to minimizing the summed immediate and future constraint violations, for all such entering states. The method differs from previous dynamic programming methods, which used penalty methods, in that the constraints of the sub problem, which may include terminal constraints and path constraints, are solved exactly if they are solvable; otherwise, their total violation is minimized. Again, the resulting solution of the sub problem is an input history that minimizes the quadratic cost function subject to being a minimizer of the total constraint violation. The expected quadratic convergence of the proposed algorithm is demonstrated on a numerical example.

  3. MULTI-OBJECTIVE PID CONTROLLER BASED ON ADAPTIVE WEIGHTED PSO WITH APPLICATION TO STEAM TEMPERATURE CONTROL IN BOILERS

    Directory of Open Access Journals (Sweden)

    C.Agees Kumar

    2010-07-01

    Full Text Available PID controller is widely used for main steam temperature control of boiler unit in thermal power plant. To avoid the drawback of current PID design methods, this paper presents a new design method for multi-objective PID controller to synthetically consider system requirement in reliability and robustness. Adaptive weighted PSO (AWPSO technique is applied to the parameter optimization design. The optimization problem considered is highly nonlinear, complex, with multiple objectives and constraints. The simulation results on an actual main steam temperature control system indicate that, the multi-objective PID controller designed by presented method, can improve the dynamic performance of main steam temperature control system, with good robustness ability.

  4. Multi-Objective Predictive Balancing Control of Battery Packs Based on Predictive Current

    Directory of Open Access Journals (Sweden)

    Wenbiao Li

    2016-04-01

    Full Text Available Various balancing topology and control methods have been proposed for the inconsistency problem of battery packs. However, these strategies only focus on a single objective, ignore the mutual interaction among various factors and are only based on the external performance of the battery pack inconsistency, such as voltage balancing and state of charge (SOC balancing. To solve these problems, multi-objective predictive balancing control (MOPBC based on predictive current is proposed in this paper, namely, in the driving process of an electric vehicle, using predictive control to predict the battery pack output current the next time. Based on this information, the impact of the battery pack temperature caused by the output current can be obtained. Then, the influence is added to the battery pack balancing control, which makes the present degradation, temperature, and SOC imbalance achieve balance automatically due to the change of the output current the next moment. According to MOPBC, the simulation model of the balancing circuit is built with four cells in Matlab/Simulink. The simulation results show that MOPBC is not only better than the other traditional balancing control strategies but also reduces the energy loss in the balancing process.

  5. A Frequency Control Approach for Hybrid Power System Using Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    Mohammed Elsayed Lotfy

    2017-01-01

    Full Text Available A hybrid power system uses many wind turbine generators (WTG and solar photovoltaics (PV in isolated small areas. However, the output power of these renewable sources is not constant and can diverge quickly, which has a serious effect on system frequency and the continuity of demand supply. In order to solve this problem, this paper presents a new frequency control scheme for a hybrid power system to ensure supplying a high-quality power in isolated areas. The proposed power system consists of a WTG, PV, aqua-electrolyzer (AE, fuel cell (FC, battery energy storage system (BESS, flywheel (FW and diesel engine generator (DEG. Furthermore, plug-in hybrid electric vehicles (EVs are implemented at the customer side. A full-order observer is utilized to estimate the supply error. Then, the estimated supply error is considered in a frequency domain. The high-frequency component is reduced by BESS and FW; while the low-frequency component of supply error is mitigated using FC, EV and DEG. Two PI controllers are implemented in the proposed system to control the system frequency and reduce the supply error. The epsilon multi-objective genetic algorithm ( ε -MOGA is applied to optimize the controllers’ parameters. The performance of the proposed control scheme is compared with that of recent well-established techniques, such as a PID controller tuned by the quasi-oppositional harmony search algorithm (QOHSA. The effectiveness and robustness of the hybrid power system are investigated under various operating conditions.

  6. A multi-objective approach to the design of low thrust space trajectories using optimal control

    Science.gov (United States)

    Dellnitz, Michael; Ober-Blöbaum, Sina; Post, Marcus; Schütze, Oliver; Thiere, Bianca

    2009-11-01

    In this article, we introduce a novel three-step approach for solving optimal control problems in space mission design. We demonstrate its potential by the example task of sending a group of spacecraft to a specific Earth L 2 halo orbit. In each of the three steps we make use of recently developed optimization methods and the result of one step serves as input data for the subsequent one. Firstly, we perform a global and multi-objective optimization on a restricted class of control functions. The solutions of this problem are (Pareto-)optimal with respect to Δ V and flight time. Based on the solution set, a compromise trajectory can be chosen suited to the mission goals. In the second step, this selected trajectory serves as initial guess for a direct local optimization. We construct a trajectory using a more flexible control law and, hence, the obtained solutions are improved with respect to control effort. Finally, we consider the improved result as a reference trajectory for a formation flight task and compute trajectories for several spacecraft such that these arrive at the halo orbit in a prescribed relative configuration. The strong points of our three-step approach are that the challenging design of good initial guesses is handled numerically by the global optimization tool and afterwards, the last two steps only have to be performed for one reference trajectory.

  7. Scalable multi-objective control for large scale water resources systems under uncertainty

    Science.gov (United States)

    Giuliani, Matteo; Quinn, Julianne; Herman, Jonathan; Castelletti, Andrea; Reed, Patrick

    2016-04-01

    The use of mathematical models to support the optimal management of environmental systems is rapidly expanding over the last years due to advances in scientific knowledge of the natural processes, efficiency of the optimization techniques, and availability of computational resources. However, undergoing changes in climate and society introduce additional challenges for controlling these systems, ultimately motivating the emergence of complex models to explore key causal relationships and dependencies on uncontrolled sources of variability. In this work, we contribute a novel implementation of the evolutionary multi-objective direct policy search (EMODPS) method for controlling environmental systems under uncertainty. The proposed approach combines direct policy search (DPS) with hierarchical parallelization of multi-objective evolutionary algorithms (MOEAs) and offers a threefold advantage: the DPS simulation-based optimization can be combined with any simulation model and does not add any constraint on modeled information, allowing the use of exogenous information in conditioning the decisions. Moreover, the combination of DPS and MOEAs prompts the generation or Pareto approximate set of solutions for up to 10 objectives, thus overcoming the decision biases produced by cognitive myopia, where narrow or restrictive definitions of optimality strongly limit the discovery of decision relevant alternatives. Finally, the use of large-scale MOEAs parallelization improves the ability of the designed solutions in handling the uncertainty due to severe natural variability. The proposed approach is demonstrated on a challenging water resources management problem represented by the optimal control of a network of four multipurpose water reservoirs in the Red River basin (Vietnam). As part of the medium-long term energy and food security national strategy, four large reservoirs have been constructed on the Red River tributaries, which are mainly operated for hydropower

  8. Multi-objective control of a full-car model using linear-matrix-inequalities and fixed-order optimisation

    Science.gov (United States)

    Türkay, Semiha; Akçay, Hüseyin

    2014-03-01

    This paper studies multi-objective control of a full-vehicle suspension excited by random road disturbances. The control problem is first formulated as a mixed ℋ2/ℋ∞ synthesis problem and an output-feedback solution is obtained by using linear-matrix-inequalities. Next, the multi-objective control problem is re-formulated as a non-convex and non-smooth optimisation problem with controller order restricted to be less than the vehicle model order. For a range of orders, controllers are synthesised by using the HIFOO toolbox. The efficacy of the presented procedures are demonstrated by several design examples.

  9. Multi-Objective Hybrid Optimal Control for Multiple-Flyby Interplanetary Mission Design Using Chemical Propulsion

    Science.gov (United States)

    Englander, Jacob; Vavrina, Matthew

    2015-01-01

    The customer (scientist or project manager) most often does not want just one point solution to the mission design problem Instead, an exploration of a multi-objective trade space is required. For a typical main-belt asteroid mission the customer might wish to see the trade-space of: Launch date vs. Flight time vs. Deliverable mass, while varying the destination asteroid, planetary flybys, launch year, etcetera. To address this question we use a multi-objective discrete outer-loop which defines many single objective real-valued inner-loop problems.

  10. Multi-objective decoupling algorithm for active distance control of intelligent hybrid electric vehicle

    Science.gov (United States)

    Luo, Yugong; Chen, Tao; Li, Keqiang

    2015-12-01

    The paper presents a novel active distance control strategy for intelligent hybrid electric vehicles (IHEV) with the purpose of guaranteeing an optimal performance in view of the driving functions, optimum safety, fuel economy and ride comfort. Considering the complexity of driving situations, the objects of safety and ride comfort are decoupled from that of fuel economy, and a hierarchical control architecture is adopted to improve the real-time performance and the adaptability. The hierarchical control structure consists of four layers: active distance control object determination, comprehensive driving and braking torque calculation, comprehensive torque distribution and torque coordination. The safety distance control and the emergency stop algorithms are designed to achieve the safety and ride comfort goals. The optimal rule-based energy management algorithm of the hybrid electric system is developed to improve the fuel economy. The torque coordination control strategy is proposed to regulate engine torque, motor torque and hydraulic braking torque to improve the ride comfort. This strategy is verified by simulation and experiment using a forward simulation platform and a prototype vehicle. The results show that the novel control strategy can achieve the integrated and coordinated control of its multiple subsystems, which guarantees top performance of the driving functions and optimum safety, fuel economy and ride comfort.

  11. A novel multi-objective electromagnetism-like mechanism algorithm with applications in reservoir flood control operation.

    Science.gov (United States)

    Ouyang, Shuo; Zhou, Jianzhong; Qin, Hui; Liao, Xiang; Wang, Hao

    2014-01-01

    Reservoir flood control operation (RFCO) is a complex problem that involves various constraints and purposes, which include the safety of the dam, watershed flood control and navigation. These objectives often conflict with each other. Thus, traditional methods have difficulty in solving the multi-objective problem efficiently. In this paper, a multi-objective self-adaptive electromagnetism-like mechanism (MOSEM) algorithm is introduced in the local searching operation of the proposed method. To enhance the optimization ability of EM, a self-adaptive parameter is applied in the local search operation of MOSEM for adjusting the values of parameters dynamically. Moreover, MOSEM is tested by several benchmark test problems and compared with some well-known multi-objective evolutionary algorithms. A case study is also used for solving RFCO problems of the Three Georges Reservoir by using the multi-objective cultured differential evolution (MOCDE), non-dominated sorting genetic algorithm-II (NSGA-II) and proposed MOSEM methods. The study results reveal that MOSEM can provide alternative Pareto-optimal solutions (POS) with better convergence properties and diversification.

  12. Multi-objective optimization of the control strategy of electric vehicle electro-hydraulic composite braking system with genetic algorithm

    OpenAIRE

    Zhang Fengjiao; Wei Minxiang

    2015-01-01

    Optimization of the control strategy plays an important role in improving the performance of electric vehicles. In order to improve the braking stability and recover the braking energy, a multi-objective genetic algorithm is applied to optimize the key parameters in the control strategy of electric vehicle electro-hydraulic composite braking system. Various limitations are considered in the optimization process, and the optimization results are verified by a software simulation platform of el...

  13. A diagnostic assessment of evolutionary algorithms for multi-objective surface water reservoir control

    Science.gov (United States)

    Zatarain Salazar, Jazmin; Reed, Patrick M.; Herman, Jonathan D.; Giuliani, Matteo; Castelletti, Andrea

    2016-06-01

    Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs' candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs' abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ɛ-MOEA, the auto-adaptive Borg MOEA, and ɛ-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity.

  14. Multi-Objective Genetic Algorithm Optimisation Approach for the Geometrical Design of an Active Noise Control Systems

    Directory of Open Access Journals (Sweden)

    N. Jafferi

    2009-09-01

    Full Text Available This paper focuses on the geometrical design of active noise control (ANC in free- field propagation medium. The development and performance assessment uses genetic optimisation techniques to arrange system components so as to satisfy several performance requirements, such as physical extent of cancellation, controller design restriction and system stability. The ANC system design can be effectively addressed if it is considered as multi – objective optimisation problems. The multi-objective genetic algorithms (MOGAs are well suited to the design of an ANC system and the approach used for it is based on a multi - objective method, with which the physical extent of cancellation and relative stability assessment are dealt with simultaneously.

  15. A mask quality control tool for the OSIRIS multi-object spectrograph

    Science.gov (United States)

    López-Ruiz, J. C.; Vaz Cedillo, Jacinto Javier; Ederoclite, Alessandro; Bongiovanni, Ángel; González Escalera, Víctor

    2012-09-01

    OSIRIS multi object spectrograph uses a set of user-customised-masks, which are manufactured on-demand. The manufacturing process consists of drilling the specified slits on the mask with the required accuracy. Ensuring that slits are on the right place when observing is of vital importance. We present a tool for checking the quality of the process of manufacturing the masks which is based on analyzing the instrument images obtained with the manufactured masks on place. The tool extracts the slit information from these images, relates specifications with the extracted slit information, and finally communicates to the operator if the manufactured mask fulfills the expectations of the mask designer. The proposed tool has been built using scripting languages and using standard libraries such as opencv, pyraf and scipy. The software architecture, advantages and limits of this tool in the lifecycle of a multiobject acquisition are presented.

  16. Multi-Objective Flight Control for Drag Minimization and Load Alleviation of High-Aspect Ratio Flexible Wing Aircraft

    Science.gov (United States)

    Nguyen, Nhan; Ting, Eric; Chaparro, Daniel; Drew, Michael; Swei, Sean

    2017-01-01

    As aircraft wings become much more flexible due to the use of light-weight composites material, adverse aerodynamics at off-design performance can result from changes in wing shapes due to aeroelastic deflections. Increased drag, hence increased fuel burn, is a potential consequence. Without means for aeroelastic compensation, the benefit of weight reduction from the use of light-weight material could be offset by less optimal aerodynamic performance at off-design flight conditions. Performance Adaptive Aeroelastic Wing (PAAW) technology can potentially address these technical challenges for future flexible wing transports. PAAW technology leverages multi-disciplinary solutions to maximize the aerodynamic performance payoff of future adaptive wing design, while addressing simultaneously operational constraints that can prevent the optimal aerodynamic performance from being realized. These operational constraints include reduced flutter margins, increased airframe responses to gust and maneuver loads, pilot handling qualities, and ride qualities. All of these constraints while seeking the optimal aerodynamic performance present themselves as a multi-objective flight control problem. The paper presents a multi-objective flight control approach based on a drag-cognizant optimal control method. A concept of virtual control, which was previously introduced, is implemented to address the pair-wise flap motion constraints imposed by the elastomer material. This method is shown to be able to satisfy the constraints. Real-time drag minimization control is considered to be an important consideration for PAAW technology. Drag minimization control has many technical challenges such as sensing and control. An initial outline of a real-time drag minimization control has already been developed and will be further investigated in the future. A simulation study of a multi-objective flight control for a flight path angle command with aeroelastic mode suppression and drag

  17. A multi-objective optimization tool for the selection and placement of BMPs for pesticide control

    Science.gov (United States)

    Maringanti, C.; Chaubey, I.; Arabi, M.; Engel, B.

    2008-07-01

    Pesticides (particularly atrazine used in corn fields) are the foremost source of water contamination in many of the water bodies in Midwestern corn belt, exceeding the 3 ppb MCL established by the U.S. EPA for drinking water. Best management practices (BMPs), such as buffer strips and land management practices, have been proven to effectively reduce the pesticide pollution loads from agricultural areas. However, selection and placement of BMPs in watersheds to achieve an ecologically effective and economically feasible solution is a daunting task. BMP placement decisions under such complex conditions require a multi-objective optimization algorithm that would search for the best possible solution that satisfies the given watershed management objectives. Genetic algorithms (GA) have been the most popular optimization algorithms for the BMP selection and placement problem. Most optimization models also had a dynamic linkage with the water quality model, which increased the computation time considerably thus restricting them to apply models on field scale or relatively smaller (11 or 14 digit HUC) watersheds. However, most previous works have considered the two objectives individually during the optimization process by introducing a constraint on the other objective, therefore decreasing the degree of freedom to find the solution. In this study, the optimization for atrazine reduction is performed by considering the two objectives simultaneously using a multi-objective genetic algorithm (NSGA-II). The limitation with the dynamic linkage with a distributed parameter watershed model was overcome through the utilization of a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The model was used for the selection and placement of BMPs in Wildcat Creek Watershed (located in Indiana, for atrazine reduction. The most ecologically effective solution from the model had an annual atrazine concentration reduction

  18. A multi-objective optimization tool for the selection and placement of BMPs for pesticide control

    Directory of Open Access Journals (Sweden)

    C. Maringanti

    2008-07-01

    Full Text Available Pesticides (particularly atrazine used in corn fields are the foremost source of water contamination in many of the water bodies in Midwestern corn belt, exceeding the 3 ppb MCL established by the U.S. EPA for drinking water. Best management practices (BMPs, such as buffer strips and land management practices, have been proven to effectively reduce the pesticide pollution loads from agricultural areas. However, selection and placement of BMPs in watersheds to achieve an ecologically effective and economically feasible solution is a daunting task. BMP placement decisions under such complex conditions require a multi-objective optimization algorithm that would search for the best possible solution that satisfies the given watershed management objectives. Genetic algorithms (GA have been the most popular optimization algorithms for the BMP selection and placement problem. Most optimization models also had a dynamic linkage with the water quality model, which increased the computation time considerably thus restricting them to apply models on field scale or relatively smaller (11 or 14 digit HUC watersheds. However, most previous works have considered the two objectives individually during the optimization process by introducing a constraint on the other objective, therefore decreasing the degree of freedom to find the solution. In this study, the optimization for atrazine reduction is performed by considering the two objectives simultaneously using a multi-objective genetic algorithm (NSGA-II. The limitation with the dynamic linkage with a distributed parameter watershed model was overcome through the utilization of a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The model was used for the selection and placement of BMPs in Wildcat Creek Watershed (located in Indiana, for atrazine reduction. The most ecologically effective solution from the model had an annual atrazine

  19. Spatio-angular Minimum-variance Tomographic Controller for Multi-Object Adaptive Optics systems

    CERN Document Server

    Correia, Carlos M; Veran, Jean-Pierre; Andersen, David; Lardiere, Olivier; Bradley, Colin

    2015-01-01

    Multi-object astronomical adaptive-optics (MOAO) is now a mature wide-field observation mode to enlarge the adaptive-optics-corrected field in a few specific locations over tens of arc-minutes. The work-scope provided by open-loop tomography and pupil conjugation is amenable to a spatio-angular Linear-Quadratic Gaussian (SA-LQG) formulation aiming to provide enhanced correction across the field with improved performance over static reconstruction methods and less stringent computational complexity scaling laws. Starting from our previous work [1], we use stochastic time-progression models coupled to approximate sparse measurement operators to outline a suitable SA-LQG formulation capable of delivering near optimal correction. Under the spatio-angular framework the wave-fronts are never explicitly estimated in the volume,providing considerable computational savings on 10m-class telescopes and beyond. We find that for Raven, a 10m-class MOAO system with two science channels, the SA-LQG improves the limiting mag...

  20. Multi-objective LQR with optimum weight selection to design FOPID controllers for delayed fractional order processes.

    Science.gov (United States)

    Das, Saptarshi; Pan, Indranil; Das, Shantanu

    2015-09-01

    An optimal trade-off design for fractional order (FO)-PID controller is proposed with a Linear Quadratic Regulator (LQR) based technique using two conflicting time domain objectives. A class of delayed FO systems with single non-integer order element, exhibiting both sluggish and oscillatory open loop responses, have been controlled here. The FO time delay processes are handled within a multi-objective optimization (MOO) formalism of LQR based FOPID design. A comparison is made between two contemporary approaches of stabilizing time-delay systems withinLQR. The MOO control design methodology yields the Pareto optimal trade-off solutions between the tracking performance and total variation (TV) of the control signal. Tuning rules are formed for the optimal LQR-FOPID controller parameters, using median of the non-dominated Pareto solutions to handle delayed FO processes.

  1. The real-time control system for the CANARY multi-object adaptive optics on-sky demonstrator

    Science.gov (United States)

    Dipper, N. A.; Basden, A.; Looker, N. E.; Gendron, E.; Geng, D.; Gratadour, D.; Hubert, Z.; Vidal, F.; Myers, R. M.; Rousset, G.; Sevin, A.; Younger, E. J.

    2010-07-01

    CANARY is a Multi-Object Adaptive Optics (MOAO) system designed to demonstrate the AO aspects of proposed EELT instruments such as the multi-object spectrograph EAGLE. The first phase of Canary will be executed on the 4.2m William Herschel Telescope in 2010. We describe here the AO Real-time Control System (RTCS) for Canary. This is based on a distributed architecture of components interconnected by a fast serial fabric (sFPDP). The hardware used is a hybrid of FPGA and CPU technology. The middleware used for system data telemetry and control is based on CORBA and the publish/subscribe pattern. The system is designed to be easily modified and extended for the later, higher order, phases of CANARY. In order to provide the increase in computational power required in higher order systems, the current CPU technology can be readily replaced by acceleration hardware based on FPGA or GPU technologies. The Canary RTCS thus provides a test-bed for these new technologies that will be required for E-ELT instruments. These design concepts can be developed to provide an RTCS for E-ELT instruments and are in line with those under consideration by ESO for the E-ELT AO systems to which instruments such as EAGLE will be required to interface.

  2. Design for aircraft engine multi-objective controllers with switching characteristics

    Directory of Open Access Journals (Sweden)

    Liu Xiaofeng

    2014-10-01

    Full Text Available The aircraft engine multi-loop control system is described and the switching control theory is introduced to solve the regulating and protecting control problems in this paper. The aircraft engine multi-loop control system is firstly described and the control problems are formulated. Secondly, the theory of the smooth switching control is devoted and a new extended scheme for the smooth switching of a switched control system is introduced. Then, for the key technologies of aero-engines switching control, a design algorithm is presented which can determine which candidate controller should be put in feedback with the plant to achieve a desired performance and the procedure to design the aircraft engine multi-loop control system is detailed. The switching performance objectives and the switching scheme are given and a family of PID controllers and compensators is designed. The simulation shows that using the switching control design method can not only improve the dynamic performance of the aircraft engine control system and reduce the switching times, but also guarantee the stability in some peculiar occasions.

  3. Design for aircraft engine multi-objective controllers with switching characteristics

    Institute of Scientific and Technical Information of China (English)

    Liu Xiaofeng; Shi Jing; Qi Yiwen; Yuan Ye

    2014-01-01

    The aircraft engine multi-loop control system is described and the switching control theory is introduced to solve the regulating and protecting control problems in this paper. The aircraft engine multi-loop control system is firstly described and the control problems are formu-lated. Secondly, the theory of the smooth switching control is devoted and a new extended scheme for the smooth switching of a switched control system is introduced. Then, for the key technologies of aero-engines switching control, a design algorithm is presented which can determine which candidate controller should be put in feedback with the plant to achieve a desired performance and the procedure to design the aircraft engine multi-loop control system is detailed. The switching performance objectives and the switching scheme are given and a family of PID controllers and compensators is designed. The simulation shows that using the switching control design method can not only improve the dynamic performance of the aircraft engine control system and reduce the switching times, but also guarantee the stability in some peculiar occasions.

  4. Determination of an optimal control strategy for drug administration in tumor treatment using multi-objective optimization differential evolution.

    Science.gov (United States)

    Lobato, Fran Sérgio; Machado, Vinicius Silvério; Steffen, Valder

    2016-07-01

    The mathematical modeling of physical and biologic systems represents an interesting alternative to study the behavior of these phenomena. In this context, the development of mathematical models to simulate the dynamic behavior of tumors is configured as an important theme in the current days. Among the advantages resulting from using these models is their application to optimization and inverse problem approaches. Traditionally, the formulated Optimal Control Problem (OCP) has the objective of minimizing the size of tumor cells by the end of the treatment. In this case an important aspect is not considered, namely, the optimal concentrations of drugs may affect the patients' health significantly. In this sense, the present work has the objective of obtaining an optimal protocol for drug administration to patients with cancer, through the minimization of both the cancerous cells concentration and the prescribed drug concentration. The resolution of this multi-objective problem is obtained through the Multi-objective Optimization Differential Evolution (MODE) algorithm. The Pareto's Curve obtained supplies a set of optimal protocols from which an optimal strategy for drug administration can be chosen, according to a given criterion.

  5. Multi-objective optimization of the control strategy of electric vehicle electro-hydraulic composite braking system with genetic algorithm

    Directory of Open Access Journals (Sweden)

    Zhang Fengjiao

    2015-03-01

    Full Text Available Optimization of the control strategy plays an important role in improving the performance of electric vehicles. In order to improve the braking stability and recover the braking energy, a multi-objective genetic algorithm is applied to optimize the key parameters in the control strategy of electric vehicle electro-hydraulic composite braking system. Various limitations are considered in the optimization process, and the optimization results are verified by a software simulation platform of electric vehicle regenerative braking system in typical brake conditions. The results show that optimization objectives achieved a good astringency, and the optimized control strategy can increase the brake energy recovery effectively under the condition of ensuring the braking stability.

  6. Framework of Multi-objective Wind Farm Controller Applicable to Real Wind Farms

    DEFF Research Database (Denmark)

    Kazda, Jonas; Gögmen, Tuhfe; Giebel, Gregor;

    2016-01-01

    Optimal wind farm control can mitigate adverse wake effects that can potentially cause up to 40% power loss and 80% increased fatigue loads in wind farms. The aim of this work is to outline a methodological framework of an optimal wind farm controller, which provides improved solutions to critical......-objective optimal wind farm controller is outlined with the following key characteristics. Available control objectives are (i) to maximize the total wind farm power output or (ii) to follow a specified power reference for the wind farm’s total power output while reducing the fatigue loads of the wind turbines...... areas of optimal wind farm control research. The basis of this framework is a review of optimal wind farm control methodologies, which is presented first. It is observed that there is, at present, mainly a need for more advanced wind farm operation models. Thereafter the framework of a multi...

  7. Multi-Objective Control of Balancing Systems for Li-Ion Battery Packs

    DEFF Research Database (Denmark)

    Barreras, Jorge Varela; Pinto, Claudio; de Castro, Ricardo;

    2014-01-01

    While a great number of battery balancing circuit topologies have been proposed, the unique control objective typically pursued is equalization of single cell charge. However, a balancing circuit could offer potentially more control features, especially with topologies able to provide bidirection...

  8. Metabolic flux ratio analysis and multi-objective optimization revealed a globally conserved and coordinated metabolic response of E. coli to paraquat-induced oxidative stress.

    Science.gov (United States)

    Shen, Tie; Rui, Bin; Zhou, Hong; Zhang, Ximing; Yi, Yin; Wen, Han; Zheng, Haoran; Wu, Jihui; Shi, Yunyu

    2013-01-27

    The ability of a microorganism to adapt to changes in the environment, such as in nutrient or oxygen availability, is essential for its competitive fitness and survival. The cellular objective and the strategy of the metabolic response to an extreme environment are therefore of tremendous interest and, thus, have been increasingly explored. However, the cellular objective of the complex regulatory structure of the metabolic changes has not yet been fully elucidated and more details regarding the quantitative behaviour of the metabolic flux redistribution are required to understand the systems-wide biological significance of this response. In this study, the intracellular metabolic flux ratios involved in the central carbon metabolism were determined by fractional (13)C-labeling and metabolic flux ratio analysis (MetaFoR) of the wild-type E. coli strain JM101 at an oxidative environment in a chemostat. We observed a significant increase in the flux through phosphoenolpyruvate carboxykinase (PEPCK), phosphoenolpyruvate carboxylase (PEPC), malic enzyme (MEZ) and serine hydroxymethyltransferase (SHMT). We applied an ε-constraint based multi-objective optimization to investigate the trade-off relationships between the biomass yield and the generation of reductive power using the in silico iJR904 genome-scale model of E. coli K-12. The theoretical metabolic redistribution supports that the trans-hydrogenase pathway should not play a direct role in the defence mounted by E. coli against oxidative stress. The agreement between the measured ratio and the theoretical redistribution established the significance of NADPH synthesis as the goal of the metabolic reprogramming that occurs in response to oxidative stress. Our work presents a framework that combines metabolic flux ratio analysis and multi-objective optimization to investigate the metabolic trade-offs that occur under varied environmental conditions. Our results led to the proposal that the metabolic response of E

  9. Multi-Objective PID-Controller Tuning for a Magnetic Levitation System using NSGA-II

    DEFF Research Database (Denmark)

    Pedersen, Gerulf K. M.; Yang, Zhenyu

    2006-01-01

    This paper investigates the issue of PID-controller parameter tuning for a magnetic levitation system using the non-dominated sorting genetic algorithm (NSGA-II). The magnetic levitation system is inherently unstable and the PID-controller parameters are hard to find using conventional methods....... Based on four different performance measures, derived from the step response of the levitation system, the algorithm is used to find a set of non-dominated parameters for a PID-controller that can stabilize the system and minimize the performance measures....

  10. A Building Model Framework for a Genetic Algorithm Multi-objective Model Predictive Control

    DEFF Research Database (Denmark)

    Arendt, Krzysztof; Ionesi, Ana; Jradi, Muhyiddine;

    Model Predictive Control (MPC) of building systems is a promising approach to optimize building energy performance. In contrast to traditional control strategies which are reactive in nature, MPC optimizes the utilization of resources based on the predicted effects. It has been shown that energy...... savings potential of this technique can reach up to 40% compared to conventional control strategies depending on the particular building type. However, the effort needed to implement MPC in buildings is significant and often considered prohibitive. That is why until now fully-functional MPC has been...

  11. Optimization of PID Parameter In Control System Tuning With Multi-Objective Genetic Algorithm.

    Directory of Open Access Journals (Sweden)

    Md Amanullah

    2014-05-01

    Full Text Available Way of playing advancement is the out-standing design of the study of PID control and frequently research work has been guided for this aspiration. The Proportional plus Integral plus Derivative (PID, controllers are most sweepingly used in control theory as well as industrial plants owing to their ease of execution and sturdiness way of playing. The aspiration of this deed representation capable and apace tuning approach using Genetic Algorithm (GA to obtain the optimized criterion of the PID controller so as to acquire the essential appearance designation of the technique below meditation. The make perfect achievement about multiple plants have in relation to the established tuning approach, to consider the ability of intended approach. Mostly, the whole system’s performance powerfully depends on the controller’s proficiency and thus the tuning technique plays a key part in the system’s behavior.

  12. Multi Objective Robust Active Vibration Control for Flexure Jointed Struts of Stewart Platforms via H∞ and μ Synthesis

    Institute of Scientific and Technical Information of China (English)

    Liu Lei; Wang Benli

    2008-01-01

    Active vibration control is needed for future space telescopes, space laser communication and other precision sensitive payloads which require ultra-quiet environments. A Stewart platform based hybrid isolator with 6 hybrid struts is the effective system for ac- tive/passive vibration isolation over 5-250 Hz band. Using an identification transfer matrix of the Stewart platform, the coupling analysis of six channels is provided. A dynamics model is derived, and the rigid mode is removed to keep the signal of pointing control. Multi objective robust H∞ and μ synthesis strategies, based on singular values and structured singular values respectively, are presented, which simultaneously satisfy the low frequency pointing and high frequency disturbance rejection requirements and take account of the model uncertainty, parametric uncertainty and sensor noise. Then, by performing robust stability test, it is shown that the two controllers are robust to the uncertainties, the robust stability margin of H∞ controller is less than that of μ controller, but the order of μ controller is higher than that of H∞ controller, so the balanced controller reduction is provided. Additionally, the μ controller is compared with a PI controller. The time domain simulation of the μ controller indicates that the two robust control strategies are effective for keeping the pointing command and isolating the harmonic and stochastic disturbances.

  13. Multi-objective design of fuzzy logic controller in supply chain

    Science.gov (United States)

    Ghane, Mahdi; Tarokh, Mohammad Jafar

    2012-08-01

    Unlike commonly used methods, in this paper, we have introduced a new approach for designing fuzzy controllers. In this approach, we have simultaneously optimized both objective functions of a supply chain over a two-dimensional space. Then, we have obtained a spectrum of optimized points, each of which represents a set of optimal parameters which can be chosen by the manager according to the importance of objective functions. Our used supply chain model is a member of inventory and order-based production control system family, a generalization of the periodic review which is termed `Order-Up-To policy.' An auto rule maker, based on non-dominated sorting genetic algorithm-II, has been applied to the experimental initial fuzzy rules. According to performance measurement, our results indicate the efficiency of the proposed approach.

  14. Design of Autonomous Navigation Controllers for Unmanned Aerial Vehicles Using Multi-Objective Genetic Programming

    Science.gov (United States)

    2004-03-01

    In Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 398–406, 1997. [23] Emilio Frazzoli. Maneuver-based motion planning...Evolutionary approaches to neural control of rolling, walking, swimming and flying animats or robots. In Richard J. Duro, Jose Santos, and Manuel Grana...objective genetic programming. In Proceedings of the Congress on Evolutionary Computation, Portland, OR, June 2004. [66] Peter Pacheco . Parallel

  15. Multi-objective optimization for combined quality-quantity urban runoff control

    Science.gov (United States)

    Oraei Zare, S.; Saghafian, B.; Shamsai, A.

    2012-12-01

    Urban development affects the quantity and quality of urban surface runoff. In recent years, the best management practices (BMPs) concept has been widely promoted for control of both quality and quantity of urban floods. However, means to optimize the BMPs in a conjunctive quantity/quality framework are still under research. In this paper, three objective functions were considered: (1) minimization of the total flood damages, cost of BMP implementation and cost of land-use development; (2) reducing the amount of TSS (total suspended solid) and BOD5 (biological oxygen demand), representing the pollution characteristics, to below the threshold level; and (3) minimizing the total runoff volume. The biological oxygen demand and total suspended solid values were employed as two measures of urban runoff quality. The total surface runoff volume produced by sub-basins was representative of the runoff quantity. The construction and maintenance costs of the BMPs were also estimated based on the local price standards. Urban runoff quantity and quality in the case study watershed were simulated with the Storm Water Management Model (SWMM). The NSGA-II (Non-dominated Sorting Genetic Algorithm II) optimization technique was applied to derive the optimal trade off curve between various objectives. In the proposed structure for the NSGA-II algorithm, a continuous structure and intermediate crossover were used because they perform better as far as the optimization efficiency is concerned. Finally, urban runoff management scenarios were presented based on the optimal trade-off curve using the k-means method. Subsequently, a specific runoff control scenario was proposed to the urban managers.

  16. Multi-objective optimization for combined quality–quantity urban runoff control

    Directory of Open Access Journals (Sweden)

    S. Oraei Zare

    2012-12-01

    Full Text Available Urban development affects the quantity and quality of urban surface runoff. In recent years, the best management practices (BMPs concept has been widely promoted for control of both quality and quantity of urban floods. However, means to optimize the BMPs in a conjunctive quantity/quality framework are still under research. In this paper, three objective functions were considered: (1 minimization of the total flood damages, cost of BMP implementation and cost of land-use development; (2 reducing the amount of TSS (total suspended solid and BOD5 (biological oxygen demand, representing the pollution characteristics, to below the threshold level; and (3 minimizing the total runoff volume. The biological oxygen demand and total suspended solid values were employed as two measures of urban runoff quality. The total surface runoff volume produced by sub-basins was representative of the runoff quantity. The construction and maintenance costs of the BMPs were also estimated based on the local price standards. Urban runoff quantity and quality in the case study watershed were simulated with the Storm Water Management Model (SWMM. The NSGA-II (Non-dominated Sorting Genetic Algorithm II optimization technique was applied to derive the optimal trade off curve between various objectives. In the proposed structure for the NSGA-II algorithm, a continuous structure and intermediate crossover were used because they perform better as far as the optimization efficiency is concerned. Finally, urban runoff management scenarios were presented based on the optimal trade-off curve using the k-means method. Subsequently, a specific runoff control scenario was proposed to the urban managers.

  17. Pareto-optimal multi-objective design of airplane control systems

    Science.gov (United States)

    Schy, A. A.; Johnson, K. G.; Giesy, D. P.

    1980-01-01

    A constrained minimization algorithm for the computer aided design of airplane control systems to meet many requirements over a set of flight conditions is generalized using the concept of Pareto-optimization. The new algorithm yields solutions on the boundary of the achievable domain in objective space in a single run, whereas the older method required a sequence of runs to approximate such a limiting solution. However, Pareto-optimality does not guarantee a satisfactory design, since such solutions may emphasize some objectives at the expense of others. The designer must still interact with the program to obtain a well-balanced set of objectives. Using the example of a fighter lateral stability augmentation system (SAS) design over five flight conditions, several effective techniques are developed for obtaining well-balanced Pareto-optimal solutions. For comparison, one of these techniques is also used in a recently developed algorithm of Kreisselmeier and Steinhauser, which replaces the hard constraints with soft constraints, using a special penalty function. It is shown that comparable results can be obtained.

  18. Elevator Group Control System Based on Multi-Objective Planning Algorithm%基于多目标规划算法的电梯群控系统

    Institute of Scientific and Technical Information of China (English)

    俞雯

    2011-01-01

    以提高电梯群的运行效率和服务质量为出发点,提出一种基于多目标规划调度算法的电梯群控系统.主要研究内容包括电梯群控系统的特点及要求、电梯群控系统的多目标规划算法建模过程以及电梯群控仿真系统的设计等几个方面.在电梯群控仿真系统当中,同时嵌入最小等待时间算法和多目标规划算法,进行2种算法的仿真比较,从仿真结果得出基于多目标规划调度算法的电梯群控系统具有一定的实际应用价值.图5参10%In order to improve the elevator's operating efficiency and service quality, this paper proposed an elevator group control system based on the multi-objective planning algorithm. The main research contents were listed as follows: the characters and requirements of elevator group control system, established an evaluate function for elevator group control system based on multi-objective planning algorithm, designed the simulating system of elevator group control system, and so on. The least waiting time and multi-objective planning algorithm were both embedded in the simulating system in order to make compare through simulation. The simulation results show that elevator group control system based on the multi-objective planning algorithm has a certain practical applied value.

  19. PRODUCT LIFECYCLE OPTIMISATION OF CAR CLIMATE CONTROLS USING ANALYTICAL HIERARCHICAL PROCESS (AHP ANALYSIS AND A MULTI-OBJECTIVE GROUPING GENETIC ALGORITHM (MOGGA

    Directory of Open Access Journals (Sweden)

    MICHAEL J. LEE

    2016-01-01

    Full Text Available A product’s lifecycle performance (e.g. assembly, outsourcing, maintenance and recycling can often be improved through modularity. However, modularisation under different and often conflicting lifecycle objectives is a complex problem that will ultimately require trade-offs. This paper presents a novel multi-objective modularity optimisation framework; the application of which is illustrated through the modularisation of a car climate control system. Central to the framework is a specially designed multi-objective grouping genetic algorithm (MOGGA that is able to generate a whole range of alternative product modularisations. Scenario analysis, using the principles of the analytical hierarchical process (AHP, is then carried out to explore the solution set and choose a suitable modular architecture that optimises the product lifecycle according to the company’s strategic vision.

  20. Fractional order PID control design for semi-active control of smart base-isolated structures: A multi-objective cuckoo search approach.

    Science.gov (United States)

    Zamani, Abbas-Ali; Tavakoli, Saeed; Etedali, Sadegh

    2017-03-01

    Fractional order PID (FOPID) controllers are introduced as a general form of classical PID controllers using fractional calculus. As this controller provides good disturbance rejection and is robust against plant uncertainties it is appropriate for the vibration mitigation in structures. In this paper, an FOPID controller is designed to adjust the contact force of piezoelectric friction dampers for semi-active control of base-isolated structures during far-field and near-field earthquake excitations. A multi-objective cuckoo search algorithm is employed to tune the controller parameters. Considering the resulting Pareto optimal front, the best input for the FOPID controller is selected. For seven pairs of earthquakes and nine performance indices, the performance of the proposed controller is compared with those provided by several well-known control techniques. According to the simulation results, the proposed controller performs better than other controllers in terms of simultaneous reduction of the maximum base displacement and story acceleration for various types of earthquakes. Also, it provides acceptable responses in terms of inter-story drifts, root mean square of base displacements and floor acceleration. In addition, the evaluation of robustness for a stiffness uncertainty of ±10% indicates that the proposed controller gives a robust performance against such modeling errors.

  1. Multi-objective optimal design of online PID controllers using model predictive control based on the group method of data handling-type neural networks

    Science.gov (United States)

    Majdabadi-Farahani, V.; Hanif, M.; Gholaminezhad, I.; Jamali, A.; Nariman-Zadeh, N.

    2014-10-01

    In this paper, model predictive control (MPC) is used for optimal selection of proportional-integral-derivative (PID) controller gains. In conventional tuning methods a history of response error of the system under control in the passed time is measured and used to adjust PID parameters in order to improve the performance of the system in proceeding time. But MPC obviates this characteristic of classic PID. In fact MPC tries to tune the controller by predicting the system's behaviour some time steps ahead. In this way, PID parameters are adjusted before any real error occurs in the system's response. For this purpose, polynomial meta-models based on the evolved group method of data handling neural networks are obtained to simply simulate the time response of the dynamic system. Moreover, a non-dominated sorting genetic algorithm has been used in a multi-objective Pareto optimisation to select the parameters of the MPC which are prediction horizon, control horizon and relation of weight of Δ u and error, to minimise simultaneously two objective functions that are control effort and integral time absolute error of the system response. The results mentioned at the end obviously declare that the proposed method surpasses conventional tuning methods for PID controllers, and Pareto optimal selection of predictive parameters also improves the performance of the introduced method.

  2. Application of a Multi-Objective Optimization Method to Provide Least Cost Alternatives for NPS Pollution Control

    Science.gov (United States)

    Maringanti, Chetan; Chaubey, Indrajeet; Arabi, Mazdak; Engel, Bernard

    2011-09-01

    Nonpoint source (NPS) pollutants such as phosphorus, nitrogen, sediment, and pesticides are the foremost sources of water contamination in many of the water bodies in the Midwestern agricultural watersheds. This problem is expected to increase in the future with the increasing demand to provide corn as grain or stover for biofuel production. Best management practices (BMPs) have been proven to effectively reduce the NPS pollutant loads from agricultural areas. However, in a watershed with multiple farms and multiple BMPs feasible for implementation, it becomes a daunting task to choose a right combination of BMPs that provide maximum pollution reduction for least implementation costs. Multi-objective algorithms capable of searching from a large number of solutions are required to meet the given watershed management objectives. Genetic algorithms have been the most popular optimization algorithms for the BMP selection and placement. However, previous BMP optimization models did not study pesticide which is very commonly used in corn areas. Also, with corn stover being projected as a viable alternative for biofuel production there might be unintended consequences of the reduced residue in the corn fields on water quality. Therefore, there is a need to study the impact of different levels of residue management in combination with other BMPs at a watershed scale. In this research the following BMPs were selected for placement in the watershed: (a) residue management, (b) filter strips, (c) parallel terraces, (d) contour farming, and (e) tillage. We present a novel method of combing different NPS pollutants into a single objective function, which, along with the net costs, were used as the two objective functions during optimization. In this study we used BMP tool, a database that contains the pollution reduction and cost information of different BMPs under consideration which provides pollutant loads during optimization. The BMP optimization was performed using a NSGA

  3. Multi-Objective Advanced Inverter Controls to Dispatch the Real and Reactive Power of Many Distributed PV Systems.

    Energy Technology Data Exchange (ETDEWEB)

    Reno, Matthew J. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Lave, Matthew Samuel [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Broderick, Robert Joseph [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Seuss, John [Georgia Inst. of Technology, Atlanta, GA (United States); Grijalva, Santiago [Georgia Inst. of Technology, Atlanta, GA (United States)

    2016-01-01

    The research presented in this report compares several real - time control strategies for the power output of a large number of PV distributed throughout a large distribution feeder circuit. Both real and reactive power controls are considered with the goal of minimizing network over - voltage violations caused by large amounts of PV generation. Several control strategies are considered under various assumptions regarding the existence and latency of a communication network. The control parameters are adjusted to maximize the effectiveness of each control. The controls are then compared based on their ability to achieve multiple objectiv es. These objectives include minimizing the total number of voltage violations , minimizing the total amount of PV energy curtailed or reactive power generated, and maximizing the fairness of any control action among all PV systems . The controls are simulat ed on the OpenDSS platform using time series load and spatially - distributed irradiance data.

  4. Non-fragile multi-objective static output feedback control of vehicle active suspension with time-delay

    Science.gov (United States)

    Kong, Yongsu; Zhao, Dingxuan; Yang, Bin; Han, Chenghao; Han, Kyongwon

    2014-07-01

    This paper presents an approach to design a delay-dependent non-fragile H∞/L2-L∞ static output feedback (SOF) controller for active suspension with input time-delay. The control problem of quarter-car active suspension with actuator time-delay is formulated to a H∞/L2-L∞ control problem. By employing a delay-dependent Lyapunov function, new existence conditions of delay-dependent non-fragile SOF H∞ controller and L2-L∞ controller are derived, respectively, in terms of the feasibility of bilinear matrix inequalities (BMIs). Then, a procedure based on linear matrix inequality optimisation and a hybrid algorithm of the particle swarm optimisation and differential evolution is used to solve an optimisation problem with BMI constraints. Design and simulation results of non-fragile H∞/L2-L∞ controller for active suspension show that the designed controller not only can achieve the optimal performance and stability of the closed-loop system in spite of the existence of the actuator time-delay, but also has significantly improved the non-fragility characteristics over controller perturbations.

  5. Application of multi-objective controller to optimal tuning of PID gains for a hydraulic turbine regulating system using adaptive grid particle swam optimization.

    Science.gov (United States)

    Chen, Zhihuan; Yuan, Yanbin; Yuan, Xiaohui; Huang, Yuehua; Li, Xianshan; Li, Wenwu

    2015-05-01

    A hydraulic turbine regulating system (HTRS) is one of the most important components of hydropower plant, which plays a key role in maintaining safety, stability and economical operation of hydro-electrical installations. At present, the conventional PID controller is widely applied in the HTRS system for its practicability and robustness, and the primary problem with respect to this control law is how to optimally tune the parameters, i.e. the determination of PID controller gains for satisfactory performance. In this paper, a kind of multi-objective evolutionary algorithms, named adaptive grid particle swarm optimization (AGPSO) is applied to solve the PID gains tuning problem of the HTRS system. This newly AGPSO optimized method, which differs from a traditional one-single objective optimization method, is designed to take care of settling time and overshoot level simultaneously, in which a set of non-inferior alternatives solutions (i.e. Pareto solution) is generated. Furthermore, a fuzzy-based membership value assignment method is employed to choose the best compromise solution from the obtained Pareto set. An illustrative example associated with the best compromise solution for parameter tuning of the nonlinear HTRS system is introduced to verify the feasibility and the effectiveness of the proposed AGPSO-based optimization approach, as compared with two another prominent multi-objective algorithms, i.e. Non-dominated Sorting Genetic Algorithm II (NSGAII) and Strength Pareto Evolutionary Algorithm II (SPEAII), for the quality and diversity of obtained Pareto solutions set. Consequently, simulation results show that this AGPSO optimized approach outperforms than compared methods with higher efficiency and better quality no matter whether the HTRS system works under unload or load conditions.

  6. Multi-objective optimal reactive power dispatch to maximize power system social welfare in the presence of generalized unified power flow controller

    Directory of Open Access Journals (Sweden)

    Suresh Chintalapudi Venkata

    2015-09-01

    Full Text Available In this paper a novel non-linear optimization problem is formulated to maximize the social welfare in restructured environment with generalized unified power flow controller (GUPFC. This paper presents a methodology to optimally allocate the reactive power by minimizing voltage deviation at load buses and total transmission power losses so as to maximize the social welfare. The conventional active power generation cost function is modified by combining costs of reactive power generated by the generators, shunt capacitors and total power losses to it. The formulated objectives are optimized individually and simultaneously as multi-objective optimization problem, while satisfying equality, in-equality, practical and device operational constraints. A new optimization method, based on two stage initialization and random distribution processes is proposed to test the effectiveness of the proposed approach on IEEE-30 bus system, and the detailed analysis is carried out.

  7. IOT Overview: Optical Multi-Object Spectrographs

    Science.gov (United States)

    Schmidtobreick, L.; Bagnulo, S.; Jehin, E.; Marconi, G.; O'Brien, K.; Pompei, E.; Saviane, I.

    We give an introduction to the several instruments that ESO operates and which are able to perform optical multi-object spectroscopy. We point out the standard ways of reducing these spectra, the problems that occur, and the way we deal with them. A short introduction is given on how the quality control is performed.

  8. Multi-objective energy analysis

    Energy Technology Data Exchange (ETDEWEB)

    Cherniavsky, E.A.

    1979-11-01

    Analytic models have been applied to energy-planning problems in an effort to assess the probable impacts of alternative courses of action on vital social concerns such as the quality of the environment, the state of the economy, or extent of dependence on insecure foreign energy sources. A proposed program may have a variety of effects on social objectives; beneficial results in one area may be purchased at the cost of undesirable consequences in another. A policy must be judged by its impacts on a number of social concerns. The purpose of multi-objective analysis is to identify and quantify the tradeoffs between different social objectives, and to aid policymakers in formulating decisions that achieve the best possible compromise between conflicting goals. This paper reviews approaches and techniques currently employed in multi-objective analysis. Associated problems are explored and discussed in the light of experience with applications to energy-planning models. Conclusions are drawn concerning the most-fruitful directions for future research in this area. 40 references.

  9. 多目标控制电梯群控调度算法的优化%Optimization of elevator group scheduling algorithm of multi-objective control

    Institute of Scientific and Technical Information of China (English)

    李强; 郑永康; 王瀚韬

    2013-01-01

    针对模糊推理获取电梯群控指标可信度时缺乏学习性的缺点,引入了人工鱼群算法用于决策函数的多元线性回归曲线的优化.呼梯信号和电梯运行信息经采集与计算,得到了厅层召唤等待时间(HCWT)、厅层召唤最大等待时间(maxHCWT)、剩余响应能力(CV)和召唤集中程度(GD)这4个输入变量,经模糊推理获取了平均候梯时间(AWT)、乘客长候梯率(LWP)和能量消耗(RNC)3个群控指标的可信度值,并以此为样本训练人工鱼群,获得了决策函数.经决策函数计算得到的群控指标可信度值,再与由客流交通模式决定的指标加权系数进行线性平均,以此作为目标评价函数进行电梯群控调度,实现了对电梯运行效率和节能的多目标控制.经电梯群控仿真平台评测,实验结果表明优化后的群控调度算法能明显改善群控指标.%Aiming at the shortcoming of lack of learning ability for fuzzy inference to obtain the index reliabilities of elevator group control,the artificial fish-swarm algorithm was led into optimize the multiple linear regression curve of the decision function.Four input variables:hall call wait time(HCWT),maximum of HCWT (maxHCWT),capacity of validation (CV)and group density (GD)could be gotten through the collection and calculation of call signals and running signals of elevators,and the three index reliabilities of elevator group control:average wait time (AWT),long-time wait percentage (LWP)and energy consumption (ENC)could be gained by fuzzy inference with these input variables.The artificial fish-swarm was trained with these three index reliabilities as samples to gain the decision function,which could be used to calculate the index reliabilities,and then be taken to linear weighted mean with the weighting coefficients which was determined by passenger traffic mode.This formula was taken as the objective evaluation function of elevator group control to reach the multi-objective

  10. Coordination Control Of Complex Machines

    NARCIS (Netherlands)

    J.C.M. Baeten; B. van Beek; J. Markovski; L.J.A.M. Somers

    2015-01-01

    Control and coordination are important aspects of the development of complex machines due to an ever-increasing demand for better functionality, quality, and performance. In WP6 of the C4C project, we developed a synthesis-centric systems engineering framework suitable for supervisory coordination o

  11. 飞机环控/发动机系统多目标决策研究%Study on Multi-objective Decision of Aircraft Environmental Control System and Engine

    Institute of Scientific and Technical Information of China (English)

    李洪波; 董新民; 李婷婷; 郭军

    2011-01-01

    A two-phase multi-objective decision method is proposed to the multi-objective decision of aircraft environmental control system and engine. The total entropy generation minimum at flight phases of take-off, climb and supersonic penetration are regarded as different objective, the multi-objective optimization model is established. The Pareto optimal set is obtained by multi-objective optimization algorithm. Based on the Pareto optimal set the scheme primary selection is done, and it evaluates the selected scheme through Vague set decision-making method with improved integrated weight, the final optimal scheme is obtained. Results validates the rationality of the method.%提出一种两阶段多目标决策方法,应用于飞机环控/发动机系统的多目标决策分析.将起飞、加速爬升和高空超音速巡航阶段的总熵产最小视为不同目标函数以建立多目标优化模型,采用多目标优化算法得到非劣最优解集.在此基础上进行方案初选,利用改进综合权重的Vague集决策方法对备选方案进行模糊评价和优选,并找到最终解.计算结果验证了该方法的合理性.

  12. Coordination control of distributed systems

    CERN Document Server

    Villa, Tiziano

    2015-01-01

    This book describes how control of distributed systems can be advanced by an integration of control, communication, and computation. The global control objectives are met by judicious combinations of local and nonlocal observations taking advantage of various forms of communication exchanges between distributed controllers. Control architectures are considered according to  increasing degrees of cooperation of local controllers:  fully distributed or decentralized controlcontrol with communication between controllerscoordination control, and multilevel control.  The book covers also topics bridging computer science, communication, and control, like communication for control of networks, average consensus for distributed systems, and modeling and verification of discrete and of hybrid systems. Examples and case studies are introduced in the first part of the text and developed throughout the book. They include: control of underwater vehicles, automated-guided vehicles on a container terminal, contro...

  13. Non-convex multi-objective optimization

    CERN Document Server

    Pardalos, Panos M; Žilinskas, Julius

    2017-01-01

    Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in...

  14. 光伏系统多目标粒子群优化模糊MPPT控制%Fuzzy Logic Controller for MPPT of the Photovoltaic Generation System Using Multi-objective Particle Swarm Optimization

    Institute of Scientific and Technical Information of China (English)

    游国栋; 李继生; 侯勇; 赵春东; 王磊

    2016-01-01

    针对光伏发电系统遭受部分阴影时呈现多峰值、非线性和时变不确定等特性,提出了基于多目标粒子群优化(multi-objective particle swarm optimization,MO-PSO)模糊算法,对最大功率点(maximum power point,MPP)进行追踪控制。该算法对模糊控制的模糊集、模糊规则分别进行多目标粒子群算法优化,同时最小化两个目标函数,以提高光照强度变化时系统对最大功率点跟踪(maximum powerpoint tracking,MPPT)的暂态响应速度和稳态精度。通过对干扰观察法、常规模糊控制方法和多目标粒子群优化模糊控制的仿真波形比较,验证了所提控制策略的有效性。%Aimed at the problems of multi-peak,nonlinearity and uncertainty of thephotovoltaic generation system shaded at the time in actual operation,a fuzzy controller using multi-objective particle swarm optimization was proposed,with which the actual maximum power point of the photovoltaic generation system can be tracked.Multi-objective particle swarm optimization algorithm was applied to optimize the fuzzy sets and the fuzzy rules of the fuzzy controller and the two objec-tive functions were minimizedin order to ensure that the control system has a faster dynamic response speed and higher steady-state accuracy in case the light intensity varies.In this research,simulations were performed and studied with the perturbation and observation method,the fuzzy control method and the fuzzy controller with multi-objective particle swarm optimization algorithm under the same condition,and the results demonstrated the effectiveness of the proposed method.

  15. 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...

  16. Study of Multi-objective Fuzzy Optimization for Path Planning

    Institute of Scientific and Technical Information of China (English)

    WANG Yanyang; WEI Tietao; QU Xiangju

    2012-01-01

    During path planning,it is necessary to satisfy the requirements of multiple objectives.Multi-objective synthesis is based on the need of flight mission and subjectivity inclination of decision-maker.The decision-maker,however,has illegibility for understanding the requirements of multiple objectives and the subjectivity inclination.It is important to develop a reasonable cost performance index for describing the illegibility of the decision-maker in multi-objective path planning.Based on Voronoi diagram method for the path planning,this paper studies the synthesis method of the multi-objective cost performance index.According to the application of the cost performance index to the path planning based on Voronoi diagram method,this paper analyzes the cost performance index which has been referred to at present.The analysis shows the insufficiency of the cost performance index at present,i.e.,it is difficult to synthesize sub-objective functions because of the great disparity of the sub-objective functions.Thus,a new approach is developed to optimize the cost performance index with the multi-objective fuzzy optimization strategy,and an improved performance index is established,which could coordinate the weight conflict of the sub-objective functions.Finally,the experimental result shows the effectiveness of the proposed approach.

  17. Tuning of a Proportional-Integral-Derivative Controller using Multi-Objective Non Dominated Sorting Particle Swarm Optimization Applied to pH Control in Continuous Stirred Tank Reactor

    Directory of Open Access Journals (Sweden)

    C. A. Kumar

    2011-01-01

    Full Text Available Problem statement: Most of the control engineering problems are characterized by several, contradicting, conflicting objectives, which have to be satisfied simultaneously. Two widely used methods for finding the optimal solution to such problems are aggregating to a single criterion and using Pareto-optimal solutions. Approach: Non-Dominated Sorting Particle Swarm Optimization algorithm (NSPSO based approach is used in the design of multiobjective PID controller to find the constant proportional-integral-derivative gains for a chemical neutralization plant. The plant considered in this study is highly non-linear and with varying time delay, provides a challenging test bed for nonlinear control problems. Results: Experimental results confirm that a multi-objective, Paretobased GA search gives a better performance than a single objective GA. Conclusion: Finally, the results for single objective and multiobjective optimization using NSPSO for the neutralization plant are compared. Gain scheduled PID controllers are designed from Pareto front obtained with NSPSO which exhibit good disturbance rejection capability.

  18. Multi-Object Tracking Scheme with Pyroelectric Infrared Sensor and Video Camera Coordination%融合热释电红外传感器与视频监控器的多目标跟踪算法

    Institute of Scientific and Technical Information of China (English)

    李方敏; 姜娜; 熊迹; 张景源

    2014-01-01

    现有基于热释电红外传感器的多目标跟踪系统在目标之间距离较近或者轨迹相交的情况下存在着误差较大的缺点。针对此缺点,提出了一种新型的基于热释电红外传感器与视频监测器协同工作的多目标跟踪方案。该方案可以充分利用两种传感器的优势,弥补在目标跟踪中的不足。算法采用最小二乘法利用热释电信息进行定位,并通过从图像或热释电传感器信号的幅频特性中提取特征信息来校正联合概率数据关联算法的关联矩阵,有效避免了错误关联。实验表明,该方案在多目标交叉情况下跟踪误差仅为其它算法的八分之一到四分之一。%The error tends to be significant in many existing pyroelectric infrared sensor based multi-object tracking systems when the measured objects get close to each other or their trajectories have intersections .To solve this problem ,we proposed a mul-ti-object tracking scheme by having pyroelectric infrared sensors and video cameras work cooperatively .This scheme takes the ad-vantages of both kinds of sensors ,which help to improve the performance compared to those using any kind of such sensors .In the proposed scheme ,we first achieve coarse positioning using least square method with data collected by pyroelectric infrared sensors , and then we correct the incidence matrix in joint probabilistic data association with features extracted from the images or the fre -quency responses of pyroelectric sensors .The coarse positioning is further filtered by joint probabilistic data association algorithm to obtain the final fine result .Such a method prevents false association effectively .Experimental results show that the tracking error of the proposed scheme in multi-object crossover scenario reduces to a quarter ,even to one eighth of the errors that exist in the com-pared schemes .

  19. Adaptive surrogate model based multi-objective transfer trajectory optimization between different libration points

    Science.gov (United States)

    Peng, Haijun; Wang, Wei

    2016-10-01

    An adaptive surrogate model-based multi-objective optimization strategy that combines the benefits of invariant manifolds and low-thrust control toward developing a low-computational-cost transfer trajectory between libration orbits around the L1 and L2 libration points in the Sun-Earth system has been proposed in this paper. A new structure for a multi-objective transfer trajectory optimization model that divides the transfer trajectory into several segments and gives the dominations for invariant manifolds and low-thrust control in different segments has been established. To reduce the computational cost of multi-objective transfer trajectory optimization, a mixed sampling strategy-based adaptive surrogate model has been proposed. Numerical simulations show that the results obtained from the adaptive surrogate-based multi-objective optimization are in agreement with the results obtained using direct multi-objective optimization methods, and the computational workload of the adaptive surrogate-based multi-objective optimization is only approximately 10% of that of direct multi-objective optimization. Furthermore, the generating efficiency of the Pareto points of the adaptive surrogate-based multi-objective optimization is approximately 8 times that of the direct multi-objective optimization. Therefore, the proposed adaptive surrogate-based multi-objective optimization provides obvious advantages over direct multi-objective optimization methods.

  20. Multi-objective optimization of steel nitriding

    Directory of Open Access Journals (Sweden)

    P. Cavaliere

    2016-03-01

    Full Text Available Steel nitriding is a thermo-chemical process largely employed in the machine components production to solve mainly wear and fatigue damage in materials. The process is strongly influenced by many different variables such as steel composition, nitrogen potential (range 0.8–35, temperature (range 350–1200 °C, time (range 2–180 hours. In the present study, the influence of such parameters affecting the nitriding layers' thickness, hardness, composition and residual stress was evaluated. The aim was to streamline the process by numerical–experimental analysis allowing to define the optimal conditions for the success of the process. The optimization software that was used is modeFRONTIER (Esteco, through which was defined a set of input parameters (steel composition, nitrogen potential, nitriding time, etc. evaluated on the basis of an optimization algorithm carefully chosen for the multi-objective analysis. The mechanical and microstructural results belonging to the nitriding process, performed with different processing conditions for various steels, are presented. The data were employed to obtain the analytical equations describing nitriding behavior as a function of nitriding parameters and steel composition. The obtained model was validated through control designs and optimized by taking into account physical and processing conditions.

  1. Reservoir Flood Control Operation Based on Multi-Objective Decision Method and Application%水库洪水调度多目标决策方法及应用

    Institute of Scientific and Technical Information of China (English)

    申海; 解建仓; 罗军刚

    2011-01-01

    To reservoir flood control operation of the multi-objective decision making information incomplete and expert decision-making fuzzy question,this paper proposed reservoir flood control operation of the multi-objective decision based on fuzzy set theory. According to the method, the goal weight with realistic and easy to program have been obtained by iterative calculation. According to the goal weight met the goal of accuracy, the value of integrated decision-making and fuzzy partition, and the type of the decision-making and an Quality sorting have been determined according to the calculated results. The decision scheme has been got, it was fulfilled both subjective preferences and objective attributes. The example showed that flood control reservoir flood control operation based on multi-objective decision making method could be used for practice simply and effectively.%针对水库洪水调度多目标决策中专家主观思维模糊、决策信息不完备的问题,提出了一种基于模糊集理论的水库洪水调度多目标决策方法.该方法首先通过迭代计算得到了符合实际要求、便于程序化实现的目标权重.然后,根据得到的目标权重确立了满足精度要求的目标综合决策值和模糊划分,根据计算结果判定各决策方案所属类别和优劣排序,得到了既兼顾主观偏好又满足客观属性的决策方案.实例结果表明了提出的基于模糊集理论的多目标决策方法对水库洪水调度是简便而有效的.

  2. Attitude Maneuver Control for Flexible Spacecraft Based on a Multi-objective Joint-optimization Model%基于多目标联合优化的挠性航天器姿态机动控制

    Institute of Scientific and Technical Information of China (English)

    周端; 杨忠

    2015-01-01

    In order to implement the attitude maneuvering control of flexible spacecraft with so-lar array in the presence of external disturbances in space,the parameters optimization of atti-tude maneuver control for spacecraft has been studied.A multi-objective joint-optimization model of the attitude maneuver control for flexible spacecraft is constructed.Based on the multi-objective optimization elitist evolutionary algorithm,the profile and the controller pro-posed are optimized simultaneously.Considering the closed-loops stability,the contradiction between rapid maneuvering performance and high stabilization performance in maneuvering of flexible spacecraft is relieved.The semi-physical experiment results demonstrate that the meth-od proposed is effective.%针对受环境干扰的带有太阳能帆板的挠性航天器,研究了航天器姿态机动控制的参数优化问题。建立了挠性航天器姿态机动控制参数的联合优化模型,并采用一种基于精英机制的多目标优化算法,设计机动路径和控制器参数,在保证闭环系统稳定的前提下,以获得较好的机动快速性和较高的稳态精度。地面模拟实验验证了所提方法的有效性。

  3. 基于NSGA-Ⅱ的IPMC机器鱼动态多目标相容优化控制%Dynamic Multi-objective Compatible Control of IPMC Propelled Robotic Fish Based on NSGA-Ⅱ

    Institute of Scientific and Technical Information of China (English)

    胡庆松; 徐立鸿; Erik Goodman

    2011-01-01

    实际控制问题中往往有多个控制目标需要兼顾,且多个目标通常情况下是冲突的。根据相邻控制步之间系统状态和控制输入的连续性,提出了一个基于NSGA-II的动态迭代多目标相容优化控制算法,并且这一算法有能力处理目标空间为非凸的控制问题和提高在线优化速度。考虑到IPMC驱动机器鱼在运行过程中能耗和速度两个关键且冲突的目标,建立IMPC驱动机器鱼的运动及能耗模型,将所提算法进行了应用。仿真结果表明了控制算法的有效性及其在慢复杂系统动态控制中的应用潜力。%It is popular that there exists multiple objectives in practical control system,and these objectives are usually competitive.Based on the tight relation between the system states of the neighboring sampling instants,a dynamic iterative multi-objective control algorithm based on NSGA-II was proposed,which could cope with nonconvex control problem as well as improve the computing speed.Considering the two key and conflicting objectives-speed and energy consumption,the robotic fish velocityand engery consumption model was established and the proposed algorithm was successfully applied.The simulation result shows the validity of the algorithm and its application potential for the multi-objective evolutionary algorithm to the slow complex varying system online control.

  4. Cosmological surveys with multi-object spectrographs

    CERN Document Server

    Colless, Matthew

    2016-01-01

    Multi-object spectroscopy has been a key technique contributing to the current era of 'precision cosmology'. From the first exploratory surveys of the large-scale structure and evolution of the universe to the current generation of superbly detailed maps spanning a wide range of redshifts, multi-object spectroscopy has been a fundamentally important tool for mapping the rich structure of the cosmic web and extracting cosmological information of increasing variety and precision. This will continue to be true for the foreseeable future, as we seek to map the evolving geometry and structure of the universe over the full extent of cosmic history in order to obtain the most precise and comprehensive measurements of cosmological parameters. Here I briefly summarize the contributions that multi-object spectroscopy has made to cosmology so far, then review the major surveys and instruments currently in play and their prospects for pushing back the cosmological frontier. Finally, I examine some of the next generation ...

  5. Multi-Objective Sliding Mode Control on Vehicle Cornering Stability with Variable Gear Ratio Actuator-Based Active Front Steering Systems.

    Science.gov (United States)

    Ma, Xinbo; Wong, Pak Kin; Zhao, Jing; Xie, Zhengchao

    2016-12-28

    Active front steering (AFS) is an emerging technology to improve the vehicle cornering stability by introducing an additional small steering angle to the driver's input. This paper proposes an AFS system with a variable gear ratio steering (VGRS) actuator which is controlled by using the sliding mode control (SMC) strategy to improve the cornering stability of vehicles. In the design of an AFS system, different sensors are considered to measure the vehicle state, and the mechanism of the AFS system is also modelled in detail. Moreover, in order to improve the cornering stability of vehicles, two dependent objectives, namely sideslip angle and yaw rate, are considered together in the design of SMC strategy. By evaluating the cornering performance, Sine with Dwell and accident avoidance tests are conducted, and the simulation results indicate that the proposed SMC strategy is capable of improving the cornering stability of vehicles in practice.

  6. Principal-subordinate hierarchical multi-objective programming model of initial water rights allocation

    Directory of Open Access Journals (Sweden)

    Dan WU

    2009-06-01

    Full Text Available The principal-subordinate hierarchical multi-objective programming model of initial water rights allocation was developed based on the principle of coordinated and sustainable development of different regions and water sectors within a basin. With the precondition of strictly controlling maximum emissions rights, initial water rights were allocated between the first and the second levels of the hierarchy in order to promote fair and coordinated development across different regions of the basin and coordinated and efficient water use across different water sectors, realize the maximum comprehensive benefits to the basin, promote the unity of quantity and quality of initial water rights allocation, and eliminate water conflict across different regions and water sectors. According to interactive decision-making theory, a principal-subordinate hierarchical interactive iterative algorithm based on the satisfaction degree was developed and used to solve the initial water rights allocation model. A case study verified the validity of the model.

  7. Principal-subordinate hierarchical multi-objective programming model of initial water rights allocation

    Institute of Scientific and Technical Information of China (English)

    Dan WU; Feng-ping WU; Yan-ping CHEN

    2009-01-01

    The principal-subordinate hierarchical multi-objective programming model of initial water rights allocation was developed based on the principle of coordinated and sustainable development of different regions and water sectors within a basin. With the precondition of strictly controlling maximum emissions rights, initial water rights were allocated between the first and the second levels of the hierarchy in order to promote fair and coordinated development across different regions of the basin and coordinated and efficient water use across different water sectors, realize the maximum comprehensive benefits to the basin, promote the unity of quantity and quality of initial water rights allocation, and eliminate water conflict across different regions and water sectors. According to interactive decision-making theory, a principal-subordinate hierarchical interactive iterative algorithm based on the satisfaction degree was developed and used to solve the initial water rights allocation model. A case study verified the validity of the model.

  8. Integrated control of emission reductions, energy-saving, and cost-benefit using a multi-objective optimization technique in the pulp and paper industry.

    Science.gov (United States)

    Wen, Zongguo; Xu, Chang; Zhang, Xueying

    2015-03-17

    Reduction of water pollutant emissions and energy consumption is regarded as a key environmental objective for the pulp and paper industry. The paper develops a bottom-up model called the Industrial Water Pollutant Control and Technology Policy (IWPCTP) based on an industrial technology simulation system and multiconstraint technological optimization. Five policy scenarios covering the business as usual (BAU) scenario, the structural adjustment (SA) scenario, the cleaner technology promotion (CT) scenario, the end-treatment of pollutants (EOP) scenario, and the coupling measures (CM) scenario have been set to describe future policy measures related to the development of the pulp and paper industry from 2010-2020. The outcome of this study indicates that the energy saving amount under the CT scenario is the largest, while that under the SA scenario is the smallest. Under the CT scenario, savings by 2020 include 70 kt/year of chemical oxygen demand (COD) emission reductions and savings of 7443 kt of standard coal, 539.7 ton/year of ammonia nitrogen (NH4-N) emission reductions, and savings of 7444 kt of standard coal. Taking emission reductions, energy savings, and cost-benefit into consideration, cleaner technologies like highly efficient pulp washing, dry and wet feedstock preparation, and horizontal continuous cooking, medium and high consistency pulping and wood dry feedstock preparation are recommended.

  9. Dynamic multi-objective optimisation using PSO

    CSIR Research Space (South Africa)

    Greeff, M

    2010-01-01

    Full Text Available Functions. In Proc. of 2nd Italian Workshop on Evolutionary Computation and 3rd Italian Workshop on Artificial Life, 2006. 13. I. Hatzakis and D. Wallace. Dynamic Multi-Objective Optimization with Evolu- tionary Algorithms: A Forward Looking Approach...

  10. Covariance matrix adaptation for multi-objective optimization.

    Science.gov (United States)

    Igel, Christian; Hansen, Nikolaus; Roth, Stefan

    2007-01-01

    The covariance matrix adaptation evolution strategy (CMA-ES) is one of the most powerful evolutionary algorithms for real-valued single-objective optimization. In this paper, we develop a variant of the CMA-ES for multi-objective optimization (MOO). We first introduce a single-objective, elitist CMA-ES using plus-selection and step size control based on a success rule. This algorithm is compared to the standard CMA-ES. The elitist CMA-ES turns out to be slightly faster on unimodal functions, but is more prone to getting stuck in sub-optimal local minima. In the new multi-objective CMAES (MO-CMA-ES) a population of individuals that adapt their search strategy as in the elitist CMA-ES is maintained. These are subject to multi-objective selection. The selection is based on non-dominated sorting using either the crowding-distance or the contributing hypervolume as second sorting criterion. Both the elitist single-objective CMA-ES and the MO-CMA-ES inherit important invariance properties, in particular invariance against rotation of the search space, from the original CMA-ES. The benefits of the new MO-CMA-ES in comparison to the well-known NSGA-II and to NSDE, a multi-objective differential evolution algorithm, are experimentally shown.

  11. Overview of multi-objective optimization methods

    Institute of Scientific and Technical Information of China (English)

    雷秀娟; 史忠科

    2004-01-01

    To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.

  12. 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.

  13. Novel multi-objective optimization algorithm

    Institute of Scientific and Technical Information of China (English)

    Jie Zeng; Wei Nie

    2014-01-01

    Many multi-objective evolutionary algorithms (MOEAs) can converge to the Pareto optimal front and work wel on two or three objectives, but they deteriorate when faced with many-objective problems. Indicator-based MOEAs, which adopt various indicators to evaluate the fitness values (instead of the Pareto-dominance relation to select candidate solutions), have been regarded as promising schemes that yield more satisfactory re-sults than wel-known algorithms, such as non-dominated sort-ing genetic algorithm (NSGA-II) and strength Pareto evolution-ary algorithm (SPEA2). However, they can suffer from having a slow convergence speed. This paper proposes a new indicator-based multi-objective optimization algorithm, namely, the multi-objective shuffled frog leaping algorithm based on the ε indicator (ε-MOSFLA). This algorithm adopts a memetic meta-heuristic, namely, the SFLA, which is characterized by the powerful capa-bility of global search and quick convergence as an evolutionary strategy and a simple and effective ε-indicator as a fitness as-signment scheme to conduct the search procedure. Experimental results, in comparison with other representative indicator-based MOEAs and traditional Pareto-based MOEAs on several standard test problems with up to 50 objectives, show thatε-MOSFLA is the best algorithm for solving many-objective optimization problems in terms of the solution quality as wel as the speed of convergence.

  14. Advanced Coordinating Control System for Power Plant

    Institute of Scientific and Technical Information of China (English)

    WU Peng; WEI Shuangying

    2006-01-01

    The coordinating control system is popular used in power plant. This paper describes the advanced coordinating control by control methods and optimal operation, introduces their principals and features by using the examples of power plant operation. It is wealthy for automation application in optimal power plant operation.

  15. 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.

  16. Optimal Trajectory Planning and Coordinated Tracking Control Method of Tethered Space Robot Based on Velocity Impulse

    Directory of Open Access Journals (Sweden)

    Panfeng Huang

    2014-09-01

    Full Text Available The tethered space robot (TSR is a new concept of space robot which consists of a robot platform, space tether and operation robot. This paper presents a multi-objective optimal trajectory planning and a coordinated tracking control scheme for TSR based on velocity impulse in the approaching phase. Both total velocity impulse and flight time are included in this optimization. The non-dominated sorting genetic algorithm is employed to obtain the optimal trajectory Pareto solution using the TSR dynamic model and optimal trajectory planning model. The coordinated tracking control scheme utilizes optimal velocity impulse. Furthermore, the PID controller is designed in order to compensate for the distance measurement errors. The PID control force is optimized and distributed to thrusters and the space tether using a simulated annealing algorithm. The attitude interferential torque of the space tether is compensated a using time-delay algorithm through reaction wheels. The simulation results show that the multi-objective optimal trajectory planning method can reveal the relationships among flight time, fuel consumption, planar view angle and velocity impulse number. This method can provide a series of optimal trajectory according to a number of special tasks. The coordinated control scheme can significantly save thruster fuel for tracking the optimal trajectory, restrain the attitude interferential torque produced by space tether and maintain the relative attitude stability of the operation robot.

  17. 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...... simultaneously, aiming at integrating the market operation and planning as one unified process in the market environment. Subsequently, reliability assessment is performed to evaluate and reinforce the resultant expansion plan from MOOP. The proposed method has been tested with the IEEE 14-bus system...

  18. A Multi-Objective Demand Side Management Considering ENS Cost in Smart Grids

    DEFF Research Database (Denmark)

    Yousefi Khanghah, Babak; Ghassemzadeh, Saeid; Hosseini, Seyed Hossein

    2017-01-01

    In this paper a new method is presented to achieve economic exploitation and proper usage of network capacity by exerting controlling actions over flexible loads and energy storage (ES) equipment. Multi-objective planning for demand response programs (DRP) and battery management policies is carri...... company (DisCo) modifies energy cost as a signal for DGO in order to coordinate with each other. So, behavior of DGO is based on modified energy price applied by upstream system considering ENS price....... out by considering energy not supplied (ENS). In order to achieve an optimal scheduling, charge/discharge control for batteries, demand response programs and dispatch of controllable distributed generations (DGs) are also considered. Then, the balanced cost and benefits of participants are evaluated....... As a whole, the main objective of this paper is to manage the load and energy storage options in a smart grid to reduce ENS, to minimize overall operation cost and to maximize DG operators’ (DGOs) profit. These goals are obtained by considering ENS cost in a multi-objective optimization problem. Distribution...

  19. Multi-Object Spectroscopy with MUSE

    Science.gov (United States)

    Kelz, A.; Kamann, S.; Urrutia, T.; Weilbacher, P.; Bacon, R.

    2016-10-01

    Since 2014, MUSE, the Multi-Unit Spectroscopic Explorer, is in operation at the ESO-VLT. It combines a superb spatial sampling with a large wavelength coverage. By design, MUSE is an integral-field instrument, but its field-of-view and large multiplex make it a powerful tool for multi-object spectroscopy too. Every data-cube consists of 90,000 image-sliced spectra and 3700 monochromatic images. In autumn 2014, the observing programs with MUSE have commenced, with targets ranging from distant galaxies in the Hubble Deep Field to local stellar populations, star formation regions and globular clusters. This paper provides a brief summary of the key features of the MUSE instrument and its complex data reduction software. Some selected examples are given, how multi-object spectroscopy for hundreds of continuum and emission-line objects can be obtained in wide, deep and crowded fields with MUSE, without the classical need for any target pre-selection.

  20. Multi-Object Spectroscopy with MUSE

    CERN Document Server

    Kelz, Andreas; Urrutia, Tanya; Weilbacher, Peter; Bacon, Roland

    2015-01-01

    Since 2014, MUSE, the Multi-Unit Spectroscopic Explorer, is in operation at the ESO-VLT. It combines a superb spatial sampling with a large wavelength coverage. By design, MUSE is an integral-field instrument, but its field-of-view and large multiplex make it a powerful tool for multi-object spectroscopy too. Every data-cube consists of 90,000 image-sliced spectra and 3700 monochromatic images. In autumn 2014, the observing programs with MUSE have commenced, with targets ranging from distant galaxies in the Hubble Deep Field to local stellar populations, star formation regions and globular clusters. This paper provides a brief summary of the key features of the MUSE instrument and its complex data reduction software. Some selected examples are given, how multi-object spectroscopy for hundreds of continuum and emission-line objects can be obtained in wide, deep and crowded fields with MUSE, without the classical need for any target pre-selection.

  1. Multi-objective based spectral unmixing for hyperspectral images

    Science.gov (United States)

    Xu, Xia; Shi, Zhenwei

    2017-02-01

    Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method.

  2. Modeling, Control and Coordination of Helicopter Systems

    CERN Document Server

    Ren, Beibei; Chen, Chang; Fua, Cheng-Heng; Lee, Tong Heng

    2012-01-01

    Modeling, Control and Coordination of Helicopter Systems provides a comprehensive treatment of helicopter systems, ranging from related nonlinear flight dynamic modeling and stability analysis to advanced control design for single helicopter systems, and also covers issues related to the coordination and formation control of multiple helicopter systems to achieve high performance tasks. Ensuring stability in helicopter flight is a challenging problem for nonlinear control design and development. This book is a valuable reference on modeling, control and coordination of helicopter systems,providing readers with practical solutions for the problems that still plague helicopter system design and implementation. Readers will gain a complete picture of helicopters at the systems level, as well as a better understanding of the technical intricacies involved. This book also: Presents a complete picture of modeling, control and coordination for helicopter systems Provides a modeling platform for a general class of ro...

  3. Control coordination abilities in shock combat sports

    Directory of Open Access Journals (Sweden)

    Natalya Boychenko

    2014-12-01

    Full Text Available Purpose: optimize the process control level of coordination abilities in martial arts. Material and Methods: analysis and compilation of scientific and methodological literature, interviews with coaches of drum martial arts, video analysis techniques, teacher observations. Results: identified specific types of coordination abilities in shock combat sports. Pod branny and offered specific and nonspecific tests to monitor the level of species athletes coordination abilities. Conclusion: it is determined that in order to achieve victory in the fight martial artists to navigate the space to be able to assess and manage dynamic and spatio-temporal parameters of movements, maintain balance, have a high coordination of movements. The proposed tests to monitor species coordination abilities athletes allow an objective assessment of not only the overall level of coordination, and the level of specific types of manifestations of this ability.

  4. 中厚板超快速冷却多目标控制研究及应用%Research and application of multi-objective control of ultra fast cooling for plate

    Institute of Scientific and Technical Information of China (English)

    刘涛; 余伟; 李彦彬; 殷实; 何春雨

    2015-01-01

    The cooling temperature is the main control target for the traditional cooling system,while the control of the cooling rate and the cooling uniformity is ignored. The ultra fast cooling system is developed in this paper to realize the multi-objective control of the cooling route and the uniform cool-ing. That is to say,mathematical models are optimized and the corresponding control strategies are de-veloped to achieve the accurate control of the final cooling temperature and the cooling rate,and the uniform cooling in plate width,length and thickness directions is achieved through strategies such as edge masking,head and tail masking, micro acceleration of the roll table, symmetrical cooling, etc. The ultra fast cooling system is applied in a certain domestic iron and steel corporation. The results show that the indices such as the final cooling temperature,the cooling rate,the temperature uniformi-ty after cooling, the performance uniformity, etc. meet the control requirements, and the application effect is good.%传统的轧后控冷系统以温度作为主要控制目标,忽略了对冷却速率及冷却均匀性的控制,为此,作者开发了超快速冷却系统,实现了冷却路径及均匀冷却的多目标控制,即,通过优化数学模型算法及相应控制策略实现终冷温度及冷却速率精确控制,通过边部遮蔽、头尾遮蔽、辊道微加速及对称冷却等策略实现钢板横向、纵向及厚度方向的均匀冷却。超快冷系统在国内某钢铁公司应用后,终冷温度、冷却速率、冷却后温度均匀性及性能均匀性等指标均达到控制要求,应用效果良好。

  5. Multi-objective optimization of inverse planning for accurate radiotherapy

    Institute of Scientific and Technical Information of China (English)

    曹瑞芬; 吴宜灿; 裴曦; 景佳; 李国丽; 程梦云; 李贵; 胡丽琴

    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 pl

  6. Coordinated intelligent adaptive control of legged robots

    Science.gov (United States)

    McLauchlan, Lifford; Mehrübeoğlu, Mehrübe

    2006-05-01

    In planetary or hazardous environment exploration, there will be unforseen environmental circumstances which can not be planned. To overcome telerobotic control issues due to communication delays, autonomous robot control becomes necessary. Autonomously controlled landers and instrumentation can be used in exploration, such as lunar and martian missions. However, wheeled robots have difficulty in exploring uneven terrain; thus, legged robots can be used in such situations. This research develops intelligent and adaptive control of mobile robots to perform functions such as environmental exploration in coordination and obstacle avoidance. The coordinated control is demonstrated in simulations.

  7. Improved multi-objective evolutionary algorithm for optimization control in greenhouse environment%基于改进多目标进化算法的温室环境优化控制

    Institute of Scientific and Technical Information of China (English)

    王立舒; 侯涛; 姜淼

    2014-01-01

    A greenhouse environment control system plays a decisive role in greenhouse production processes and is a complex system to control. This paper provides an overview of a greenhouse control system and control technologies. We investigated the issue of a greenhouse climate control system based on temperature and humidity, and formulated a greenhouse climate dynamic model. The control strategy was presented for the dynamic model made use of conventional Proportional Integral and Derivative (PID) control algorithms in which it combined with an modified multi-objective evolutionary algorithm (MNSEA-II) based on NSGA-II. In MNSEA-II, mixed mutation strategy and local search strategy were utilized to tune two PID controller parameters, and the integrated time square error (ITSE) was considered as one of performance criteria. The mixed mutation strategy based on game theory could utilize adaptively the advantages of a different mutation operator to maintain the globe search capacity of population for a diversity of Pareto solutions, and the local search strategy could speed the convergence of algorithms to achieve more precise solutions. The mixed mutation strategy and the local search strategy could obtain an equilibrium between the diversity and precision of Pareto solutions. An evolutionary optimization process was employed to approximate the set of Pareto solutions, which was used to tune PID controller parameters to achieve good control performance. The tuning scheme has been tested for greenhouse climate control by minimizing ITSE and control increment or rate in a simulation system. Simulation results showed the effectiveness and usability of the proposed method for step responses. The obtained gains were applied in PID controllers and could achieve good control performance such as small overshoot, fast settling time, and less rise time and steady state error. The proposed optimization method offers an effective way to implement simple but robust solutions providing

  8. A Direct Torque Control Algorithm of Five-phase Permanent-magnet Machines With Multi-objective Optimization%五相永磁同步电机多目标优化直接转矩控制算法

    Institute of Scientific and Technical Information of China (English)

    薛诚; 宋文胜; 冯晓云

    2016-01-01

    The multi-phase permanent magnet synchronous machine (PMSM) has the property of small size, low noise and high power density, so it has been widely used in high-power and low-voltage occasion. Based on the analysis of traditional direct torque control (DTC) models and space vector pulse width modulation (SVPWM) technologies, a fixed switching frequency modified DTC algorithm of five-phase PMSM with multi-objective optimization was proposed, which aims at improving the trajectory of stator flux effectively, reducing torque ripples and low harmonic components of stator currents. The proposed algorithm brings the following benefits: such as fast torque control dynamic response, the fixed switching frequency, the optimization control of torque, stator flux and stator phase currents. Simulation and experimental results show the feasibility and effectiveness of the proposed algorithm.%多相永磁同步电机驱动系统因具有体积小、噪声低、功率密度高等诸多优点,在低压、大功率输出及可靠性要求高的场合已得到了广泛关注和应用。该文以五相永磁同步电机为研究对象,分析并建立传统直接转矩控制(direct torque control,DTC)算法数学模型,在此基础上,以进一步优化转矩纹波及磁链轨迹,降低定子电流低次谐波为控制目标,结合空间矢量脉宽调制(space vector pulse width modulation, SVPWM)技术,提出改进DTC的定频多目标优化控制模型。该算法不仅保持了传统DTC快速的转矩控制动态响应,实现了开关频率恒定及转矩的无稳态误差,同时也兼顾了定子磁链以及相电流的优化控制。仿真和实验结果均表明该算法的正确性和有效性。

  9. Optimal coordinated voltage control of power systems

    Institute of Scientific and Technical Information of China (English)

    LI Yan-jun; HILL David J.; WU Tie-jun

    2006-01-01

    An immune algorithm solution is proposed in this paper to deal with the problem of optimal coordination of local physically based controllers in order to preserve or retain mid and long term voltage stability. This problem is in fact a global coordination control problem which involves not only sequencing and timing different control devices but also tuning the parameters of controllers. A multi-stage coordinated control scheme is presented, aiming at retaining good voltage levels with minimal control efforts and costs after severe disturbances in power systems. A self-pattern-recognized vaccination procedure is developed to transfer effective heuristic information into the new generation of solution candidates to speed up the convergence of the search procedure to global optima. An example of four bus power system case study is investigated to show the effectiveness and efficiency of the proposed algorithm, compared with several existing approaches such as differential dynamic programming and tree-search.

  10. Large Sky Area Multi-Object Fiber Spectroscopic Telescope

    Institute of Scientific and Technical Information of China (English)

    ZHAO Yongheng

    2011-01-01

    The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) is a meridian reflecting Schmidt telescope with a clear aperture of four meters, a focal length of 20 meters and a field of view of five degrees. By using active optics technique to control its reflecting corrector, the LAMOST is made a unique astronomical instrument in combining a large aperture with a wide field of view. The available large focal plane of 1.75 meter in diameter can accommodate up to 4,000 fibers,

  11. Multi objective SNP selection using pareto optimality.

    Science.gov (United States)

    Gumus, Ergun; Gormez, Zeliha; Kursun, Olcay

    2013-04-01

    Biomarker discovery is a challenging task of bioinformatics especially when targeting high dimensional problems such as SNP (single nucleotide polymorphism) datasets. Various types of feature selection methods can be applied to accomplish this task. Typically, using features versus class labels of samples in the training dataset, these methods aim at selecting feature subsets with maximal classification accuracies. Although finding such class-discriminative features is crucial, selection of relevant SNPs for maximizing other properties that exist in the nature of population genetics such as the correlation between genetic diversity and geographical distance of ethnic groups can also be equally important. In this work, a methodology using a multi objective optimization technique called Pareto Optimal is utilized for selecting SNP subsets offering both high classification accuracy and correlation between genomic and geographical distances. In this method, discriminatory power of an SNP is determined using mutual information and its contribution to the genomic-geographical correlation is estimated using its loadings on principal components. Combining these objectives, the proposed method identifies SNP subsets that can better discriminate ethnic groups than those obtained with sole mutual information and yield higher correlation than those obtained with sole principal components on the Human Genome Diversity Project (HGDP) SNP dataset.

  12. Multi-Objective Coordinated Scheduling of Electric Vehicles and Renewable Generation Based on Improved Chemical Reaction Optimization Algorithm%基于改进化学反应优化算法的电动汽车与可再生能源多目标协同调度

    Institute of Scientific and Technical Information of China (English)

    张智晟; 温令云; 李国; 张伟

    2014-01-01

    To mitigate the impact of fluctuation of renewable generation output on power grid and accelerate the population of electric vehicles (EV), taking the minimized output fluctuation of renewable generation and the maximized income of EV users as objective function and considering the constraints of the electricity quantity stored in the battery, the charging/discharging power and the charging/discharging times, a multi-objective coordinated scheduling model, in which the grid-connectible EV, wind power generation system and photovoltaic (PV) generation system are taken into account simultaneously, is established. A virtual ideal molecular based multi-objective improved chemical reaction optimization algorithm(CROA) is proposed, and the established model is solved by the proposed algorithm. In allusion to such defects of CROA as slow convergence and low accuracy, the update mode of particle swarm optimization algorithm is integrated with the improved CROA. Results of calculation example show that through reasonably arranging the charging/discharging of EV, the output fluctuation of renewable generation can be effectively suppressed and the income of EV users can be increased. Comparison results show that the virtual ideal molecular based improved multi-objective CROA possesses strong searching ability.%为减小可再生能源出力波动对电网的影响以及加快电动汽车的普及速率,以最小化可再生能源的出力波动和最大化电动汽车用户收益为目标函数,计及电池储存电量约束、充放电功率约束和充放电次数约束等条件,建立了同时计及可入网电动汽车、风力发电和光伏发电系统的多目标协同调度模型。提出了基于虚拟理想分子的多目标改进化学反应优化算法(chemical reaction optimization algorithm , CROA),并用该算法对模型进行了求解,针对化学反应算法收敛速度慢、精度低的缺陷,在算法中融入了粒子群优化算法的更

  13. Multi-Objective Model Checking of Markov Decision Processes

    CERN Document Server

    Etessami, Kousha; Vardi, Moshe Y; Yannakakis, Mihalis

    2008-01-01

    We study and provide efficient algorithms for multi-objective model checking problems for Markov Decision Processes (MDPs). Given an MDP, $M$, and given multiple linear-time ($\\omega$-regular or LTL) properties $\\varphi_i$, and probabilities $r_i \\in [0,1]$, $i=1,...,k$, we ask whether there exists a strategy $\\sigma$ for the controller such that, for all $i$, the probability that a trajectory of $M$ controlled by $\\sigma$ satisfies $\\varphi_i$ is at least $r_i$. We provide an algorithm that decides whether there exists such a strategy and if so produces it, and which runs in time polynomial in the size of the MDP. Such a strategy may require the use of both randomization and memory. We also consider more general multi-objective $\\omega$-regular queries, which we motivate with an application to assume-guarantee compositional reasoning for probabilistic systems. Note that there can be trade-offs between different properties: satisfying property $\\varphi_1$ with high probability may necessitate satisfying $\\var...

  14. Influence of aging on bimanual coordination control.

    Science.gov (United States)

    Lin, Chueh-Ho; Chou, Li-Wei; Wei, Shun-Hwa; Lieu, Fu-Kong; Chiang, Shang-Lin; Sung, Wen-Hsu

    2014-05-01

    Degeneration in the neuromuscular system due to aging can affect daily activities that need to be controlled by bimanual coordination with both hands. However, little is known about the influence of aging on grip strength and bimanual coordination control between hands. The purpose of this study was to investigate the influence of aging on the maximum grip force output and capacity of coordination control of two hands. Ten healthy elderly and 21 young adults were recruited and asked to execute maximum grip force tests and bimanual coordination control tasks with reciprocal grasping, holding, and releasing of a dynamometer with both hands at three target force levels (10, 20 and 40% maximal voluntary contraction, MVC). Compared with the young group, the maximum grip force of the hands of the elderly group was significantly lower by 77.5% (pelderly adults also displayed a significantly longer alternating time control in the dominant to non-dominant and non-dominant to dominant hands at the 20% MVC target force level (pcoordination control of two hands, which may lead to difficulty with the execution of daily activities requiring both hands.

  15. Coordinated Voltage Control of Active Distribution Network

    Directory of Open Access Journals (Sweden)

    Xie Jiang

    2016-01-01

    Full Text Available This paper presents a centralized coordinated voltage control method for active distribution network to solve off-limit problem of voltage after incorporation of distributed generation (DG. The proposed method consists of two parts, it coordinated primal-dual interior point method-based voltage regulation schemes of DG reactive powers and capacitors with centralized on-load tap changer (OLTC controlling method which utilizes system’s maximum and minimum voltages, to improve the qualified rate of voltage and reduce the operation numbers of OLTC. The proposed coordination has considered the cost of capacitors. The method is tested using a radial edited IEEE-33 nodes distribution network which is modelled using MATLAB.

  16. Waste Minimization Through Process Integration and Multi-objective Optimization

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    By avoiding or reducing the production of waste, waste minimization is an effective approach to solve the pollution problem in chemical industry. Process integration supported by multi-objective optimization provides a framework for process design or process retrofit by simultaneously optimizing on the aspects of environment and economics. Multi-objective genetic algorithm is applied in this area as the solution approach for the multi-objective optimization problem.

  17. Motor Control: CRF Regulates Coordination and Gait.

    Science.gov (United States)

    Manto, Mario

    2017-09-11

    The function of the olivo-cerebellar tract is not restricted to the supervision of plasticity in the cerebellar cortex. There is growing evidence that the climbing fibers also tune motor commands. A novel study unravels a role of corticotropin-releasing factor (CRF) in motor coordination and gait control. Copyright © 2017 Elsevier Ltd. All rights reserved.

  18. Neuromuscular Control and Coordination during Cycling

    Science.gov (United States)

    Li, Li

    2004-01-01

    The neuromuscular control aspect of cycling has been investigated through the effects of modifying posture and cadence. These studies show that changing posture has a more profound influence on neuromuscular coordination than does changing slope. Most of the changes with standing posture occur late in the downstroke: increased ankle and knee joint…

  19. Coordinated formation control of multiple nonlinear systems

    Institute of Scientific and Technical Information of China (English)

    Wei KANG; Ning XI; Jindong TAN; Yiwen ZHAO; Yuechao WANG

    2005-01-01

    A general method of controller design is developed for the purpose of formation keeping and reconfiguration of nonlinear systems with multiple subsystems,such as the formation of multiple aircraft,ground vehicles,or robot arms.The model consists of multiple nonlinear systems.Controllers are designed to keep the subsystems in a required formation and to coordinate the subsystems in the presence of environmental changes.A step-by-step algorithm of controller design is developed.Sufficient conditions for the stability of formation tracking are proved.Simulations and experiments are conducted to demonstrate some useful coordination strategies such as movement with a leader,simultaneous movement,series connection of formations,and human-machine interaction.

  20. The coordinate system for force control.

    Science.gov (United States)

    Saha, Devjani J; Hu, Xiao; Perreault, Eric; Murray, Wendy; Mussa-Ivaldi, Ferdinando A

    2015-03-01

    The primary objective of this study was to establish the coordinate frame for force control by observing how parameters of force that are not explicitly specified by a motor task vary across the workspace. We asked subjects to apply a force of a specific magnitude with their hand. Subjects could complete the task by applying forces in any direction of their choice in the transverse plane. They were tested with the arm in seven different configurations. To estimate whether contact forces are represented in extrinsic or intrinsic coordinates, we applied the parallel transport method of differential geometry to the net joint torques applied during the task. This approach allowed us to compare the force variability observed at different arm configurations with the force variability that would be expected if the control system were applying an invariant pattern of joint torques at the tested configurations. The results indicate that for the majority of the subjects, the predominant pattern was consistent with an invariant representation in joint coordinates. However, two out of eleven subjects also demonstrated a preference for extrinsic representation. These findings suggest that the central nervous system can represent contact forces in both coordinate frames, with a prevalence toward intrinsic representations.

  1. 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 depen

  2. APPLICATION OF FUZZY MATHEMATICS IN MULTI-OBJECTIVE OPTIMAL DESIGN

    Institute of Scientific and Technical Information of China (English)

    1999-01-01

    In order to overcome the problem that theoretical research lags behind practical application in the multi-objective optimal design,a practical method is suggested.In this method the fuzzy nearness is used to seek an overall solution of the multi-objective optimal design and analyse the features of the curved surface.The method is tested using three practical examples.

  3. MOOPPS: An Optimization System for Multi Objective Scheduling

    CERN Document Server

    Geiger, Martin Josef

    2008-01-01

    In the current paper, we present an optimization system solving multi objective production scheduling problems (MOOPPS). The identification of Pareto optimal alternatives or at least a close approximation of them is possible by a set of implemented metaheuristics. Necessary control parameters can easily be adjusted by the decision maker as the whole software is fully menu driven. This allows the comparison of different metaheuristic algorithms for the considered problem instances. Results are visualized by a graphical user interface showing the distribution of solutions in outcome space as well as their corresponding Gantt chart representation. The identification of a most preferred solution from the set of efficient solutions is supported by a module based on the aspiration interactive method (AIM). The decision maker successively defines aspiration levels until a single solution is chosen. After successfully competing in the finals in Ronneby, Sweden, the MOOPPS software has been awarded the European Academ...

  4. 多目标粒子群优化算法在光伏MPPT中的应用%Application of Multi-objective Particle Swarm Optimization Algorithms in MPPT Control Method of PV System

    Institute of Scientific and Technical Information of China (English)

    周天沛; 孙伟

    2012-01-01

    In order to improve the utilization rate of photovoltaic cells, it is necessary to track the maximum power point of photovoltaic array. Aiming at shortages of traditional PSO algorithm for multi-objective optimization, multi-objective PSO algorithm based on minimal particle angles is proposed. The global optimal particle is updated by comparison of angles among different particles in objective space. The method of updating local optimal particle and swarm is presented based on comparison of particle densities. The maximum power point tracking method is established and simulated with Matlab/Simulink. Simulation results show that the algorithm can rapidly and accurately track the maximum power point when the external environment changes and it ensures the stability of PV system.%为提高光伏电池的利用率,需要进行光伏阵列的最大功率点跟踪(MPPT),针对传统粒子群优化算法在多目标优化中的不足,提出了基于最小粒子角度的多目标粒子群优化算法,利用目标空间中不同粒子之间的角度进行粒子全局极值更新,通过比较粒子的浓度值给出粒子群及粒子个体极值更新方法,并在Matlab/Simulink下进行了建模与仿真.仿真结果显示,该算法在外界环境变化时能快速准确地跟踪太阳能电池的最大功率点,并能保证系统的稳定性.

  5. 基于模拟驾驶员多目标决策的汽车自适应巡航控制算法%A Vehicle Adaptive Cruise Control Algorithm Based on Simulating Driver’s Multi-objective Decision Making

    Institute of Scientific and Technical Information of China (English)

    高振海; 严伟; 李红建; 胡振程

    2015-01-01

    汽车自适应巡航控制系统根据本车与前车之间的相对距离和相对速度,综合考虑车间行驶安全性、本车纵向动力学特性和驾乘人员的舒适性等多个相互关联且存在一定矛盾的性能指标,实现本车与前车安全车间距的保持控制。针对这一多目标协调控制问题,本文在动态输出反馈控制框架下,模拟真实驾驶员对车间距控制的行为特性,利用汽车行驶状态和控制变量建立了安全性、轻便性、舒适性和工效性指标,进而基于不变集和二次有界性理论提出了以上多性能指标的动态协调控制机制,建立了一套自适应巡航控制系统的车间距控制算法。最终通过跟随、驶离和切入3种典型工况的仿真,验证了算法对安全车间距保持和协调多性能指标的可行性和有效性。%According to the distance and relative velocity between host vehicle and preceding vehicle, the adaptive cruise control (ACC) system concurrently considers three correlated and contradictory performance indica-tors of driving safety between vehicles, longitudinal dynamics characteristics and the comfort performance of driver and occupants to achieve the control for maintaining safe inter-vehicle distance. Aiming at this multi-objective coor-dinated control problem, the behavior characteristics of real driver in inter-vehicle distance control are simulated un-der the framework of dynamic output feedback control and four indicators of safety, handiness, comfort and efficien-cy are set up based on the driving state and control variables of vehicle. Then a dynamic coordinated control mecha-nism for above-mentioned performance indicators is proposed based on invariant set and quadratic boundedness theo-ry and a set of inter-vehicle distance control algorithms for ACC system are worked out. Finally simulations on three typical operation conditions (following and the cut-out and cut-in of preceding vehicle) are

  6. Decentralized Coordinated Control Strategy of Islanded Microgrids

    DEFF Research Database (Denmark)

    Wu, Dan

    as grid voltage/frequency regulation. In order to enhance the reliability of overall islanded Microgrid operation, basic functions of coordinated control which taking into account the state of charge (SoC) limitation and power availability of renewable energy sources is implemented in a distributed level...... control strategies in this thesis, in order to promote the decentralization of the overall system. Especially the consensus algorithm based secondary level is investigated in the thesis in order to simplify the communication configuration which only flood information through the neighboring units...

  7. 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

  8. Structural Multi-objective Probabilistic Design for Six Sigma

    Institute of Scientific and Technical Information of China (English)

    LI Yu-qiang; CUI Zhen-shan; CHEN Jun; ZHANG Dong-juan; RUAN Xue-yu

    2007-01-01

    Uncertainties in engineering design may lead to low reliable solutions that also exhibit high sensitivity to uncontrollable variations. In addition, there often exist several conflicting objectives and constraints in various design environments. In order to obtain solutions that are not only "multi-objectively" optimal, but also reliable and robust, a probabilistic optimization method was presented by integrating six sigma philosophy and multi-objective genetic algorithm. With this method, multi-objective genetic algorithm was adopted to obtain the global Pareto solutions, and six sigma method was used to improve the reliability and robustness of those optimal solutions. Two engineering design problems were provided as examples to illustrate the proposed method.

  9. Balanced Combinations of Solutions in Multi-Objective Optimization

    CERN Document Server

    Glaßer, Christian; Witek, Maximilian

    2010-01-01

    For every list of integers x_1, ..., x_m there is some j such that x_1 + ... + x_j - x_{j+1} - ... - x_m \\approx 0. So the list can be nearly balanced and for this we only need one alternation between addition and subtraction. But what if the x_i are k-dimensional integer vectors? Using results from topological degree theory we show that balancing is still possible, now with k alternations. This result is useful in multi-objective optimization, as it allows a polynomial-time computable balance of two alternatives with conflicting costs. The application to two multi-objective optimization problems yields the following results: - A randomized 1/2-approximation for multi-objective maximum asymmetric traveling salesman, which improves and simplifies the best known approximation for this problem. - A deterministic 1/2-approximation for multi-objective maximum weighted satisfiability.

  10. Duality Theorems on Multi-objective Programming of Generalized Functions

    Institute of Scientific and Technical Information of China (English)

    Li-ping Pang; Wei Wang; Zun-quan Xia

    2006-01-01

    The form of a dual problem of Mond-Weir type for multi-objective programming problems of generalized functions is defined and theorems of the weak duality, direct duality and inverse duality are proven.

  11. Scalable and Practical Multi-Objective Distribution Network Expansion Planning

    NARCIS (Netherlands)

    Luong, N.H.; Grond, M.O.W.; La Poutré, 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

  12. Adaptive Multi-Objective Optimization Based on Feedback Design

    Institute of Scientific and Technical Information of China (English)

    窦立谦; 宗群; 吉月辉; 曾凡琳

    2010-01-01

    The problem of adaptive multi-objective optimization(AMOO) has received extensive attention due to its practical significance.An important issue in optimizing a multi-objective system is adjusting the weighting coefficients of multiple objectives so as to keep track of various conditions.In this paper,a feedback structure for AMOO is designed.Moreover,the reinforcement learning combined with hidden biasing information is applied to online tuning weighting coefficients of objective functions.Finally,the prop...

  13. A simulated annealing technique for multi-objective simulation optimization

    OpenAIRE

    Mahmoud H. Alrefaei; Diabat, Ali H.

    2009-01-01

    In this paper, we present a simulated annealing algorithm for solving multi-objective simulation optimization problems. The algorithm is based on the idea of simulated annealing with constant temperature, and uses a rule for accepting a candidate solution that depends on the individual estimated objective function values. The algorithm is shown to converge almost surely to an optimal solution. It is applied to a multi-objective inventory problem; the numerical results show that the algorithm ...

  14. Cobweb heuristic for multi-objective vehicle routing problem

    OpenAIRE

    Joseph Okitonyumbe Y. F; Berthold Ulungu E.-L; Joel Kapiamba Nt.

    2015-01-01

    Solving a classical vehicle routing problem (VRP) by exact methods presents many difficulties for large dimension problem. Consequently, in multi-objective framework, heuristic or metaheuristic methods are required. Due to particular VRP structure, it seems that a dedicated heuristicis more suitable than a metaheuristic. The aim of this article is to collapse different heuristics solving classical VRP and adapt them for to solve the multi-objective vehicle routing problem (MOVRP). The so-call...

  15. Cobweb Heuristic for solving Multi-Objective Vehicle Routing Problem

    OpenAIRE

    Okitonyumbe Y.F., Joseph; Ulungu, Berthold E.-L.; Kapiamba Nt., Joel

    2015-01-01

    Abstract Solving a classical vehicle routing problem (VRP) by exact methods presents many difficulties for large dimension problem. Consequently, in multi-objective framework, heuristic or metaheuristic methods are required. Due to particular VRP structure, it seems that a dedicated heuristic is more suitable than a metaheuristic. The aim of this article is to collapse different heuristics solving classical VRP and adapt them for to solve the multi-objective vehicle routing problem (MOVRP)...

  16. The design of traffic signal coordinated control

    Science.gov (United States)

    Guo, Xueting; Sun, Hongsheng; Wang, Xifu

    2017-05-01

    Traffic as the tertiary industry is an important pillar industry to support the normal development of the economy. But now China's road traffic development and economic development has shown a great imbalance and fault phenomenon, which greatly inhibited the normal development of China's economy. Now in many large and medium-sized cities in China are implementing green belt construction. The so-called green band is when the road conditions to meet the conditions for the establishment of the green band, the sections of the intersection of several planning to a traffic coordination control system, so that when the driver at a specific speed can be achieved without stopping the continuous Through the intersection. Green belt can effectively reduce the delay and queuing length of vehicle driving, the normal function of urban roads and reduce the economic losses caused by traffic congestion is a great help. In this paper, the theoretical basis of the design of the coordinated control system is described. Secondly, the green time offset is calculated by the analytic method and the green band is established. And then the VISSIM software is used to simulate the traffic system before and after the improvement. Finally, the results of the two simulations are compared.

  17. 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.

  18. Solving Molecular Docking Problems with Multi-Objective Metaheuristics

    Directory of Open Access Journals (Sweden)

    María Jesús García-Godoy

    2015-06-01

    Full Text Available Molecular docking is a hard optimization problem that has been tackled in the past with metaheuristics, demonstrating new and challenging results when looking for one objective: the minimum binding energy. However, only a few papers can be found in the literature that deal with this problem by means of a multi-objective approach, and no experimental comparisons have been made in order to clarify which of them has the best overall performance. In this paper, we use and compare, for the first time, a set of representative multi-objective optimization algorithms applied to solve complex molecular docking problems. The approach followed is focused on optimizing the intermolecular and intramolecular energies as two main objectives to minimize. Specifically, these algorithms are: two variants of the non-dominated sorting genetic algorithm II (NSGA-II, speed modulation multi-objective particle swarm optimization (SMPSO, third evolution step of generalized differential evolution (GDE3, multi-objective evolutionary algorithm based on decomposition (MOEA/D and S-metric evolutionary multi-objective optimization (SMS-EMOA. We assess the performance of the algorithms by applying quality indicators intended to measure convergence and the diversity of the generated Pareto front approximations. We carry out a comparison with another reference mono-objective algorithm in the problem domain (Lamarckian genetic algorithm (LGA provided by the AutoDock tool. Furthermore, the ligand binding site and molecular interactions of computed solutions are analyzed, showing promising results for the multi-objective approaches. In addition, a case study of application for aeroplysinin-1 is performed, showing the effectiveness of our multi-objective approach in drug discovery.

  19. Solving molecular docking problems with multi-objective metaheuristics.

    Science.gov (United States)

    García-Godoy, María Jesús; López-Camacho, Esteban; García-Nieto, José; Aldana-Montes, Antonio J Nebroand José F

    2015-06-02

    Molecular docking is a hard optimization problem that has been tackled in the past with metaheuristics, demonstrating new and challenging results when looking for one objective: the minimum binding energy. However, only a few papers can be found in the literature that deal with this problem by means of a multi-objective approach, and no experimental comparisons have been made in order to clarify which of them has the best overall performance. In this paper, we use and compare, for the first time, a set of representative multi-objective optimization algorithms applied to solve complex molecular docking problems. The approach followed is focused on optimizing the intermolecular and intramolecular energies as two main objectives to minimize. Specifically, these algorithms are: two variants of the non-dominated sorting genetic algorithm II (NSGA-II), speed modulation multi-objective particle swarm optimization (SMPSO), third evolution step of generalized differential evolution (GDE3), multi-objective evolutionary algorithm based on decomposition (MOEA/D) and S-metric evolutionary multi-objective optimization (SMS-EMOA). We assess the performance of the algorithms by applying quality indicators intended to measure convergence and the diversity of the generated Pareto front approximations. We carry out a comparison with another reference mono-objective algorithm in the problem domain (Lamarckian genetic algorithm (LGA) provided by the AutoDock tool). Furthermore, the ligand binding site and molecular interactions of computed solutions are analyzed, showing promising results for the multi-objective approaches. In addition, a case study of application for aeroplysinin-1 is performed, showing the effectiveness of our multi-objective approach in drug discovery.

  20. Coordinated Control of Cross-Flow Turbines

    Science.gov (United States)

    Strom, Benjamin; Brunton, Steven; Polagye, Brian

    2016-11-01

    Cross-flow turbines, also known as vertical-axis turbines, have several advantages over axial-flow turbines for a number of applications including urban wind power, high-density arrays, and marine or fluvial currents. By controlling the angular velocity applied to the turbine as a function of angular blade position, we have demonstrated a 79 percent increase in cross-flow turbine efficiency over constant-velocity control. This strategy uses the downhill simplex method to optimize control parameter profiles during operation of a model turbine in a recirculating water flume. This optimization method is extended to a set of two turbines, where the blade motions and position of the downstream turbine are optimized to beneficially interact with the coherent structures in the wake of the upstream turbine. This control scheme has the potential to enable high-density arrays of cross-flow turbines to operate at cost-effective efficiency. Turbine wake and force measurements are analyzed for insight into the effect of a coordinated control strategy.

  1. Multi-objective optimization of aerostructures inspired by nature

    Science.gov (United States)

    Kearney, Adam C.

    The focus of this doctoral work is on the optimization of aircraft wing structures. The optimization was performed against the shape, size and topology of simple aircraft wing designs. A simple morphing wing actuator optimization is performed as well as a wing panel buckling topology optimization. This is done with biologically-inspired mathematical systems including a map L-system, a multi-objective genetic algorithm, and cellular structures represented by Voronoi diagrams. As with most aircraft optimizations, both studies aim to minimize the total weight of a wing while simultaneously meeting stiffness and strength requirements. Optimization is performed with the scripts developed in MATLAB as well as through the use of finite element codes, NASTRAN and LS-Dyna. The intent of this methodology is to develop unique designs inspired by nature and optimized through natural selection. The optimal designs are those with minimal weight as well as additional requirements specific to the problems. The designs and methodology have the potential to be of use in determining minimum weight designs in aircraft structures. A literature review of optimization techniques, methodology and method validation, and optimization comparisons is presented. The buckling panel optimization considered here also includes composite buckling failure and manufacturing assumptions for composite panels. The panels are optimized for mass and strength by controlling the laminate stacking sequence, stiffener size, and topology. The morphing wing is optimized for actuator loading and redundancy.

  2. Developer Tools for Evaluating Multi-Objective Algorithms

    Science.gov (United States)

    Giuliano, Mark E.; Johnston, Mark D.

    2011-01-01

    Multi-objective algorithms for scheduling offer many advantages over the more conventional single objective approach. By keeping user objectives separate instead of combined, more information is available to the end user to make trade-offs between competing objectives. Unlike single objective algorithms, which produce a single solution, multi-objective algorithms produce a set of solutions, called a Pareto surface, where no solution is strictly dominated by another solution for all objectives. From the end-user perspective a Pareto-surface provides a tool for reasoning about trade-offs between competing objectives. From the perspective of a software developer multi-objective algorithms provide an additional challenge. How can you tell if one multi-objective algorithm is better than another? This paper presents formal and visual tools for evaluating multi-objective algorithms and shows how the developer process of selecting an algorithm parallels the end-user process of selecting a solution for execution out of the Pareto-Surface.

  3. Modeling and Multi-objective Optimization of Refinery Hydrogen Network

    Institute of Scientific and Technical Information of China (English)

    焦云强; 苏宏业; 廖祖维; 侯卫锋

    2011-01-01

    The demand of hydrogen in oil refinery is increasing as market forces and environmental legislation, so hydrogen network management is becoming increasingly important in refineries. Most studies focused on single-objective optimization problem for the hydrogen network, but few account for the multi-objective optimization problem. This paper presents a novel approach for modeling and multi-objective optimization for hydrogen network in refineries. An improved multi-objective optimization model is proposed based on the concept of superstructure. The optimization includes minimization of operating cost and minimization of investment cost of equipment. The proposed methodology for the multi-objective optimization of hydrogen network takes into account flow rate constraints, pressure constraints, purity constraints, impurity constraints, payback period, etc. The method considers all the feasible connections and subjects this to mixed-integer nonlinear programming (MINLP). A deterministic optimization method is applied to solve this multi-objective optimization problem. Finally, a real case study is intro-duced to illustrate the applicability of the approach.

  4. The Fiber Multi-object Spectrograph (FMOS) Project: the Anglo-Australian Observatory role

    Science.gov (United States)

    Gillingham, Peter R.; Moore, Anna M.; Akiyama, Masayuki; Brzeski, Jurek; Correll, David; Dawson, John; Farrell, Tony J.; Frost, Gabriella; Griesbach, Jason S.; Haynes, Roger; Jones, Damien; Miziarski, Stan; Muller, Rolf; Smedley, Scott; Smith, Greg; Waller, Lew G.; Noakes, Katie; Arridge, Chris

    2003-03-01

    The Fiber Multi-Object Spectrograph (FMOS) project is an Australia-Japan-UK collaboration to design and build a novel 400 fiber positioner feeding two near infrared spectrographs from the prime focus of the Subaru telescope. The project comprises several parts. Those under design and construction at the Anglo-Australian Observatory (AAO) are the piezoelectric actuator driven fiber positioner (Echidna), a wide field (30 arcmin) corrector and a focal plane imager (FPI) used for controlling the positioner and for field acquisition. This paper presents an overview of the AAO share of the FMOS project. It describes the technical infrastructure required to extend the single Echidna "spine" design to a fully functioning multi-fiber instrument, capable of complete field reconfiguration in less than ten minutes. The modular Echidna system is introduced, wherein the field of view is populated by 12 identical rectangular modules, each positioning 40 science fibers and 2 guide fiber bundles. This arrangement allows maintenance by exchanging modules and minimizes the difficulties of construction. The associated electronics hardware, in itself a significant challenge, includes a 23 layer PCB board, able to supply current to each piezoelectric element in the module. The FPI is a dual purpose imaging system translating in two coordinates and is located beneath the assembled modules. The FPI measures the spine positions as well as acquiring sky images for instrument calibration and for field acquisition. An overview of the software is included.

  5. 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...

  6. A Multi-objective Model for Transmission Planning Under Uncertainties

    DEFF Research Database (Denmark)

    Zhang, Chunyu; Wang, Qi; Ding, Yi;

    2014-01-01

    The significant growth of distributed energy resources (DERs) associated with smart grid technologies has prompted excessive uncertainties in the transmission system. The most representative is the novel notation of commercial aggregator who has lighted a bright way for DERs to participate power...... trading and regulating in transmission level. In this paper, the aggregator caused uncertainty is analyzed first considering DERs’ correlation. During the transmission planning, a scenario-based multi-objective transmission planning (MOTP) framework is proposed to simultaneously optimize two objectives, i.......e. the cost of power purchase and network expansion, and the revenue of power delivery. A two-phase multi-objective PSO (MOPSO) algorithm is employed to be the solver. The feasibility of the proposed multi-objective planning approach has been verified by the 77-bus system linked with 38-bus distribution...

  7. Multi-objective quantum genetic algorithm in WSNs distribution optimization

    Science.gov (United States)

    Wen, Hao; Ren, Hong-liang

    2013-03-01

    To achieve lower energy and higher detection coverage simultaneously in scattering distribution wireless sensor networks (WSNs), a multi-objective optimization function combined with area coverage and node-communication energy is constructed. Based on the multi-objective quantum genetic algorithm (Mo-QGA) proposed by Li Bin and Zhuang-zhen Quan et al, we have obtained optimum solutions close to Pareto front. Experimental results indicate that the Mo-QGA has advantages both on efficiency and coverage, as well as low energy.

  8. MULTI OBJECTIVE ECONOMIC DISPATCH USING PARETO FRONTIER DIFFERENTIAL EVOLUTION

    Directory of Open Access Journals (Sweden)

    JAGADEESH GUNDA

    2011-10-01

    Full Text Available Multi Objective Economic dispatch (MOED problem has gained recent attention due to the deregulation of power industry and environmental regulations. So generating utilities should optimize their emission inaddition to the operating cost. In this paper a Pareto frontier Differential Evolution (PDE technique is developed to solve MOED problem, which provides a set of feasible solutions to the problem. To evaluate the performance and applicability of the proposed method, it is implemented on the standard IEEE-30 bus system having six generating units including valve point effects. The results obtained demonstrate the effectiveness of the proposed method for solving the Multi Objective economic dispatch problem considering security constraints.

  9. Entropy Diversity in Multi-Objective Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Eduardo J. Solteiro Pires

    2013-12-01

    Full Text Available Multi-objective particle swarm optimization (MOPSO is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated Pareto front for a given problem. Several approaches have been proposed to study the convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyze the MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems.

  10. Global, Multi-Objective Trajectory Optimization With Parametric Spreading

    Science.gov (United States)

    Vavrina, Matthew A.; Englander, Jacob A.; Phillips, Sean M.; Hughes, Kyle M.

    2017-01-01

    Mission design problems are often characterized by multiple, competing trajectory optimization objectives. Recent multi-objective trajectory optimization formulations enable generation of globally-optimal, Pareto solutions via a multi-objective genetic algorithm. A byproduct of these formulations is that clustering in design space can occur in evolving the population towards the Pareto front. This clustering can be a drawback, however, if parametric evaluations of design variables are desired. This effort addresses clustering by incorporating operators that encourage a uniform spread over specified design variables while maintaining Pareto front representation. The algorithm is demonstrated on a Neptune orbiter mission, and enhanced multidimensional visualization strategies are presented.

  11. Joint Geophysical Inversion With Multi-Objective Global Optimization Methods

    Science.gov (United States)

    Lelievre, P. G.; Bijani, R.; Farquharson, C. G.

    2015-12-01

    Pareto multi-objective global optimization (PMOGO) methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. Providing a suite of models, as opposed to a single model that minimizes a weighted sum of objectives, allows a more complete assessment of the possibilities and avoids the often difficult choice of how to weight each objective. We are applying PMOGO methods to three classes of inverse problems. The first class are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The second class of problems are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the third class we consider a fundamentally different type of inversion in which a model comprises wireframe surfaces representing contacts between rock units; the physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. This third class of problem is essentially a geometry inversion, which can be used to recover the unknown geometry of a target body or to investigate the viability of a proposed Earth model. Joint inversion is greatly simplified for the latter two problem classes because no additional mathematical coupling measure is required in the objective function. PMOGO methods can solve numerically complicated problems that could not be solved with standard descent-based local minimization methods. This includes the latter two classes of problems mentioned above. There are significant increases in the computational requirements when PMOGO methods are used but these can be ameliorated using parallelization and problem dimension reduction strategies.

  12. Multi-objective nested algorithms for optimal reservoir operation

    Science.gov (United States)

    Delipetrev, Blagoj; Solomatine, Dimitri

    2016-04-01

    The optimal reservoir operation is in general a multi-objective problem, meaning that multiple objectives are to be considered at the same time. For solving multi-objective optimization problems there exist a large number of optimization algorithms - which result in a generation of a Pareto set of optimal solutions (typically containing a large number of them), or more precisely, its approximation. At the same time, due to the complexity and computational costs of solving full-fledge multi-objective optimization problems some authors use a simplified approach which is generically called "scalarization". Scalarization transforms the multi-objective optimization problem to a single-objective optimization problem (or several of them), for example by (a) single objective aggregated weighted functions, or (b) formulating some objectives as constraints. We are using the approach (a). A user can decide how many multi-objective single search solutions will generate, depending on the practical problem at hand and by choosing a particular number of the weight vectors that are used to weigh the objectives. It is not guaranteed that these solutions are Pareto optimal, but they can be treated as a reasonably good and practically useful approximation of a Pareto set, albeit small. It has to be mentioned that the weighted-sum approach has its known shortcomings because the linear scalar weights will fail to find Pareto-optimal policies that lie in the concave region of the Pareto front. In this context the considered approach is implemented as follows: there are m sets of weights {w1i, …wni} (i starts from 1 to m), and n objectives applied to single objective aggregated weighted sum functions of nested dynamic programming (nDP), nested stochastic dynamic programming (nSDP) and nested reinforcement learning (nRL). By employing the multi-objective optimization by a sequence of single-objective optimization searches approach, these algorithms acquire the multi-objective properties

  13. 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

  14. 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...

  15. Progress on multi-object exoplanet search spectral interferometer

    Science.gov (United States)

    Zhang, Kai; Zhu, Yongtian; Wang, Lei; Yue, Zhongyu; Chen, Yi; Tang, Jin; Hu, Zhongwen

    2012-09-01

    It's a very important point that fully open up power of Gou Shoujing telescope (LAMOST) in exoplanet detection field by developing a multi-exoplanet survey system. But it's an indisputable truth in the present astronomy that a traditional type of multi-object high resolution spectrograph is almost impossible to be developed. External Dispersed Interferometry is an effective way to improve the radial velocity measuring accuracy of medium resolution spectrograph. With the using of this technique, Multi-object Exoplanet Search Spectral Interferometer (MESSI) is an exploratory system with medium measuring accuracy based on LAMOST low resolution spectrograph works in medium-resolution mode (R=5,000 - 10,000). And it's believed that will bring some feasible way in the future development of multi-object medium/high resolution spectrograph. After prototype experiment in 2010, a complete configuration is under the development, including a multi-object fixed-delay Michelson interferometer, an iodine cell with multi-fiber optical coupling system and a multi-terminal switching system in an efficient fiber physical coupling way. By some effective improvement, the interferometer has smaller cross section and more stable interference component. Moreover, based on physical and optical fiber coupling technique, it's possible for the iodine cell and the switching system to simultaneously and identically coupling 25 pairs of fibers. In paper, all of the progress is given in detail.

  16. Navigation Constellation Design Using a Multi-Objective Genetic Algorithm

    Science.gov (United States)

    2015-03-26

    the mutation and crossover functions specified that certain design parameters be integer values [17]. Equation 21 represents the variables that...been used to force certain design variables to be integer values. Understanding the MATLAB code for the mutation and crossover functions is not...NAVIGATION CONSTELLATION DESIGN USING A MULTI-OBJECTIVE GENETIC ALGORITHM THESIS MARCH 2015

  17. Multi-Objective Constraint Satisfaction for Mobile Robot Area Defense

    Science.gov (United States)

    2010-03-01

    Dorronsoro, and En- rique Alba. jMetal: A Java Framework for Developing Multi-Objective Optimiza- tion Metaheuristics . Technical Report ITI-2006-10...32 3.1 Framework Development...17 NSGA-II non-dominated sorting genetic algorithm II . . . . . . . . . . . . . . . . . . . 17 jMetal Metaheuristic Algorithms in

  18. Analysing the performance of dynamic multi-objective optimisation algorithms

    CSIR Research Space (South Africa)

    Helbig, M

    2013-06-01

    Full Text Available Congress on Evolutionary Computation, 20-23 June 2013, Cancún, México Analysing the Performance of Dynamic Multi-objective Optimisation Algorithms Marde Helbig CSIR: Meraka Institute, Brummeria, South Africa; and University of Pretoria Computer...

  19. Genetic Tabu Search for the Multi-Objective Knapsack Problem

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    We introduce a hybrid algorithm for the 0-1 multidimensional multi-objective knapsack problem. This algorithm, called GTSMOKP, combines a genetic procedure and a tabu search operator. The algorithm is evaluated on 9 well-known benchmark instances and shows highly competitive results compared with two state-of-the-art algorithms.

  20. 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.

  1. Improving multi-objective reservoir operation optimization with sensitivity-informed problem decomposition

    Science.gov (United States)

    Chu, J. G.; Zhang, C.; Fu, G. T.; Li, Y.; Zhou, H. C.

    2015-04-01

    This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce the computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed problem decomposition dramatically reduces the computational demands required for attaining high quality approximations of optimal tradeoff relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed problem decomposition and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform problem decomposition when solving the complex multi-objective reservoir operation problems.

  2. Improving multi-objective reservoir operation optimization with sensitivity-informed dimension reduction

    Science.gov (United States)

    Chu, J.; Zhang, C.; Fu, G.; Li, Y.; Zhou, H.

    2015-08-01

    This study investigates the effectiveness of a sensitivity-informed method for multi-objective operation of reservoir systems, which uses global sensitivity analysis as a screening tool to reduce computational demands. Sobol's method is used to screen insensitive decision variables and guide the formulation of the optimization problems with a significantly reduced number of decision variables. This sensitivity-informed method dramatically reduces the computational demands required for attaining high-quality approximations of optimal trade-off relationships between conflicting design objectives. The search results obtained from the reduced complexity multi-objective reservoir operation problems are then used to pre-condition the full search of the original optimization problem. In two case studies, the Dahuofang reservoir and the inter-basin multi-reservoir system in Liaoning province, China, sensitivity analysis results show that reservoir performance is strongly controlled by a small proportion of decision variables. Sensitivity-informed dimension reduction and pre-conditioning are evaluated in their ability to improve the efficiency and effectiveness of multi-objective evolutionary optimization. Overall, this study illustrates the efficiency and effectiveness of the sensitivity-informed method and the use of global sensitivity analysis to inform dimension reduction of optimization problems when solving complex multi-objective reservoir operation problems.

  3. Multi-objective radiomics model for predicting distant failure in lung SBRT

    Science.gov (United States)

    Zhou, Zhiguo; Folkert, Michael; Iyengar, Puneeth; Westover, Kenneth; Zhang, Yuanyuan; Choy, Hak; Timmerman, Robert; Jiang, Steve; Wang, Jing

    2017-06-01

    Stereotactic body radiation therapy (SBRT) has demonstrated high local control rates in early stage non-small cell lung cancer patients who are not ideal surgical candidates. However, distant failure after SBRT is still common. For patients at high risk of early distant failure after SBRT treatment, additional systemic therapy may reduce the risk of distant relapse and improve overall survival. Therefore, a strategy that can correctly stratify patients at high risk of failure is needed. The field of radiomics holds great potential in predicting treatment outcomes by using high-throughput extraction of quantitative imaging features. The construction of predictive models in radiomics is typically based on a single objective such as overall accuracy or the area under the curve (AUC). However, because of imbalanced positive and negative events in the training datasets, a single objective may not be ideal to guide model construction. To overcome these limitations, we propose a multi-objective radiomics model that simultaneously considers sensitivity and specificity as objective functions. To design a more accurate and reliable model, an iterative multi-objective immune algorithm (IMIA) was proposed to optimize these objective functions. The multi-objective radiomics model is more sensitive than the single-objective model, while maintaining the same levels of specificity and AUC. The IMIA performs better than the traditional immune-inspired multi-objective algorithm.

  4. Development of the Coordination between Posture and Manual Control

    Science.gov (United States)

    Haddad, Jeffrey M.; Claxton, Laura J.; Keen, Rachel; Berthier, Neil E.; Riccio, Gary E.; Hamill, Joseph; Van Emmerik, Richard E. A.

    2012-01-01

    Studies have suggested that proper postural control is essential for the development of reaching. However, little research has examined the development of the coordination between posture and manual control throughout childhood. We investigated the coordination between posture and manual control in children (7- and 10-year-olds) and adults during…

  5. Multi-objective optimal design of active vibration absorber with delayed feedback

    Science.gov (United States)

    Huan, Rong-Hua; Chen, Long-Xiang; Sun, Jian-Qiao

    2015-03-01

    In this paper, a multi-objective optimal design of delayed feedback control of an actively tuned vibration absorber for a stochastically excited linear structure is investigated. The simple cell mapping (SCM) method is used to obtain solutions of the multi-objective optimization problem (MOP). The continuous time approximation (CTA) method is applied to analyze the delayed system. Stability is imposed as a constraint for MOP. Three conflicting objective functions including the peak frequency response, vibration energy of primary structure and control effort are considered. The Pareto set and Pareto front for the optimal feedback control design are presented for two examples. Numerical results have found that the Pareto optimal solutions provide effective delayed feedback control design.

  6. 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...... of steady-state of power systems. Specifically, reactive power control is formulated as a multi-objective optimization problem and solved using the parallel VEPSO algorithm. The results on the IEEE 30-bus test system are compared with those given by another multi-objective evolutionary technique...... demonstrating the advantage of parallel VEPSO. The parallel VEPSO is also tested on a larger power system this with 136 busses. (C) 2009 Elsevier Ltd. All rights reserved....

  7. Coordinating control of multiple rigid bodies based on motion primitives

    Institute of Scientific and Technical Information of China (English)

    Fan Wu; Zhi-Yong Geng

    2012-01-01

    This paper studies the problem of coordinated motion generation for a group of rigid bodies.Two classes of coordinated motion primitives,relative equilibria and maneuvers,are given as building blocks for generating coordinated motions.In a motion-primitive based planning framework,a control method is proposed for the robust execution of a coordinated motion plan in the presence of perturbations,The control method combines the relative equilibria stabilization with maneuver design,and results in a closeloop motion planning framework.The performance of the control method has been illustrated through a numerical simulation.

  8. 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.

  9. Geophysical Inversion With Multi-Objective Global Optimization Methods

    Science.gov (United States)

    Lelièvre, Peter; Bijani, Rodrigo; Farquharson, Colin

    2016-04-01

    We are investigating the use of Pareto multi-objective global optimization (PMOGO) methods to solve numerically complicated geophysical inverse problems. PMOGO methods can be applied to highly nonlinear inverse problems, to those where derivatives are discontinuous or simply not obtainable, and to those were multiple minima exist in the problem space. PMOGO methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. This allows a more complete assessment of the possibilities and provides opportunities to calculate statistics regarding the likelihood of particular model features. We are applying PMOGO methods to four classes of inverse problems. The first are discrete-body problems where the inversion determines values of several parameters that define the location, orientation, size and physical properties of an anomalous body represented by a simple shape, for example a sphere, ellipsoid, cylinder or cuboid. A PMOGO approach can determine not only the optimal shape parameters for the anomalous body but also the optimal shape itself. Furthermore, when one expects several anomalous bodies in the subsurface, a PMOGO inversion approach can determine an optimal number of parameterized bodies. The second class of inverse problems are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The third class of problems are lithological inversions, which are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the fourth class, surface geometry inversions, we consider a fundamentally different type of problem in which a model comprises wireframe surfaces representing contacts between rock units. The physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. Surface geometry inversion can be

  10. 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 ...

  11. Evidence of coevolution in multi-objective evolutionary algorithms

    CERN Document Server

    Whitacre, James M

    2009-01-01

    This paper demonstrates that simple yet important characteristics of coevolution can occur in evolutionary algorithms when only a few conditions are met. We find that interaction-based fitness measurements such as fitness (linear) ranking allow for a form of coevolutionary dynamics that is observed when 1) changes are made in what solutions are able to interact during the ranking process and 2) evolution takes place in a multi-objective environment. This research contributes to the study of simulated evolution in a at least two ways. First, it establishes a broader relationship between coevolution and multi-objective optimization than has been previously considered in the literature. Second, it demonstrates that the preconditions for coevolutionary behavior are weaker than previously thought. In particular, our model indicates that direct cooperation or competition between species is not required for coevolution to take place. Moreover, our experiments provide evidence that environmental perturbations can dri...

  12. Multi-Objective Simulating Annealing for Permutation Flow Shop Problems

    Science.gov (United States)

    Mokotoff, E.; Pérez, J.

    2007-09-01

    Real life scheduling problems require more than one criterion. Nevertheless, the complex nature of the Permutation Flow Shop problem has prevented the development of models with multiple criteria. Considering only one regular criterion, this scheduling problem was shown to be NP-complete. The Multi-Objective Simulated Annealing (MOSA) methods are metaheuristics based on Simulated Annealing to solve Multi-Objective Combinatorial Optimization (MOCO) problems, like the problem at hand. Starting from the general MOSA method introduced by Loukil et al. [1], we developed MOSA models to provide the decision maker with efficient solutions for the Permutation Flow Shop problem (common in the production of ceramic tiles). In this paper we present three models: two bicriteria models and one based on satisfaction levels for the main criterion.

  13. Multi-objective optimization of an axial compressor blade

    Energy Technology Data Exchange (ETDEWEB)

    Samad, Abdus; Kim, Kwang Yong [Inha University, Incheon (Korea, Republic of)

    2008-05-15

    Numerical optimization with multiple objectives is carried out for design of an axial compressor blade. Two conflicting objectives, total pressure ratio and adiabatic efficiency, are optimized with three design variables concerning sweep, lean and skew of blade stacking line. Single objective optimizations have been also performed. At the data points generated by D-optimal design, the objectives are calculated by three-dimensional Reynolds-averaged Navier-Stokes analysis. A second-order polynomial based response surface model is generated, and the optimal point is searched by sequential quadratic programming method for single objective optimization. Elitist non-dominated sorting of genetic algorithm (NSGA-II) with {epsilon}-constraint local search strategy is used for multi-objective optimization. Both objective function values are found to be improved as compared to the reference one by multi-objective optimization. The flow analysis results show the mechanism for the improvement of blade performance

  14. A New RWA Algorithm Based on Multi-Objective

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    In this article, we studied the associated research problems and challenges on routing and wavelength assignment (RWA) in WDM (wavelength division multiplexing) networks. Various RWA approaches are examined and compared. We proposed a new RWA algorithm based on multi-objective. In this new algorithm, we consider multiple network optimizing objectives to setup a lightpath with maximize profit and shortest path under the limited resources. By comparing and analyzing, the proposed algorithm is much better ...

  15. OSIRIS Multi-Object Spectroscopy: Mask Design Process

    Science.gov (United States)

    Gómez-Velarde, G.; García-Alvarez, D.; Cabrerra-Lavers, A.

    2016-10-01

    The OSIRIS (Optical System for Imaging and Low-Intermediate Resolution Integrated Spectroscopy) instrument at the 10.4 m GTC has offered a multi-object spectroscopic mode since March 2014. In this paper we describe the detailed process of designing a MOS mask for OSIRIS by using the Mask Designer Tool, and give some numbers on the accuracy of the mask manufacture achievable at the telescope for its scientific use.

  16. Appendage modal coordinate truncation criteria in hybrid coordinate dynamic analysis. [for spacecraft attitude control

    Science.gov (United States)

    Likins, P.; Ohkami, Y.; Wong, C.

    1976-01-01

    The paper examines the validity of the assumption that certain appendage-distributed (modal) coordinates can be truncated from a system model without unacceptable degradation of fidelity in hybrid coordinate dynamic analysis for attitude control of spacecraft with flexible appendages. Alternative truncation criteria are proposed and their interrelationships defined. Particular attention is given to truncation criteria based on eigenvalues, eigenvectors, and controllability and observability. No definitive resolution of the problem is advanced, and exhaustive study is required to obtain ultimate truncation criteria.

  17. Enhanced Multi-Objective Energy Optimization by a Signaling Method

    Directory of Open Access Journals (Sweden)

    João Soares

    2016-10-01

    Full Text Available 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-dimensional signaling is also compared with this technique, which has previously been shown to boost metaheuristics performance for single-objective problems. Hence, multi-dimensional signaling is adapted and implemented here for the proposed multi-objective problem. In addition, parallel computing is used to mitigate the methods’ computational execution time. To validate the proposed techniques, a realistic case study for a chosen area of the northern region of Portugal is considered, namely part of Vila Real distribution grid (233-bus. It is assumed that this grid is managed by an energy aggregator entity, with reasonable amount of electric vehicles (EVs, several distributed generation (DG, customers with demand response (DR contracts and energy storage systems (ESS. The considered case study characteristics took into account several reported research works with projections for 2020 and 2050. The findings strongly suggest that the signaling method clearly improves the results and the Pareto front region quality.

  18. Replication in Overlay Networks: A Multi-objective Optimization Approach

    Science.gov (United States)

    Al-Haj Hassan, Osama; Ramaswamy, Lakshmish; Miller, John; Rasheed, Khaled; Canfield, E. Rodney

    Recently, overlay network-based collaborative applications such as instant messaging, content sharing, and Internet telephony are becoming increasingly popular. Many of these applications rely upon data-replication to achieve better performance, scalability, and reliability. However, replication entails various costs such as storage for holding replicas and communication overheads for ensuring replica consistency. While simple rule-of-thumb strategies are popular for managing the cost-benefit tradeoffs of replication, they cannot ensure optimal resource utilization. This paper explores a multi-objective optimization approach for replica management, which is unique in the sense that we view the various factors influencing replication decisions such as access latency, storage costs, and data availability as objectives, and not as constraints. This enables us to search for solutions that yield close to optimal values for these parameters. We propose two novel algorithms, namely multi-objective Evolutionary (MOE) algorithm and multi-objective Randomized Greedy (MORG) algorithm for deciding the number of replicas as well as their placement within the overlay. While MOE yields higher quality solutions, MORG is better in terms of computational efficiency. The paper reports a series of experiments that demonstrate the effectiveness of the proposed algorithms.

  19. A Bayesian Alternative for Multi-objective Ecohydrological Model Specification

    Science.gov (United States)

    Tang, Y.; Marshall, L. A.; Sharma, A.; Ajami, H.

    2015-12-01

    Process-based ecohydrological models combine the study of hydrological, physical, biogeochemical and ecological processes of the catchments, which are usually 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. Our study aims to develop appropriate prior distributions and likelihood functions that minimize the model uncertainties and bias within a Bayesian ecohydrological framework. In our study, a formal Bayesian approach is implemented in an ecohydrological model which combines a hydrological model (HyMOD) and a dynamic vegetation model (DVM). Simulations focused on one objective likelihood (Streamflow/LAI) and multi-objective likelihoods (Streamflow and LAI) with different weights are compared. Uniform, weakly informative and strongly informative prior distributions are used in different simulations. The Kullback-leibler divergence (KLD) is used to measure the dis(similarity) between different priors and corresponding posterior distributions to examine the parameter sensitivity. Results show that different prior distributions can strongly influence posterior distributions for parameters, especially when the available data is limited or parameters are insensitive to the available data. We demonstrate differences in optimized parameters and uncertainty limits in different cases based on multi-objective likelihoods vs. single objective likelihoods. We also demonstrate the importance of appropriately defining the weights of objectives in multi-objective calibration according to different data types.

  20. Research on Multi-objective Optimal Parameters Design of Aircraft Flight Control System%飞机飞行控制系统参数多目标优化设计研究

    Institute of Scientific and Technical Information of China (English)

    白俊杰; 张坤; 崔彦勇

    2014-01-01

    In the traditional optimization design of flight control system (FCS),there are some disadvanta-ges such as weak correlation between the single object and the flight quality requirements , ambiguous physical meaning and difficulty of using single object to optimize many objects at the same time .To solve such problem ,an improved particle swarm optimization ( PSO) algorithm was proposed .By simulating the foraging aggregation behavior of birds ,the particles can be divided into several dynamic sub-swarms with respect to the finding and expanding of forage in the improved PSO algorithm .So that ,the diversity of par-ticles can be maintained by this method , thus can restrain local optimum phenomena .Finally , using the improved PSO algorithm for numerical simulation of a certain type of aircraft longitudinal control law ,the results show that the proposed algorithm can effectively improve the efficiency of the FCS parameters tun-ing,and the results can meet the flight qualities requirements .%针对传统飞行控制律参数单目标优化设计不能同时满足多控制指标要求,且与飞行品质要求缺乏相关性,物理意义不明确等缺点,提出了一种基于改进粒子群算法的飞行控制律多目标优化设计方法。算法模拟鸟类捕食过程,使得种群随着“食物”的发现和消耗,聚集为数量和构成动态调整多个子群,且子群粒子速度也随之进行自适应变异,从而有利于维持种群的多样性,有效抑制早熟收敛现象发生。最后,使用改进的粒子群优化算法对某型飞机纵向控制律设计进行数值仿真,结果显示,算法有效提高控制律优化调参效率,结果满足期望的飞行品质要求。

  1. Multi-object segmentation framework using deformable models for medical imaging analysis.

    Science.gov (United States)

    Namías, Rafael; D'Amato, Juan Pablo; Del Fresno, Mariana; Vénere, Marcelo; Pirró, Nicola; Bellemare, Marc-Emmanuel

    2016-08-01

    Segmenting structures of interest in medical images is an important step in different tasks such as visualization, quantitative analysis, simulation, and image-guided surgery, among several other clinical applications. Numerous segmentation methods have been developed in the past three decades for extraction of anatomical or functional structures on medical imaging. Deformable models, which include the active contour models or snakes, are among the most popular methods for image segmentation combining several desirable features such as inherent connectivity and smoothness. Even though different approaches have been proposed and significant work has been dedicated to the improvement of such algorithms, there are still challenging research directions as the simultaneous extraction of multiple objects and the integration of individual techniques. This paper presents a novel open-source framework called deformable model array (DMA) for the segmentation of multiple and complex structures of interest in different imaging modalities. While most active contour algorithms can extract one region at a time, DMA allows integrating several deformable models to deal with multiple segmentation scenarios. Moreover, it is possible to consider any existing explicit deformable model formulation and even to incorporate new active contour methods, allowing to select a suitable combination in different conditions. The framework also introduces a control module that coordinates the cooperative evolution of the snakes and is able to solve interaction issues toward the segmentation goal. Thus, DMA can implement complex object and multi-object segmentations in both 2D and 3D using the contextual information derived from the model interaction. These are important features for several medical image analysis tasks in which different but related objects need to be simultaneously extracted. Experimental results on both computed tomography and magnetic resonance imaging show that the proposed

  2. Bus coordination holding control for transit hubs under APTS

    Institute of Scientific and Technical Information of China (English)

    TENG Jing; ZHAO Ming

    2009-01-01

    To increase the passenger transferring efficiency, the bus coordination holding control for transit hubs,hich is as an important dynamic dispatching method for improving the service level of transit hubs, was studied in the framework of bus coordination dispatching mode. Firstly, the bus coordination holding control flow was studied based on Advanced Public Transportation Systems (APTS) environment. Then a control model was presented to optimize the bus vehicle holding time, and a genetic algorithm was designed as the solving method. In the end, an example was given to illustrate the effectiveness of the control strategy and the algorithm.

  3. Artificial emotion triggered stochastic behavior transitions with motivational gain effects for multi-objective robot tasks

    Science.gov (United States)

    Dağlarli, Evren; Temeltaş, Hakan

    2007-04-01

    This paper presents artificial emotional system based autonomous robot control architecture. Hidden Markov model developed as mathematical background for stochastic emotional and behavior transitions. Motivation module of architecture considered as behavioral gain effect generator for achieving multi-objective robot tasks. According to emotional and behavioral state transition probabilities, artificial emotions determine sequences of behaviors. Also motivational gain effects of proposed architecture can be observed on the executing behaviors during simulation.

  4. Topochemical control in desolvation of coordination polymers

    OpenAIRE

    Matteo Lusi

    2015-01-01

    Reactions in the solid state are at the core of crystal engineering as they can result in new crystalline phases that are not always accessible by traditional solution methods. The work of Brammer and co-workers [Wright et al. (2015), IUCrJ, 2, 188–197] represents a clear example of this potential as applied to the synthesis of a silver–phenazine coordination polymer.

  5. A Coordinated LVRT Control for a PMSG Wind Turbine

    DEFF Research Database (Denmark)

    Kim, Chunghun; Gui, Yonghao; Chung, Chung Choo

    2017-01-01

    This paper proposes a coordinated controller for a permanent-magnet synchronous generator wind turbine to enhance its low voltage ride through capability. In the proposed method, both rotor side and grid side converters are cooperatively controlled to regulate the DC link voltage during the grid ...... of the DC link voltage could be obtained with less rotor acceleration. We validated the proposed method using MATLAB/Simulink SimPowerSystems and compared the performances of with and without the coordinated control....

  6. Development of the Coordination between Posture and Manual Control

    OpenAIRE

    Haddad, Jeffrey M.; Claxton, Laura J.; Keen, Rachel; Berthier, Neil; Riccio, Gary E.; Hamill, Joseph; Van Emmerik, Richard

    2011-01-01

    Studies have suggested that proper postural control is essential for the development of reaching. However, little research has examined the development of the coordination between posture and manual control throughout childhood. We investigated the coordination between posture and manual control in 7- and 10- year-old children, and adults during a precision fitting task as task constraints became more difficult. Participants fit a block through an opening as arm kinematics, trunk kinematics a...

  7. Methods for Coordinated Inventory Control in Supply Chain Management

    DEFF Research Database (Denmark)

    Larsen, Christian; Thorstenson, Anders

    2010-01-01

    and heuristic control methods for coordination. The numerical results obtained by simulation are compared with the solutions found when inventories in the supply chain are controlled independently of each other. Findings Coordinated inventory control can offer a significant potential for cost reduction...... in a supply chain. However, the resulting inventory allocations are not always obvious without thorough analyses of the coordination effects. Research limitations/implications Some of the conclusions are formed on the basis of numerical examples and future research could involve investigation of a wider set...

  8. Multi-objective community detection based on memetic algorithm.

    Directory of Open Access Journals (Sweden)

    Peng Wu

    Full Text Available Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

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

    Science.gov (United States)

    Kudryashov, Nikolay A.; Shilnikov, Kirill E.

    2016-06-01

    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.

  10. 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.

  11. 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.

  12. Multi-objective optimization of acoustic black hole vibration absorbers.

    Science.gov (United States)

    Shepherd, Micah R; Feurtado, Philip A; Conlon, Stephen C

    2016-09-01

    Structures with power law tapers exhibit the acoustic black hole (ABH) effect and can be used for vibration reduction. However, the design of ABHs for vibration reduction requires consideration of the underlying theory and its regions of validity. To address the competing nature of the best ABH design for vibration reduction and the underlying theoretical assumptions, a multi-objective approach is used to find the lowest frequency where both criteria are sufficiently met. The Pareto optimality curve is estimated for a range of ABH design parameters. The optimal set could then be used to implement an ABH vibration absorber.

  13. Uncertain multi-objective multi-product solid transportation problems

    Indian Academy of Sciences (India)

    DEEPIKA RANI; T R GULATI

    2016-05-01

    The solid transportation problem is an important generalization of the classical transportation problem as it also considers the conveyance constraints along with the source and destination constraints. The problem can be made more effective by incorporating some other factors, which make it useful in real lifesituations. In this paper, we consider a fully fuzzy multi-objective multi-item solid transportation problem and present a method to find its fuzzy optimal-compromise solution using the fuzzy programming technique. To take into account the imprecision in finding the exact values of parameters, all the parameters are taken as trapezoidal fuzzy numbers. A numerical example is solved to illustrate the methodology

  14. Multi-objective Optimization on Helium Liquefier Using Genetic Algorithm

    Science.gov (United States)

    Wang, H. R.; Xiong, L. Y.; Peng, N.; Meng, Y. R.; Liu, L. Q.

    2017-02-01

    Research on optimization of helium liquefier is limited at home and abroad, and most of the optimization is single-objective based on Collins cycle. In this paper, a multi-objective optimization is conducted using genetic algorithm (GA) on the 40 L/h helium liquefier developed by Technical Institute of Physics and Chemistry of the Chinese Academy of Science (TIPC, CAS), steady solutions are obtained in the end. In addition, the exergy loss of the optimized system is studied in the case of with and without liquid nitrogen pre-cooling. The results have guiding significance for the future design of large helium liquefier.

  15. METHOD OF CENTERS ALGORITHM FOR MULTI-OBJECTIVE PROGRAMMING PROBLEMS

    Institute of Scientific and Technical Information of China (English)

    Tarek Emam

    2009-01-01

    In this paper, we consider a method of centers for solving multi-objective programming problems, where the objective functions involved are concave functions and the set of feasible points is convex. The algorithm is defined so that the sub-problems that must be solved during its execution may be solved by finite-step procedures. Conditions are given under which the algorithm generates sequences of feasible points and constraint multiplier vectors that have accumulation points satisfying the KKT conditions. Finally, we establish convergence of the proposed method of centers algorithm for solving multiobjective programming problems.

  16. New results from the multi-object Keck Exoplanet Tracker

    Directory of Open Access Journals (Sweden)

    J. C. van Eyken

    2007-01-01

    Full Text Available The W. M. Keck Exoplanet Tracker is a pre- cision Doppler radial velocity instrument for extrasolar planet detection based on a new technique, dispersed fixed-delay interferome- try (DFDI, which allows for multi-object sur- veying for the first time. Installed at the 2.5- m Sloan telescope at Apache Point Observa- tory, the combination of Michelson interfer- ometer and medium resolution spectrograph (Erskine & Ge 2000; Ge 2002 allows design for simultaneous Doppler measurements of 60 targets (Ge et al. 2005.

  17. Dynamic Cell Formation based on Multi-objective Optimization Model

    Directory of Open Access Journals (Sweden)

    Guozhu Jia

    2013-08-01

    Full Text Available In this paper, a multi-objective model is proposed to address the dynamic cellular manufacturing (DCM formation problem. This model considers four conflicting objectives: relocation cost, machine utilization, material handling cost and maintenance cost. The model also considers the situation that some machines could be shared by more than one cell at the same period. A genetic algorithm is applied to get the solution of this mathematical model. Three numerical examples are simulated to evaluate the validity of this model.  

  18. 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.

  19. 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.

  20. Estimation of subsurface geomodels by multi-objective stochastic optimization

    Science.gov (United States)

    Emami Niri, Mohammad; Lumley, David E.

    2016-06-01

    We present a new method to estimate subsurface geomodels using a multi-objective stochastic search technique that allows a variety of direct and indirect measurements to simultaneously constrain the earth model. Inherent uncertainties and noise in real data measurements may result in conflicting geological and geophysical datasets for a given area; a realistic earth model can then only be produced by combining the datasets in a defined optimal manner. One approach to solving this problem is by joint inversion of the various geological and/or geophysical datasets, and estimating an optimal model by optimizing a weighted linear combination of several separate objective functions which compare simulated and observed datasets. In the present work, we consider the joint inversion of multiple datasets for geomodel estimation, as a multi-objective optimization problem in which separate objective functions for each subset of the observed data are defined, followed by an unweighted simultaneous stochastic optimization to find the set of best compromise model solutions that fits the defined objectives, along the so-called "Pareto front". We demonstrate that geostatistically constrained initializations of the algorithm improves convergence speed and produces superior geomodel solutions. We apply our method to a 3D reservoir lithofacies model estimation problem which is constrained by a set of geological and geophysical data measurements and attributes, and assess the sensitivity of the resulting geomodels to changes in the parameters of the stochastic optimization algorithm and the presence of realistic seismic noise conditions.

  1. 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.

  2. Evolutionary Multi-objective Portfolio Optimization in Practical Context

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    This paper addresses evolutionary multi-objective portfolio optimization in the practical context by incorporating realistic constraints into the problem model and preference criterion into the optimization search process. The former is essential to enhance the realism of the classical mean-variance model proposed by Harry Markowitz, since portfolio managers often face a number of realistic constraints arising from business and industry regulations, while the latter reflects the fact that portfolio managers are ultimately interested in specific regions or points along the efficient frontier during the actual execution of their investment orders. For the former,this paper proposes an order-based representation that can be easily extended to handle various realistic constraints like floor and ceiling constraints and cardinality constraint. An experimental study, based on benchmark problems obtained from the OR-library,demonstrates its capability to attain a better approximation of the efficient frontier in terms of proximity and diversity with respect to other conventional representations. The experimental results also illustrated its viability and practicality in handling the various realistic constraints. A simple strategy to incorporate preferences into the multi-objective optimization process is highlighted and the experimental study demonstrates its capability in driving the evolutionary search towards specific regions of the efficient frontier.

  3. 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.

  4. Coordinated Control of Wave Energy Converters Subject to Motion Constraints

    OpenAIRE

    2016-01-01

    In this paper, a generic coordinated control method for wave energy converters is proposed, and the constraints on motion amplitudes and the hydrodynamic interaction between converters are considered. The objective of the control problem is to maximize the energy converted from ocean waves, and this is achieved by coordinating the power take-off (PTO) damping of each wave energy converter in the frequency domain in each sea state. In a case study, a wave energy farm consisting of four convert...

  5. Multi-Objective Configuration Optimization of a Hybrid Energy Storage System

    Directory of Open Access Journals (Sweden)

    Shan Cheng

    2017-02-01

    Full Text Available This study aims to investigate multi-objective configuration optimization of a hybrid energy storage system (HESS. In order to maximize the stability of the wind power output with minimized HESS investment, a multi-objective model for optimal HESS configuration has been established, which proposes decreasing the installation and operation & maintenance costs of an HESS and improving the compensation satisfaction rate of wind power fluctuation. Besides, fuzzy control has been used to allocate power in the HESS for lengthening battery lifetime and ensuring HESS with enough energy to compensate the fluctuation of the next time interval. Instead of converting multiple objectives into one, a multi-objective particle swarm optimization with integration of bacteria quorum sensing and circular elimination (BC-MOPSO has been applied to provide diverse alternative solutions. In order to illustrate the feasibility and effectiveness of the proposed model and the application of BC-MOPSO, simulations along with analysis and discussion are carried out. The results verified the feasibility and effectiveness of the proposed approach.

  6. DNA strand generation for DNA computing by using a multi-objective differential evolution algorithm.

    Science.gov (United States)

    Chaves-González, José M; Vega-Rodríguez, Miguel A

    2014-02-01

    In this paper, we use an adapted multi-objective version of the differential evolution (DE) metaheuristics for the design and generation of reliable DNA libraries that can be used for computation. DNA sequence design is a very relevant task in many recent research fields, e.g. nanotechnology or DNA computing. Specifically, DNA computing is a new computational model which uses DNA molecules as information storage and their possible biological interactions as processing operators. Therefore, the possible reactions and interactions among molecules must be strictly controlled to prevent incorrect computations. The design of reliable DNA libraries for bio-molecular computing is an NP-hard combinatorial problem which involves many heterogeneous and conflicting design criteria. For this reason, we modelled DNA sequence design as a multiobjective optimization problem and we solved it by using an adapted multi-objective version of DE metaheuristics. Seven different bio-chemical design criteria have been simultaneously considered to obtain high quality DNA sequences which are suitable for molecular computing. Furthermore, we have developed the multiobjective standard fast non-dominated sorting genetic algorithm (NSGA-II) in order to perform a formal comparative study by using multi-objective indicators. Additionally, we have also compared our results with other relevant results published in the literature. We conclude that our proposal is a promising approach which is able to generate reliable real-world DNA sequences that significantly improve other DNA libraries previously published in the literature.

  7. Motion Planning Based Coordinated Control for Hydraulic Excavators

    Institute of Scientific and Technical Information of China (English)

    GAO Yingjie; JIN Yanchao; ZHANG Qin

    2009-01-01

    Hydraulic excavator is one type of the most widely applied construction equipment for various applications mainly because of its versatility and mobility. Among the tasks performed by a hydraulic excavator, repeatable level digging or flat surface finishing may take a large percentage. Using automated functions to perform such repeatable and tedious jobs will not only greatly increase the overall productivity but more importantly also improve the operation safety. For the purpose of investigating the technology without loss of generality, this research is conducted to create a coordinate control method for the boom, arm and bucket cylinders on a hydraulic excavator to perform accurate and effective works. On the basis of the kinematic analysis of the excavator linkage system, the tip trajectory of the end-effector can be determined in terms of three hydraulic cylinders coordinated motion with a visualized method. The coordination of those hydraulic cylinders is realized by controlling three electro-hydranlic proportional valves coordinately. Therefore,the complex control algorithm of a hydraulic excavator can be simplified into coordinated motion control of three individual systems.This coordinate control algorithm was validated on a wheeled hydraulic excavator, and the validation results indicated that this developed control method could satisfaetorily accomplish the auto-digging function for level digging or flat surface finishing.

  8. Adaptive coordinated control of engine speed and battery charging voltage

    Institute of Scientific and Technical Information of China (English)

    Jiangyan ZHANG; Xiaohong JIAO

    2008-01-01

    In this paper, the control problem of auxiliary power unit (APU) for hybrid electric vehicles is investigated. An adaptive controller is provided to achieve the coordinated control between the engine speed and the battery charging voltage. The proposed adaptive coordinated control laws for the throttle angle of the engine and the voltage of the power-converter can guarantee not only the asymptotic tracking performance of the engine speed and the regulation of the battery charging voltage, but also the robust stability of the closed loop system under external load changes. Simulation results are given to verify the performance of the proposed adaptive controller.

  9. Towards Coordination and Control of Multi-robot Systems

    DEFF Research Database (Denmark)

    Quottrup, Michael Melholt

    This thesis focuses on control and coordination of mobile multi-robot systems (MRS). MRS can often deal with tasks that are difficult to be accomplished by a single robot. One of the challenges is the need to control, coordinate and synchronize the operation of several robots to perform some...... specified task. This calls for new strategies and methods which allow the desired system behavior to be specified in a formal and succinct way. Two different frameworks for the coordination and control of MRS have been investigated. Framework I - A network of robots is modeled as a network of multi......-modal hybrid automata. The notion of bisimulations is used to abstract robots in the network. The result is a network of interacting timed automata which allows coordination among the robots and timing constraints to be considered. The model checker UPPAAL is used for formal symbolic model checking against...

  10. Multi-Objective Low-Carbon Economic Dispatch Considering Demand Response with Wind Power Integrated Systems

    Directory of Open Access Journals (Sweden)

    Liu Wenjuan

    2017-01-01

    Full Text Available The generation cost, carbon emissions and customers’ satisfaction are considered in this paper. On the basis of this, the multi-objective and low-carbon economic dispatch model with wind farm, this considers demand response, is established. The model user stochastic programming theory to describe the uncertainty of the wind power and converts it into an equivalent deterministic model by using distribution function of wind power output, optimizes demand side resources to adjust the next day load curve and to improve load rate and absorptive capacity of wind power, introduce customers’ satisfaction to ensure that the scheduling scheme satisfies customer and integrate the resources of source and load to unify coordination wind farm access to network and to meet the requirements of energy saving and emission reduction. The search process of artificial fish school algorithm introducing Tabu search and more targeted search mechanism, an multi-objective improved artificial fish school algorithm is proposed to solve this model. Using the technique for order preference by similarity to ideal solution (TOPSIS to sort the Pareto frontier, the optimal scheduling scheme is determined. Simulation results verify the rationality and validity of the proposed model and algorithm.

  11. Interleaving Guidance in Evolutionary Multi-Objective Optimization

    Institute of Scientific and Technical Information of China (English)

    Lam Thu Bui; Kalyanmoy Deb; Hussein A. Abbass; Daryl Essam

    2008-01-01

    In this paper, we propose a framework that uses localization for multi-objective optimization to simultaneously guide an evolutionary algorithm in both the decision and objective spaces. The localization is built using a limited number of adaptive spheres (local models) in the decision space. These spheres are usually guided, using some direction information, in the decision space towards the areas with non-dominated solutions. We use a second mechanism to adjust the spheres to specialize on different parts of the Pareto front by using a guided dominance technique in the objective space. Through this interleaved guidance in both spaces, the spheres will be guided towards different parts of the Pareto front while also exploring the decision space efficiently. The experimental results showed good performance for the local models using this dual guidance, in comparison with their original version.

  12. GNOMOS: The Gemini NIR-Optical Multi Object Spectrograph

    CERN Document Server

    Schiavon, Ricardo P; Chiboucas, Kristin; Diaz, Ruben; Geballe, Tom; Gimeno, German; Gomez, Percy; Hibon, Pascale; Hirst, Paul; Jorgensen, Inger; Labrie, Kathleen; Leggett, Sandy; Lemoine-Busserolle, Marie; Levenson, Nancy; Mason, Rachel; McDermid, Richard; Miller, Bryan; Nitta, Atsuko; Pessev, Peter; Rodgers, Bernadette; Schirmer, Mischa; Trujillo, Chad; Turner, James

    2012-01-01

    This paper is a response to a call for white papers solicited by Gemini Observatory and its Science and Technology Advisory Committee, to help define the science case and requirements for a new Gemini instrument, envisaged to consist of a single-object spectrograph at medium resolution simultaneously covering optical and near-infrared wavelengths. In this white paper we discuss the science case for an alternative new instrument, consisting instead of a multi-object, medium-resolution, high-throughput spectrograph, covering simultaneously the optical and near-infrared slices of the electromagnetic spectrum. We argue that combination of wide wavelength coverage at medium resolution with moderate multiplexing power is an innovative path that will enable the pursuit of fundamental science questions in a variety of astrophysical topics, without compromise of the science goals achievable by single-object spectroscopy on a wide baseline. We present a brief qualitative discussion of the main features of a notional ha...

  13. Genetic Algorithms Applied to Multi-Objective Aerodynamic Shape Optimization

    Science.gov (United States)

    Holst, Terry L.

    2005-01-01

    A genetic algorithm approach suitable for solving multi-objective problems is described and evaluated using a series of aerodynamic shape optimization problems. Several new features including two variations of a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding Pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. A new masking array capability is included allowing any gene or gene subset to be eliminated as decision variables from the design space. This allows determination of the effect of a single gene or gene subset on the Pareto optimal solution. Results indicate that the genetic algorithm optimization approach is flexible in application and reliable. The binning selection algorithms generally provide Pareto front quality enhancements and moderate convergence efficiency improvements for most of the problems solved.

  14. Well Field Management Using Multi-Objective Optimization

    DEFF Research Database (Denmark)

    Hansen, Annette Kirstine; Hendricks Franssen, H. J.; Bauer-Gottwein, Peter

    2013-01-01

    Efficient management of groundwater resources is important because groundwater availability is limited and, locally, groundwater quality has been impaired because of contamination. Here we present a multi-objective optimization framework for improving the management of a water works that operates...... with infiltration basins, injection wells and abstraction wells. The two management objectives are to minimize the amount of water needed for infiltration and to minimize the risk of getting contaminated water into the drinking water wells. The management is subject to a daily demand fulfilment constraint. Two...... optimization results are presented for the Hardhof water works in Zurich, Switzerland. It is found that both methods perform better than the historical management. The constant scheduling performs best in fairly stable conditions, whereas the sequential optimization performs best in extreme situations...

  15. A Multi-Objective Genetic Algorithm for Optimal Portfolio Problems

    Institute of Scientific and Technical Information of China (English)

    林丹; 赵瑞

    2004-01-01

    This paper concerns with modeling and design of an algorithm for the portfolio selection problems with fixed transaction costs and minimum transaction lots. A mean-variance model for the portfolio selection problem is proposed, and the model is formulated as a non-smooth and nonlinear integer programming problem with multiple objective functions. As it has been proven that finding a feasible solution to the problem only is already NP-hard, based on NSGA-II and genetic algorithm for numerical optimization of constrained problems (Genocop), a multi-objective genetic algorithm (MOGA) is designed to solve the model. Its features comprise integer encoding and corresponding operators, and special treatment of constraints conditions. It is illustrated via a numerical example that the genetic algorithm can efficiently solve portfolio selection models proposed in this paper. This approach offers promise for the portfolio problems in practice.

  16. 2000 fps multi-object tracking based on color histogram

    Science.gov (United States)

    Gu, Qingyi; Takaki, Takeshi; Ishii, Idaku

    2012-06-01

    In this study, we develop a real-time, color histogram-based tracking system for multiple color-patterned objects in a 512×512 image at 2000 fps. Our system can simultaneously extract the positions, areas, orientation angles, and color histograms of multiple objects in an image using the hardware implementation of a multi-object, color histogram extraction circuit module on a high-speed vision platform. It can both label multiple objects in an image consisting of connected components and calculate their moment features and 16-bin hue-based color histograms using cell-based labeling. We demonstrate the performance of our system by showing several experimental results: (1) tracking of multiple color-patterned objects on a plate rotating at 16 rps, and (2) tracking of human hand movement with two color-patterned drinking bottles.

  17. Multi-object fixed delay Michelson interferometer for astronomical observation

    Science.gov (United States)

    Zhang, Kai; Zhu, Yongtian; Wang, Lei; Chen, Yi; Wang, Liang

    2012-10-01

    Optical interferometry isn't only widely applied into optical workshop, but also makes great contribution in astronomical observation. A multi-object fixed delay Michelson interferometer commissioned to search extra-solar planet (exoplanet) is introduced here. Fixed delay of 1.9mm, which is good for stellar radial velocity measuring precision, is obtained by two interference arms with different materials. This configuration has different refractive indexes and physical characteristics so that supplies wider field of view and better thermal stability. In addition, compact interference component with three glued prisms and smart structure are the other important features. Because of vibration influence, the combination among the prisms is a direct and effective method. And the reason why make the structure as small as possible is of central obscuration under the workspace of interferometer.

  18. 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)

  19. SAPA: A Multi-objective Metric Temporal Planner

    CERN Document Server

    Do, M; 10.1613/jair.1156

    2011-01-01

    SAPA is a domain-independent heuristic forward chaining planner that can handle durative actions, metric resource constraints, and deadline goals. It is designed to be capable of handling the multi-objective nature of metric temporal planning. Our technical contributions include (i) planning-graph based methods for deriving heuristics that are sensitive to both cost and makespan (ii) techniques for adjusting the heuristic estimates to take action interactions and metric resource limitations into account and (iii) a linear time greedy post-processing technique to improve execution flexibility of the solution plans. An implementation of SAPA using many of the techniques presented in this paper was one of the best domain independent planners for domains with metric and temporal constraints in the third International Planning Competition, held at AIPS-02. We describe the technical details of extracting the heuristics and present an empirical evaluation of the current implementation of SAPA.

  20. 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

  1. 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

  2. Stochastic wind turbine control in multiblade coordinates

    DEFF Research Database (Denmark)

    Thomsen, Sven Creutz; Niemann, Hans Henrik; Poulsen, Niels Kjølstad

    2010-01-01

    In this paper we consider wind turbine load attenuation through model based control. Asymmetric loads caused by the wind field can be reduced by pitching the blades individually. To this end we investigate the use of stochastic models of the wind which can be included in a model based individual ...

  3. The multi-objective optimization of the horizontal-axis marine current turbine based on NSGA-II algorithm

    Science.gov (United States)

    Zhu, G. J.; Guo, P. C.; Luo, X. Q.; Feng, J. J.

    2012-11-01

    The present paper describes a hydrodynamic optimization technique for horizontal-axial marine current turbine. The pitch angle distribution is important to marine current turbine. In this paper, the pitch angle distribution curve is parameterized as four control points by Bezier curve method. The coordinates of the four control points are chosen as optimization variables, and the sample space are structured according to the Box-Behnken experimental design method (BBD). Then the power capture coefficient and axial thrust coefficient in design tip-speed ratio is obtained for all the elements in the sample space by CFD numerical simulation. The power capture coefficient and axial thrust are chosen as objective function, and quadratic polynomial regression equations are constructed to fit the relationship between the optimization variables and each objective function according to response surface model. With the obtained quadratic polynomial regression equations as performance prediction model, the marine current turbine is optimized using the NSGA-II multi-objective genetic algorithm, which finally offers an improved marine current turbine.

  4. Control Coordination of Large Scale Hereditary Systems.

    Science.gov (United States)

    1985-07-01

    SIAM J. Contr. Opt., 16 (1978), 599-645. [8] A. N. Michel , On the status of stability of interconnected systems, IEEE Trans. Automat. Contr. AC-28...Salamon, On controllability and observability of time delay systems, IEEE Trans Automat. Contr., AC-29 (1984), 432-438. [14] N. R. Sandell , P. Varaiya...John Wiley and Sons, New York, hi(T)-b I and h2 (T)-b 2 with limited information 1980." exchanged between components. (2) N. R. Sandell , P. Varaiya, M

  5. Low-thrust orbit transfer optimization with refined Q-law and multi-objective genetic algorithm

    Science.gov (United States)

    Lee, Seungwon; Petropoulos, Anastassios E.; von Allmen, Paul

    2005-01-01

    An optimization method for low-thrust orbit transfers around a central body is developed using the Q-law and a multi-objective genetic algorithm. in the hybrid method, the Q-law generates candidate orbit transfers, and the multi-objective genetic algorithm optimizes the Q-law control parameters in order to simultaneously minimize both the consumed propellant mass and flight time of the orbit tranfer. This paper addresses the problem of finding optimal orbit transfers for low-thrust spacecraft.

  6. Decentralized Receding Horizon Control and Coordination of Autonomous Vehicle Formations

    NARCIS (Netherlands)

    Keviczky, T.; Borelli, F.; Fregene, K.; Godbole, D.; Bals, G.J.

    2008-01-01

    This paper describes the application of a novel methodology for high-level control and coordination of autonomous vehicle teams and its demonstration on high-fidelity models of the organic air vehicle developed at Honeywell Laboratories. The scheme employs decentralized receding horizon controllers

  7. The Application Research of Inventory Control with Multi -objective Constraints under the Perspective of Supply Chain---A Case Analysis of Industrial Lubricating Oil Supply Chain%库存控制之供应链多目标视角下的应用研究--以工业润滑油供应链为例

    Institute of Scientific and Technical Information of China (English)

    王丽娟; 李英英

    2016-01-01

    〔Abstract〕 Study on inventory control of supply chain has become a trend , different from the conventional single target study on in-ventory control , the author made the emphasis on the study on minimizing inventory cost and maximizing the average satisfaction rate of cus -tomer demand and built a multi -objective inventory control model . In the end , the model and the solution were applied to the lubricating oil supply chain , which was helpful for making decisions for these enterprises in the lubricating oil supply chain .%供应链环境下库存控制的研究已经成为趋势,区别于常规的单目标库存控制的研究,本文以工业润滑油供应链为依托,提出了在供应链背景下考虑供应链库存成本最小化和客户平均需求满足率最大化为目标的库存控制研究思路,构建了多目标库存控制模型。最后将所建的供应链视角下多目标库存控制决策模型进行实证分析,为目标供应链上的企业做出生产、销售等决策提供依据。

  8. An Efficient Multi-objective Approach for Designing of Communication Interfaces in Smart Grids

    DEFF Research Database (Denmark)

    Ghasemkhani, Amir; Anvari-Moghaddam, Amjad; Guerrero, Josep M.

    2016-01-01

    The next generation of power systems require to use smart grid technologies due to their unique features like high speed, reliable and secure data communications to monitor, control and protect system effectively. Hence, one of the main requirements of achieving a smart grid is optimal designing...... of telecommunication systems. In this study, a novel dynamic Multi-Objective Shortest Path (MOSP) algorithm is presented to design a spanning graph of a communication infrastructure using high speed Optimal Power Ground Wire (OPGW) cables and Phasor Measurement Units (PMUs). Applicability of the proposed model...

  9. Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored

    Institute of Scientific and Technical Information of China (English)

    Carlos A. COELLO COELLO

    2009-01-01

    This paper provides a short review of some of the main topics in which the current research in evolutionary multi-objective optimization is being focused. The topics discussed include new algorithms, efficiency, relaxed forms of dominance, scalability, and alternative metaheuristics. This discussion motivates some further topics which,from the author's perspective, constitute good potential areas for future research, namely, constraint-handling techniques,incorporation of user's preferences and parameter control,This information is expected to be useful for those interested in pursuing research in this area.

  10. 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

  11. Multi-objective robust airfoil optimization based on non-uniform rational B-spline (NURBS) representation

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    In order to improve airfoil performance under different flight conditions and to make the performance insensitive to off-design condition at the same time,a multi-objective optimization approach considering robust design has been developed and applied to airfoil design. Non-uniform rational B-spline (NURBS) representation is adopted in airfoil design process,control points and related weights around airfoil are used as design variables. Two airfoil representation cases show that the NURBS method can get airfoil geometry with max geometry error less than 0.0019. By using six-sigma robust approach in multi-objective airfoil design,each sub-objective function of the problem has robustness property. By adopting multi-objective genetic algorithm that is based on non-dominated sorting,a set of non-dominated airfoil solutions with robustness can be obtained in the design. The optimum robust airfoil can be traded off and selected in these non-dominated solutions by design tendency. By using the above methods,a multi-objective robust optimization was conducted for NASA SC0712 airfoil. After performing robust airfoil optimization,the mean value of drag coefficient at Ma0.7-0.8 and the mean value of lift coefficient at post stall regime (Ma0.3) have been improved by 12.2% and 25.4%. By comparing the aerodynamic force coefficients of optimization result,it shows that: different from single robust airfoil design which just improves the property of drag divergence at Ma0.7-0.8,multi-objective robust design can improve both the drag divergence property at Ma0.7-0.8 and stall property at low speed. The design cases show that the multi-objective robust design method makes the airfoil performance robust under different off-design conditions.

  12. A Multi-Objective Genetic Algorithm for Outlier Removal.

    Science.gov (United States)

    Nahum, Oren E; Yosipof, Abraham; Senderowitz, Hanoch

    2015-12-28

    Quantitative structure activity relationship (QSAR) or quantitative structure property relationship (QSPR) models are developed to correlate activities for sets of compounds with their structure-derived descriptors by means of mathematical models. The presence of outliers, namely, compounds that differ in some respect from the rest of the data set, compromise the ability of statistical methods to derive QSAR models with good prediction statistics. Hence, outliers should be removed from data sets prior to model derivation. Here we present a new multi-objective genetic algorithm for the identification and removal of outliers based on the k nearest neighbors (kNN) method. The algorithm was used to remove outliers from three different data sets of pharmaceutical interest (logBBB, factor 7 inhibitors, and dihydrofolate reductase inhibitors), and its performances were compared with those of five other methods for outlier removal. The results suggest that the new algorithm provides filtered data sets that (1) better maintain the internal diversity of the parent data sets and (2) give rise to QSAR models with much better prediction statistics. Equally good filtered data sets in terms of these metrics were obtained when another objective function was added to the algorithm (termed "preservation"), forcing it to remove certain compounds with low probability only. This option is highly useful when specific compounds should be preferably kept in the final data set either because they have favorable activities or because they represent interesting molecular scaffolds. We expect this new algorithm to be useful in future QSAR applications.

  13. Determination of Pareto frontier in multi-objective maintenance optimization

    Energy Technology Data Exchange (ETDEWEB)

    Certa, Antonella [Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale, Universita di Palermo 90128 Palermo (Italy); Galante, Giacomo, E-mail: galante@dtpm.unipa.i [Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale, Universita di Palermo 90128 Palermo (Italy); Lupo, Toni; Passannanti, Gianfranco [Dipartimento di Tecnologia Meccanica, Produzione e Ingegneria Gestionale, Universita di Palermo 90128 Palermo (Italy)

    2011-07-15

    The objective of a maintenance policy generally is the global maintenance cost minimization that involves not only the direct costs for both the maintenance actions and the spare parts, but also those ones due to the system stop for preventive maintenance and the downtime for failure. For some operating systems, the failure event can be dangerous so that they are asked to operate assuring a very high reliability level between two consecutive fixed stops. The present paper attempts to individuate the set of elements on which performing maintenance actions so that the system can assure the required reliability level until the next fixed stop for maintenance, minimizing both the global maintenance cost and the total maintenance time. In order to solve the previous constrained multi-objective optimization problem, an effective approach is proposed to obtain the best solutions (that is the Pareto optimal frontier) among which the decision maker will choose the more suitable one. As well known, describing the whole Pareto optimal frontier generally is a troublesome task. The paper proposes an algorithm able to rapidly overcome this problem and its effectiveness is shown by an application to a case study regarding a complex series-parallel system.

  14. 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.

  15. A Multi-objective Procedure for Efficient Regression Modeling

    CERN Document Server

    Sinha, Ankur; Kuosmanen, Timo

    2012-01-01

    Variable selection is recognized as one of the most critical steps in statistical modeling. The problems encountered in engineering and social sciences are commonly characterized by over-abundance of explanatory variables, non-linearities and unknown interdependencies between the regressors. An added difficulty is that the analysts may have little or no prior knowledge on the relative importance of the variables. To provide a robust method for model selection, this paper introduces a technique called the Multi-objective Genetic Algorithm for Variable Selection (MOGA-VS) which provides the user with an efficient set of regression models for a given data-set. The algorithm considers the regression problem as a two objective task, where the purpose is to choose those models over the other which have less number of regression coefficients and better goodness of fit. In MOGA-VS, the model selection procedure is implemented in two steps. First, we generate the frontier of all efficient or non-dominated regression m...

  16. 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).

  17. Fireball multi object spectrograph: as-built optic performances

    Science.gov (United States)

    Grange, R.; Milliard, B.; Lemaitre, G.; Quiret, S.; Pascal, S.; Origné, A.; Hamden, E.; Schiminovich, D.

    2016-07-01

    Fireball (Faint Intergalactic Redshifted Emission Balloon) is a NASA/CNES balloon-borne experiment to study the faint diffuse circumgalactic medium from the line emissions in the ultraviolet (200 nm) above 37 km flight altitude. Fireball relies on a Multi Object Spectrograph (MOS) that takes full advantage of the new high QE, low noise 13 μm pixels UV EMCCD. The MOS is fed by a 1 meter diameter parabola with an extended field (1000 arcmin2) using a highly aspherized two mirror corrector. All the optical train is working at F/2.5 to maintain a high signal to noise ratio. The spectrograph (R 2200 and 1.5 arcsec FWHM) is based on two identical Schmidt systems acting as collimator and camera sharing a 2400 g/mm aspherized reflective Schmidt grating. This grating is manufactured from active optics methods by double replication technique of a metal deformable matrix whose active clear aperture is built-in to a rigid elliptical contour. The payload and gondola are presently under integration at LAM. We will present the alignment procedure and the as-built optic performances of the Fireball instrument.

  18. Monocular visual scene understanding: understanding multi-object traffic scenes.

    Science.gov (United States)

    Wojek, Christian; Walk, Stefan; Roth, Stefan; Schindler, Konrad; Schiele, Bernt

    2013-04-01

    Following recent advances in detection, context modeling, and tracking, scene understanding has been the focus of renewed interest in computer vision research. This paper presents a novel probabilistic 3D scene model that integrates state-of-the-art multiclass object detection, object tracking and scene labeling together with geometric 3D reasoning. Our model is able to represent complex object interactions such as inter-object occlusion, physical exclusion between objects, and geometric context. Inference in this model allows us to jointly recover the 3D scene context and perform 3D multi-object tracking from a mobile observer, for objects of multiple categories, using only monocular video as input. Contrary to many other approaches, our system performs explicit occlusion reasoning and is therefore capable of tracking objects that are partially occluded for extended periods of time, or objects that have never been observed to their full extent. In addition, we show that a joint scene tracklet model for the evidence collected over multiple frames substantially improves performance. The approach is evaluated for different types of challenging onboard sequences. We first show a substantial improvement to the state of the art in 3D multipeople tracking. Moreover, a similar performance gain is achieved for multiclass 3D tracking of cars and trucks on a challenging dataset.

  19. Multi-Objective Optimization of A PCHE Channels

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Sang Moon; Kim, Kwang Yong [Inha University, Incheon (Korea, Republic of)

    2011-10-15

    High-temperature, gas-cooled nuclear reactors with a closed gas turbine cycle are recently being considered as a nuclear power generation concept for the future. In theory, the gas turbine cycle has an advantage in terms of simplicity and efficiency compared to the steam turbine cycle. However, since gas is used as the working fluid, inefficiency due to large volumes is inevitable, and a heat exchanger is used as a recuperator and pre-cooler. To solve this problem, different types of heat exchanger are needed. One of the alternative heat exchangers is the printed circuit heat exchanger (PCHE) developed by HEATRIC. Each flow channel of the PCHE is made through chemical etching on the surface of metal plates, and the typical PCHE channels on each plate have a zigzag shape to promote the heat transfer between the cold and hot channels. In this work, the zigzag flow channels of the PCHE have been optimized by using three-dimensional RANS analysis and a hybrid multi-objective evolutionary algorithm coupled with the RSA model. The cold channel angle and the ellipse aspect ratio of the cold channel are employed as the design variables. A group of optimal shapes are presented through Paretooptimal front (POF) by an {epsilon}-constraint strategy through an NSGA-II algorithm

  20. Multi-objective evolutionary algorithm for operating parallel reservoir system

    Science.gov (United States)

    Chang, Li-Chiu; Chang, Fi-John

    2009-10-01

    SummaryThis paper applies a multi-objective evolutionary algorithm, the non-dominated sorting genetic algorithm (NSGA-II), to examine the operations of a multi-reservoir system in Taiwan. The Feitsui and Shihmen reservoirs are the most important water supply reservoirs in Northern Taiwan supplying the domestic and industrial water supply needs for over 7 million residents. A daily operational simulation model is developed to guide the releases of the reservoir system and then to calculate the shortage indices (SI) of both reservoirs over a long-term simulation period. The NSGA-II is used to minimize the SI values through identification of optimal joint operating strategies. Based on a 49 year data set, we demonstrate that better operational strategies would reduce shortage indices for both reservoirs. The results indicate that the NSGA-II provides a promising approach. The pareto-front optimal solutions identified operational compromises for the two reservoirs that would be expected to improve joint operations.

  1. Coordinated Control of Wave Energy Converters Subject to Motion Constraints

    Directory of Open Access Journals (Sweden)

    Liguo Wang

    2016-06-01

    Full Text Available In this paper, a generic coordinated control method for wave energy converters is proposed, and the constraints on motion amplitudes and the hydrodynamic interaction between converters are considered. The objective of the control problem is to maximize the energy converted from ocean waves, and this is achieved by coordinating the power take-off (PTO damping of each wave energy converter in the frequency domain in each sea state. In a case study, a wave energy farm consisting of four converters based on the concept developed by Uppsala University is studied. In the solution, motion constraints, including constraints on the amplitudes of displacement and velocity, are included. Twelve months of sea states, based on measured wave data at the Lysekil test site on the Swedish west coast, are used in the simulation to evaluate the performance of the wave energy farm using the new method. Results from the new coordinated control method and traditional control method are compared, indicating that the coordinated control of wave energy converters is an effective way to improve the energy production of wave energy farm in harmonic waves.

  2. 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.

  3. Modelling and control of two coordinated robot arms

    Science.gov (United States)

    Tarn, T. J.; Yun, X.; Bejczy, A. K.

    1988-01-01

    Two coordinated robot arms are modeled by considering the two arms as working on the same object simultaneously and as a closed kinematic chain. In both formulations, a novel dynamic control method is used which is based on feedback linearization and simultaneous output decoupling.

  4. 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.

  5. Controlled isotropic or anisotropic nanoscale growth of coordination polymers: formation of hybrid coordination polymer particles.

    Science.gov (United States)

    Lee, Hee Jung; Cho, Yea Jin; Cho, Won; Oh, Moonhyun

    2013-01-22

    The ability to fabricate multicompositional hybrid materials in a precise and controlled manner is one of the primary goals of modern materials science research. In addition, an understanding of the phenomena associated with the systematic growth of one material on another can facilitate the evolution of multifunctional hybrid materials. Here, we demonstrate precise manipulation of the isotropic and/or anisotropic nanoscale growth of various coordination polymers (CPs) to obtain heterocompositional hybrid coordination polymer particles. Chemical composition analyses conducted at every growth step reveal the formation of accurately assembled hybrid nanoscale CPs, and microscopy images are used to examine the morphology of the particles and visualize the hybrid structures. The dissimilar growth behavior, that is, growth in an isotropic or anisotropic fashion, is found to be dependent on the size of the metal ions involved within the CPs.

  6. Kinematics-coordinated walking pattern based on embedded controls.

    Science.gov (United States)

    Mishra, S; Joshi, D; Ribeiro, R; Anand, S

    2010-01-01

    Electromechanical above-knee prosthetics are widely available, and are reliant on repetitive knee movements of fixed length/angle. This work explores the viability of developing adaptive movements on existing prototypes, through embedded controls from 8051-class 8-bit microcontroller units (MCUs). The system includes an integrated goniometer, intended for measuring the knee angle of the sound limb. The phase delay is subsequently processed to bring about kinematic coordination in the proposed echo-controlled prosthetic.

  7. The Power Unit Coordinated Control via Uniform Differential Evolution

    OpenAIRE

    Zain Abdalla Zahran; Rui Feng Shi; Xiang Jie Liu

    2013-01-01

    This paper modified the differential evolution (DE) algorithm adaptively to solve the power unit coordinated control (PUCC) problem. It was modified in two aspects: 1) a uniform initialization, which was controlled and regulated by a zone factor (m), 2) a regular mutation process, to develop an effective searching process and improve the convergence of the basic DE algorithm. A numerical case study was employed to verify the performance of our proposed uniform differential evolution (UDE) a...

  8. A Novel Distributed Secondary Coordination Control Approach for Islanded Microgrids

    DEFF Research Database (Denmark)

    Lu, Xiaoqing; Yu, Xinghuo; Lai, Jingang

    2017-01-01

    This paper develops a new distributed secondary cooperative control scheme to coordinate distributed generators (DGs) in islanded microgrids (MGs). A finite time frequency regulation strategy containing a consensus-based distributed active power regulator is presented, which can not only guarantee...... controllers are equipped with bounded control inputs to suppress the transient overshoot, and they are implemented through sparse communication networks. The effectiveness of the control in case of load variation, plug-and-play capability, communication topology change, link failure, time delays and data drop...

  9. Multi-telerobot collaboration based on coordinated controller

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    A coordinated controller used for multi-telerobots collaboration was presented based on the strategy of shared control. First, it can overcome the effect of time delay. And, it combines the intelligence of the master side and the slave side, which cannot only increase the efficiency and the safety of the system but also relieve the burden and requirements of the operator. The controller can also cope with the collision between two telerobots. A simulation experiment was carried out to verify the validity of the controller for two slave robots.

  10. Arm coordination in octopus crawling involves unique motor control strategies.

    Science.gov (United States)

    Levy, Guy; Flash, Tamar; Hochner, Binyamin

    2015-05-04

    To cope with the exceptional computational complexity that is involved in the control of its hyper-redundant arms [1], the octopus has adopted unique motor control strategies in which the central brain activates rather autonomous motor programs in the elaborated peripheral nervous system of the arms [2, 3]. How octopuses coordinate their eight long and flexible arms in locomotion is still unknown. Here, we present the first detailed kinematic analysis of octopus arm coordination in crawling. The results are surprising in several respects: (1) despite its bilaterally symmetrical body, the octopus can crawl in any direction relative to its body orientation; (2) body and crawling orientation are monotonically and independently controlled; and (3) contrasting known animal locomotion, octopus crawling lacks any apparent rhythmical patterns in limb coordination, suggesting a unique non-rhythmical output of the octopus central controller. We show that this uncommon maneuverability is derived from the radial symmetry of the arms around the body and the simple pushing-by-elongation mechanism by which the arms create the crawling thrust. These two together enable a mechanism whereby the central controller chooses in a moment-to-moment fashion which arms to recruit for pushing the body in an instantaneous direction. Our findings suggest that the soft molluscan body has affected in an embodied way [4, 5] the emergence of the adaptive motor behavior of the octopus.

  11. Multi-objective Genetic Algorithm for Association Rule Mining Using a Homogeneous Dedicated Cluster of Workstations

    Directory of Open Access Journals (Sweden)

    S. Dehuri

    2006-01-01

    Full Text Available This study presents a fast and scalable multi-objective association rule mining technique using genetic algorithm from large database. The objective functions such as confidence factor, comprehensibility and interestingness can be thought of as different objectives of our association rule-mining problem and is treated as the basic input to the genetic algorithm. The outcomes of our algorithm are the set of non-dominated solutions. However, in data mining the quantity of data is growing rapidly both in size and dimensions. Furthermore, the multi-objective genetic algorithm (MOGA tends to be slow in comparison with most classical rule mining methods. Hence, to overcome these difficulties we propose a fast and scalability technique using the inherent parallel processing nature of genetic algorithm and a homogeneous dedicated network of workstations (NOWs. Our algorithm exploit both data and control parallelism by distributing the data being mined and the population of individuals across all available processors. The experimental result shows that the algorithm has been found suitable for large database with an encouraging speed up.

  12. 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...... 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.......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......-objective problems having 10+ design variables are both highly constrained, nonlinear and non-smooth but nevertheless the algorithm converges to the Pareto-front within a hours of computation (20k function evaluations). Additionally, the robustness and convergence speed of the algorithm are investigated using...

  13. Long Series Multi-objectives Optimal Operation of Water And Sediment Regulation

    Science.gov (United States)

    Bai, T.; Jin, W.

    2015-12-01

    Secondary suspended river in Inner Mongolia reaches have formed and the security of reach and ecological health of the river are threatened. Therefore, researches on water-sediment regulation by cascade reservoirs are urgent and necessary. Under this emergency background, multi-objectives water and sediment regulation are studied in this paper. Firstly, multi-objective optimal operation models of Longyangxia and Liujiaxia cascade reservoirs are established. Secondly, based on constraints handling and feasible search space techniques, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is greatly improved to solve the model. Thirdly, four different scenarios are set. It is demonstrated that: (1) scatter diagrams of perato front are obtained to show optimal solutions of power generation maximization, sediment maximization and the global equilibrium solutions between the two; (2) the potentiality of water-sediment regulation by Longyangxia and Liujiaxia cascade reservoirs are analyzed; (3) with the increasing water supply in future, conflict between water supply and water-sediment regulation occurred, and the sustainability of water and sediment regulation will confront with negative influences for decreasing transferable water in cascade reservoirs; (4) the transfer project has less benefit for water-sediment regulation. The research results have an important practical significance and application on water-sediment regulation by cascade reservoirs in the Upper Yellow River, to construct water and sediment control system in the whole Yellow River basin.

  14. Multi-objective vs. single-objective calibration of a hydrologic model using single- and multi-objective screening

    Science.gov (United States)

    Mai, Juliane; Cuntz, Matthias; Shafii, Mahyar; Zink, Matthias; Schäfer, David; Thober, Stephan; Samaniego, Luis; Tolson, Bryan

    2016-04-01

    Hydrologic models are traditionally calibrated against observed streamflow. Recent studies have shown however, that only a few global model parameters are constrained using this kind of integral signal. They can be identified using prior screening techniques. Since different objectives might constrain different parameters, it is advisable to use multiple information to calibrate those models. One common approach is to combine these multiple objectives (MO) into one single objective (SO) function and allow the use of a SO optimization algorithm. Another strategy is to consider the different objectives separately and apply a MO Pareto optimization algorithm. In this study, two major research questions will be addressed: 1) How do multi-objective calibrations compare with corresponding single-objective calibrations? 2) How much do calibration results deteriorate when the number of calibrated parameters is reduced by a prior screening technique? The hydrologic model employed in this study is a distributed hydrologic model (mHM) with 52 model parameters, i.e. transfer coefficients. The model uses grid cells as a primary hydrologic unit, and accounts for processes like snow accumulation and melting, soil moisture dynamics, infiltration, surface runoff, evapotranspiration, subsurface storage and discharge generation. The model is applied in three distinct catchments over Europe. The SO calibrations are performed using the Dynamically Dimensioned Search (DDS) algorithm with a fixed budget while the MO calibrations are achieved using the Pareto Dynamically Dimensioned Search (PA-DDS) algorithm allowing for the same budget. The two objectives used here are the Nash Sutcliffe Efficiency (NSE) of the simulated streamflow and the NSE of the logarithmic transformation. It is shown that the SO DDS results are located close to the edges of the Pareto fronts of the PA-DDS. The MO calibrations are hence preferable due to their supply of multiple equivalent solutions from which the

  15. Valuing hydrological alteration in Multi-Objective reservoir management

    Science.gov (United States)

    Bizzi, S.; Pianosi, F.; Soncini-Sessa, R.

    2012-04-01

    Water management through dams and reservoirs is worldwide necessary to support key human-related activities ranging from hydropower production to water allocation for agricultural production, and flood risk mitigation. Advances in multi-objectives (MO) optimization techniques and ever growing computing power make it possible to design reservoir operating policies that represent Pareto-optimal tradeoffs between the multiple interests analysed. These progresses if on one hand are likely to enhance performances of commonly targeted objectives (such as hydropower production or water supply), on the other risk to strongly penalize all the interests not directly (i.e. mathematically) optimized within the MO algorithm. Alteration of hydrological regime, although is a well established cause of ecological degradation and its evaluation and rehabilitation are commonly required by recent legislation (as the Water Framework Directive in Europe), is rarely embedded as an objective in MO planning of optimal releases from reservoirs. Moreover, even when it is explicitly considered, the criteria adopted for its evaluation are doubted and not commonly trusted, undermining the possibility of real implementation of environmentally friendly policies. The main challenges in defining and assessing hydrological alterations are: how to define a reference state (referencing); how to define criteria upon which to build mathematical indicators of alteration (measuring); and finally how to aggregate the indicators in a single evaluation index that can be embedded in a MO optimization problem (valuing). This paper aims to address these issues by: i) discussing benefits and constrains of different approaches to referencing, measuring and valuing hydrological alteration; ii) testing two alternative indices of hydrological alteration in the context of MO problems, one based on the established framework of Indices of Hydrological Alteration (IHA, Richter et al., 1996), and a novel satisfying the

  16. Statistical process control (SPC) for coordinate measurement machines

    Energy Technology Data Exchange (ETDEWEB)

    Escher, R.N.

    2000-01-04

    The application of process capability analysis, using designed experiments, and gage capability studies as they apply to coordinate measurement machine (CMM) uncertainty analysis and control will be demonstrated. The use of control standards in designed experiments, and the use of range charts and moving range charts to separate measurement error into it's discrete components will be discussed. The method used to monitor and analyze the components of repeatability and reproducibility will be presented with specific emphasis on how to use control charts to determine and monitor CMM performance and capability, and stay within your uncertainty assumptions.

  17. [Bionic model for coordinated head-eye motion control].

    Science.gov (United States)

    Mao, Xiaobo; Chen, Tiejun

    2011-10-01

    The relationships between eye movements and head movements of the primate during gaze shifts are analyzed in detail in the present paper. Applying the mechanisms of neurophysiology to engineering domain, we have improved the robot eye-head coordination. A bionic control strategy of coordinated head-eye motion was proposed. The processes of gaze shifts are composed of an initial fast phase followed by a slow phase. In the fast phase saccade eye movements and slow head movements were combined, which cooperate to bring gaze from an initial resting position toward the new target rapidly, while in the slow phase the gaze stability and target fixation were ensured by the action of the vestibulo-ocular reflex (VOR) where the eyes and head rotate by equal amplitudes in opposite directions. A bionic gaze control model was given. The simulation results confirmed the effectiveness of the model by comparing with the results of neurophysiology experiments.

  18. 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...

  19. Postural Control in Children With Developmental Coordination Disorder

    OpenAIRE

    Geuze, Reint H.

    2005-01-01

    The development of static balance is a basic characteristic of normal motor development. Most developmental motor tests include a measure of static balance. Children with Developmental Coordination Disorder (DCD) often fail this item. This study reviews the balance problems of children with DCD. The general conclusion is drawn that under normal conditions static balance control is not a problem for children with DCD. Only in difficult, unattended, or novel situations such children seem to suf...

  20. Novel system identification method and multi-objective-optimal multivariable disturbance observer for electric wheelchair.

    Science.gov (United States)

    Nasser Saadatzi, Mohammad; Poshtan, Javad; Sadegh Saadatzi, Mohammad; Tafazzoli, Faezeh

    2013-01-01

    Electric wheelchair (EW) is subject to diverse types of terrains and slopes, but also to occupants of various weights, which causes the EW to suffer from highly perturbed dynamics. A precise multivariable dynamics of the EW is obtained using Lagrange equations of motion which models effects of slopes as output-additive disturbances. A static pre-compensator is analytically devised which considerably decouples the EW's dynamics and also brings about a more accurate identification of the EW. The controller is designed with a disturbance-observer (DOB) two-degree-of-freedom architecture, which reduces sensitivity to the model uncertainties while enhancing rejection of the disturbances. Upon disturbance rejection, noise reduction, and robust stability of the control system, three fitness functions are presented by which the DOB is tuned using a multi-objective optimization (MOO) approach namely non-dominated sorting genetic algorithm-II (NSGA-II). Finally, experimental results show desirable performance and robust stability of the proposed algorithm.

  1. Improving Multi-Objective Management of Water Quality Tipping Points: Revisiting the Classical Shallow Lake Problem

    Science.gov (United States)

    Quinn, J. D.; Reed, P. M.; Keller, K.

    2015-12-01

    Recent multi-objective extensions of the classical shallow lake problem are useful for exploring the conceptual and computational challenges that emerge when managing irreversible water quality tipping points. Building on this work, we explore a four objective version of the lake problem where a hypothetical town derives economic benefits from polluting a nearby lake, but at the risk of irreversibly tipping the lake into a permanently polluted state. The trophic state of the lake exhibits non-linear threshold dynamics; below some critical phosphorus (P) threshold it is healthy and oligotrophic, but above this threshold it is irreversibly eutrophic. The town must decide how much P to discharge each year, a decision complicated by uncertainty in the natural P inflow to the lake. The shallow lake problem provides a conceptually rich set of dynamics, low computational demands, and a high level of mathematical difficulty. These properties maximize its value for benchmarking the relative merits and limitations of emerging decision support frameworks, such as Direct Policy Search (DPS). Here, we explore the use of DPS as a formal means of developing robust environmental pollution control rules that effectively account for deeply uncertain system states and conflicting objectives. The DPS reformulation of the shallow lake problem shows promise in formalizing pollution control triggers and signposts, while dramatically reducing the computational complexity of the multi-objective pollution control problem. More broadly, the insights from the DPS variant of the shallow lake problem formulated in this study bridge emerging work related to socio-ecological systems management, tipping points, robust decision making, and robust control.

  2. Coordinated design and performance evaluation of UPFC supplementary modulation controllers

    Energy Technology Data Exchange (ETDEWEB)

    Padiyar, K.R.; Saikumar, H.V. [Indian Inst. of Science, Dept. of Electrical Engineering, Bangalore (India)

    2005-02-01

    Mitigation of low-frequency electro-mechanical oscillations is essential for secure operation of power systems. The fast acting, power electronics based FACTS controllers which are capable of improving both steady state and dynamic performance permit newer approaches to system stabilization. The objective of the work presented in this paper is to carry out a coordinated design of supplementary modulation controllers (SMCs) for UPFC and to evaluate their performance. The effectiveness of the SMCs in damping the critical modes is tested on a four machine, 10 bus system. (Author)

  3. Microgrids and distributed generation systems: Control, operation, coordination and planning

    Science.gov (United States)

    Che, Liang

    Distributed Energy Resources (DERs) which include distributed generations (DGs), distributed energy storage systems, and adjustable loads are key components in microgrid operations. A microgrid is a small electric power system integrated with on-site DERs to serve all or some portion of the local load and connected to the utility grid through the point of common coupling (PCC). Microgrids can operate in both grid-connected mode and island mode. The structure and components of hierarchical control for a microgrid at Illinois Institute of Technology (IIT) are discussed and analyzed. Case studies would address the reliable and economic operation of IIT microgrid. The simulation results of IIT microgrid operation demonstrate that the hierarchical control and the coordination strategy of distributed energy resources (DERs) is an effective way of optimizing the economic operation and the reliability of microgrids. The benefits and challenges of DC microgrids are addressed with a DC model for the IIT microgrid. We presented the hierarchical control strategy including the primary, secondary, and tertiary controls for economic operation and the resilience of a DC microgrid. The simulation results verify that the proposed coordinated strategy is an effective way of ensuring the resilient response of DC microgrids to emergencies and optimizing their economic operation at steady state. The concept and prototype of a community microgrid that interconnecting multiple microgrids in a community are proposed. Two works are conducted. For the coordination, novel three-level hierarchical coordination strategy to coordinate the optimal power exchanges among neighboring microgrids is proposed. For the planning, a multi-microgrid interconnection planning framework using probabilistic minimal cut-set (MCS) based iterative methodology is proposed for enhancing the economic, resilience, and reliability signals in multi-microgrid operations. The implementation of high-reliability microgrids

  4. Multimodal Perception and Multicriterion Control of Nested Systems. 1; Coordination of Postural Control and Vehicular Control

    Science.gov (United States)

    Riccio, Gary E.; McDonald, P. Vernon

    1998-01-01

    The purpose of this report is to identify the essential characteristics of goal-directed whole-body motion. The report is organized into three major sections (Sections 2, 3, and 4). Section 2 reviews general themes from ecological psychology and control-systems engineering that are relevant to the perception and control of whole-body motion. These themes provide an organizational framework for analyzing the complex and interrelated phenomena that are the defining characteristics of whole-body motion. Section 3 of this report applies the organization framework from the first section to the problem of perception and control of aircraft motion. This is a familiar problem in control-systems engineering and ecological psychology. Section 4 examines an essential but generally neglected aspect of vehicular control: coordination of postural control and vehicular control. To facilitate presentation of this new idea, postural control and its coordination with vehicular control are analyzed in terms of conceptual categories that are familiar in the analysis of vehicular control.

  5. Coordination of baseload power plant group control with static reactive power compensator control

    Directory of Open Access Journals (Sweden)

    Zbigniew Szczerba

    2012-06-01

    Full Text Available Reactive power sources in power system nodes: generators and static reactive power compensators, are controlled by control systems. Generators – by generator node group controllers, compensators – by voltage controllers. The paper presents issues of these control systems’ coordination and proposals for its implementation.

  6. Acceleration of solving the dynamic multi-objective network design problem using response surface methods

    NARCIS (Netherlands)

    Wismans, L.J.J.; Berkum, van E.C.; Bliemer, M.C.J.

    2014-01-01

    Optimization of externalities and accessibility using dynamic traffic management measures on a strategic level is a specific example of solving a multi-objective network design problem. Solving this optimization problem is time consuming, because heuristics like evolutionary multi objective algorith

  7. The Aggregate Homotopy Method for Multi-objective Max-min Problems

    Directory of Open Access Journals (Sweden)

    He Li

    2011-03-01

    Full Text Available Multi-objective programming problem was transformed into a class of simple unsmooth single-objective programming problem by Max-min ways. After smoothing with aggregate function, a new homotopy mapping was constructed. The minimal weak efficient solution of the multi-objective optimization problem was obtained by path tracking. Numerical simulation confirmed the viability of this method.

  8. Optimizing municipal wastewater treatment plants using an improved multi-objective optimization method.

    Science.gov (United States)

    Zhang, Rui; Xie, Wen-Ming; Yu, Han-Qing; Li, Wen-Wei

    2014-04-01

    An improved multi-objective optimization (MOO) model was established and used for simultaneously optimizing the treatment cost and multiple effluent quality indexes (including effluent COD, NH4(+)-N, NO3(-)-N) of a municipal wastewater treatment plant (WWTP). Compared with previous models that were mainly based on the use of fixed decision factors and did not taken into account the treatment cost, this model introduces a relationship model based on back propagation algorithm to determine the set of decision factors according to the expected optimization targets. Thus, a more flexible and precise optimization of the treatment process was allowed. Moreover, a MOO of conflicting objectives (i.e., treatment cost and effluent quality) was achieved. Applying this method, an optimal balance between operating cost and effluent quality of a WWTP can be found. This model may offer a useful tool for optimized design and control of practical WWTPs.

  9. Using multi-objective optimization to design parameters in electro-discharge machining by wire

    Directory of Open Access Journals (Sweden)

    Carlos Alberto OCHOA

    2015-03-01

    Full Text Available The following paper describes the main objective to follow the methodology used and proposed to obtain the optimal values of WEDM process operation on the machine Robofil 310 by robust parameter design (RPD of Dr. G. Taguichi [TAGUCHI, G. 1993], through controllable factors which result in more inferences regarding the problem to noise signal (S / N, which for this study is the variability of the hardness of samples from 6061, also studied the behaviour of the output parameters as the material removal rate (MRR and surface roughness (Ra, subsequently took the RPD orthogonal array and characterized the individuals in the population, each optimal value is a gene and each possible solution is a chromosome, used multi-objective optimization using Non-dominated Sorting Genetic Algorithm to cross and mutate this population to generate better results MRR and Ra.

  10. Multi-Objective Optimization for Smart House Applied Real Time Pricing Systems

    Directory of Open Access Journals (Sweden)

    Yasuaki Miyazato

    2016-12-01

    Full Text Available A smart house generally has a Photovoltaic panel (PV, a Heat Pump (HP, a Solar Collector (SC and a fixed battery. Since the fixed battery can buy and store inexpensive electricity during the night, the electricity bill can be reduced. However, a large capacity fixed battery is very expensive. Therefore, there is a need to determine the economic capacity of fixed battery. Furthermore, surplus electric power can be sold using a buyback program. By this program, PV can be effectively utilized and contribute to the reduction of the electricity bill. With this in mind, this research proposes a multi-objective optimization, the purpose of which is electric demand control and reduction of the electricity bill in the smart house. In this optimal problem, the Pareto optimal solutions are searched depending on the fixed battery capacity. Additionally, it is shown that consumers can choose what suits them by comparing the Pareto optimal solutions.

  11. An adjustable slit mechanism for a fiber-fed multi-object spectrograph

    Science.gov (United States)

    Bailey, John I.; Mateo, Mario L.; Bagish, Alan P.; Crane, Jeffrey D.; Slater, Colin T.

    2012-09-01

    Fiber-fed multi-object spectrographs have greatly enhanced the spectroscopic capabilities of the world's premiere telescopes, but their flexibility has typically been limited by a fixed effective slit size that constrains the available resolving power. We present a novel mechanism that, for the first time, equips a fiber-fed spectrograph with multiple discreet slits of different widths. In this paper, we detail the mechanical design of our variable slit mechanism, which is capable of positioning any one of six slits in front of the fibers immediately prior to injection into the spectrograph's optical train. Further, we present the details of related systems necessary to achieve closed loop positioning of the slit mechanism given that no encoder is used. We also briefly discuss our use of open source and open hardware projects in the design. Finally, we describe the control system we have implemented for this subsystem.

  12. 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)

  13. A multi-object detection and tracking method in wireless video sensor networks

    Science.gov (United States)

    Chu, Zheng; Zhang, Jing; Zhuo, Li

    2012-04-01

    Most multi-object detection and tracking techniques suffer from the well-known "multi-object occlusion" problem. The abundant nodes of wireless video sensor networks (WVSNs) can be utilized to solve the problem, and the video nodes in WVSN have limited calculation capability and energy. In order to achieve effective multi-object tracking using WVSN, the main contributions of our proposed method are that: (1) the limits of field of view (FOV) of every video nodes are utilized to establish the consistent labeling of the objects in different views. (2) Mobile Agent is employed to communicate among network nodes, so the objects are assigned correct labels after multi-object occlusion. The performance of the approach has been demonstrated on real-world and the experimental results show that the proposed method is effective for resolving multi-object occlusions and meets the requirement of WVSN.

  14. Optimal design of the front linkage of a hydraulic excavator for multi-objective function

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Jong Won; Jung, Seung Min; Kim, Jong Won [Seoul National University, Seoul (Korea, Republic of); Kim, Jin Uk [Doosan Infracore, Incheon (Korea, Republic of); Seo, Tae Won [Yeungnam University, Gyeongsan (Korea, Republic of)

    2014-08-15

    The workspace, working velocity, excavating force, and load capacity of a hydraulic excavator play critical roles in the performance of the excavator for various tasks. This paper presents an optimal design of the front linkage of an excavator to maximize the performances of several indices simultaneously. A multi-objective function is defined to increase the excavator's workspace, working velocity, excavating force, and load capacity simultaneously. The workspace is defined by using four geometrical indices and the working velocity is defined by the amount of time needed to perform one cycle composed of digging and dumping. The excavating force consists of two forces, and the load capacity is defined by using the minimum values of three types with specific operations. A total of 10 indices define objective function with each weight, and pin-points of the front linkage are the design parameters, including joint positions of links and hydraulic actuators. A two-step optimization procedure is considered based on a new method called the hybrid Taguchi-random coordinate search algorithm. The results indicate a 3.43% increase in performance relative to the initial design parameters of a commercial excavator. More specifically, the excavator's workspace, working velocity, excavating force, and load capacity increase by 5.55%, 0.14%, 5.46%, and 0.33%, respectively. These improved design parameters can be applied to next generation excavators.

  15. Optimal design of coordination control strategy for distributed generation system

    Institute of Scientific and Technical Information of China (English)

    WANG Ai-hua; Norapon Kanjanapadit

    2009-01-01

    This paper presents a novel design procedure for optimizing the power distribution strategy in distributed generation system. A coordinating controller, responsible to distribute the total load power request among multiple DG units, is suggested based on the conception of hierarchical control structure in the dynamic system.The optimal control problem was formulated as a nonlinear optimization problem subject to set of constraints.The resulting problem was solved using the Kutm-Tucker method. Computer simulation results demonstrate that the proposed method can provide better efficiency in terms of reducing total costs compared to existing methods.In addition, the proposed optimal load distribution strategy can be easily implemented in real-time thanks to the simplicity of closed-form solutions.

  16. Coordinated control of wind power and energy storage

    DEFF Research Database (Denmark)

    Zhao, Haoran

    the coordinated control of wind power and ESS. Due to the different technical characteristics, such as power and energy density, ESS can play different roles either in generation-side, grid-side or demand side. This thesis focuses on the following two scenarios:• Scenario 1: As a part of wind farm, the ESS plays...... a generation-side role which aims to improve the grid-friendliness of the wind farm. • Scenario 2: As a part of microgrid, the ESS is used to efficiently accommodate the wind power fluctuation.Around the main objective, the relevant research fields including the wind turbine modeling and control, wind farm...... modeling and control, planning of ESS are also studied in this thesis. The implementation and validation of the International Electrotechnical Commission (IEC) generic Type 1A are presented in this thesis. It is shown that the implemented IEC generic Type 1 models in PowerFactory (PF) can represent...

  17. Communications for Coordinative Control of Wind Power Systems

    DEFF Research Database (Denmark)

    Wei, Mu

    system control strategies for wind power integration, in order to achieve coordinative control for a secure and efficient power system. The project basically contains three main aspects: studies on DGS (Distributed Generation System) characteristics, analysis of communication technologies......, and integration of power system and communication system. For the DGS studies, the main components, such as WTs (Wind Turbines) and CHPs (Combined Head and Power), are modelled. Since WTs are sensitive to disturbances, one focus of this PhD study is the investigation of WTs characteristics; mainly covering FSIG...... (Fixed Speed Induction Generator) and DFIG (Doubly-Fed Induction Generator) based wind turbine systems. Based on the study, the critical points to stabilize FSWTs (Fixed Speed Wind Turbines), after disturbances, are determined. This demands the latency requirements on the possible control and protection...

  18. 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.

  19. Interior High Frequency Noise Analysis of Heavy Vehicle Cab and Multi-Objective Optimization with Statistical Energy Analysis Method

    Science.gov (United States)

    Chen, Shuming; Wang, Lianhui; Song, Jiqang; Wang, Dengfeng; Chen, Jing

    The interior sound pressure levels of a commercial vehicle cab at the driver’s right ear position and head rest position are determined as evaluation indices of vehicle acoustic performances. A statistical energy analysis model of the commercial vehicle cab was created by using statistical energy analysis method. The simulated interior acoustic performance of the cab has a significant coincidence with the experimental results. A response surface model was presented to determine the relationship between sound package parameters and evaluation indices of the interior acoustic performance for the vehicle cab. A multi-objective optimization was performed by using NSGA II algorithm with weighting coefficient method. The presented method provides a new idea for the multi-objective optimization design of the acoustic performances in vehicle noise analysis and control field.

  20. Quantifying coordination and coordination variability in backward versus forward running: Implications for control of motion.

    Science.gov (United States)

    Mehdizadeh, Sina; Arshi, Ahmed Reza; Davids, Keith

    2015-07-01

    The aims of this study were to compare coordination and coordination variability in backward and forward running and to investigate the effects of speed on coordination variability in both backward and forward running. Fifteen healthy male participants took part in this study to run forwards and backwards on a treadmill at 80%, 100% and 120% of their preferred running speeds. The coordinate data of passive reflective markers attached to body segments were recorded using motion capture systems. Coordination of shank-foot and thigh-shank couplings in sagittal plane was quantified using the continuous relative phase method. Coordination variability was calculated as the standard deviation of a coordination pattern over 50 strides. Cross-correlation coefficients and associated phase shifts were determined to quantify similarity in coordination patterns between forward and backward running. Our results demonstrated that the coordination pattern in a gait cycle of backward running was in reverse to that of forward running at all speeds implying that the same neural circuitry is responsible for regulating both forward and backward running gaits. In addition, results demonstrated that there was an average of approximately 11% phase shift between the coordination patterns of backward and forward running which indicates that a single underlying mechanism might be responsible for generating motor patterns in both forward and backward running. Finally, backward running had significantly higher magnitude of coordination variability compared to forward running, signifying that more degrees of freedom were involved in backward running. Speed however, did not affect coordination variability in either task. Copyright © 2015 Elsevier B.V. All rights reserved.

  1. Robust coordinated control of a dual-arm space robot

    Science.gov (United States)

    Shi, Lingling; Kayastha, Sharmila; Katupitiya, Jay

    2017-09-01

    Dual-arm space robots are more capable of implementing complex space tasks compared with single arm space robots. However, the dynamic coupling between the arms and the base will have a serious impact on the spacecraft attitude and the hand motion of each arm. Instead of considering one arm as the mission arm and the other as the balance arm, in this work two arms of the space robot perform as mission arms aimed at accomplishing secure capture of a floating target. The paper investigates coordinated control of the base's attitude and the arms' motion in the task space in the presence of system uncertainties. Two types of controllers, i.e. a Sliding Mode Controller (SMC) and a nonlinear Model Predictive Controller (MPC) are verified and compared with a conventional Computed-Torque Controller (CTC) through numerical simulations in terms of control accuracy and system robustness. Both controllers eliminate the need to linearly parameterize the dynamic equations. The MPC has been shown to achieve performance with higher accuracy than CTC and SMC in the absence of system uncertainties under the condition that they consume comparable energy. When the system uncertainties are included, SMC and CTC present advantageous robustness than MPC. Specifically, in a case where system inertia increases, SMC delivers higher accuracy than CTC and costs the least amount of energy.

  2. Human Memory Limitations in Multi-Object Tracking.

    Science.gov (United States)

    1982-06-01

    Atkinson , R. C., & Shiffrin , R. M. Human memory : A proposed system and its control processes. In K. W. Spence & J. T. Spence (eds.), The psychology of...informa- tion-processing concepts of Norman (1968) and Atkinson and Shiffrin (1968), and from the "levels of processing" formulation of Craik and Lockhart...this selection. These strategies (referred to as "control processes" by Atkinson & Shiffrin , 1968) include the processes that direct attention among

  3. Multi-objective trajectory optimization for the space exploration vehicle

    Science.gov (United States)

    Qin, Xiaoli; Xiao, Zhen

    2016-07-01

    The research determines temperature-constrained optimal trajectory for the space exploration vehicle by developing an optimal control formulation and solving it using a variable order quadrature collocation method with a Non-linear Programming(NLP) solver. The vehicle is assumed to be the space reconnaissance aircraft that has specified takeoff/landing locations, specified no-fly zones, and specified targets for sensor data collections. A three degree of freedom aircraft model is adapted from previous work and includes flight dynamics, and thermal constraints.Vehicle control is accomplished by controlling angle of attack, roll angle, and propellant mass flow rate. This model is incorporated into an optimal control formulation that includes constraints on both the vehicle and mission parameters, such as avoidance of no-fly zones and exploration of space targets. In addition, the vehicle models include the environmental models(gravity and atmosphere). How these models are appropriately employed is key to gaining confidence in the results and conclusions of the research. Optimal trajectories are developed using several performance costs in the optimal control formation,minimum time,minimum time with control penalties,and maximum distance.The resulting analysis demonstrates that optimal trajectories that meet specified mission parameters and constraints can be quickly determined and used for large-scale space exloration.

  4. Constrained model predictive control, state estimation and coordination

    Science.gov (United States)

    Yan, Jun

    In this dissertation, we study the interaction between the control performance and the quality of the state estimation in a constrained Model Predictive Control (MPC) framework for systems with stochastic disturbances. This consists of three parts: (i) the development of a constrained MPC formulation that adapts to the quality of the state estimation via constraints; (ii) the application of such a control law in a multi-vehicle formation coordinated control problem in which each vehicle operates subject to a no-collision constraint posed by others' imperfect prediction computed from finite bit-rate, communicated data; (iii) the design of the predictors and the communication resource assignment problem that satisfy the performance requirement from Part (ii). Model Predictive Control (MPC) is of interest because it is one of the few control design methods which preserves standard design variables and yet handles constraints. MPC is normally posed as a full-state feedback control and is implemented in a certainty-equivalence fashion with best estimates of the states being used in place of the exact state. However, if the state constraints were handled in the same certainty-equivalence fashion, the resulting control law could drive the real state to violate the constraints frequently. Part (i) focuses on exploring the inclusion of state estimates into the constraints. It does this by applying constrained MPC to a system with stochastic disturbances. The stochastic nature of the problem requires re-posing the constraints in a probabilistic form. In Part (ii), we consider applying constrained MPC as a local control law in a coordinated control problem of a group of distributed autonomous systems. Interactions between the systems are captured via constraints. First, we inspect the application of constrained MPC to a completely deterministic case. Formation stability theorems are derived for the subsystems and conditions on the local constraint set are derived in order to

  5. Systems and Methods of Coordination Control for Robot Manipulation

    Science.gov (United States)

    Chang, Chu-Yin (Inventor); English, James (Inventor); Tardella, Neil (Inventor); Bacon, James (Inventor)

    2013-01-01

    Disclosed herein are systems and methods for controlling robotic apparatus having several movable elements or segments coupled by joints. At least one of the movable elements can include one or more mobile bases, while the others can form one or more manipulators. One of the movable elements can be treated as an end effector for which a certain motion is desired. The end effector may include a tool, for example, or represent a robotic hand (or a point thereon), or one or more of the one or more mobile bases. In accordance with the systems and methods disclosed herein, movement of the manipulator and the mobile base can be controlled and coordinated to effect a desired motion for the end effector. In many cases, the motion can include simultaneously moving the manipulator and the mobile base.

  6. Communications for Coordinative Control of Wind Power Systems

    DEFF Research Database (Denmark)

    Wei, Mu

    . The performances of FSWT and DFIG connected DGS are compared and analysed. At last, the cyber security study is presented, due to the important place of security in power system communications. A security domain model is proposed to guide the implementation of the security technologies. Cyber security related...... system control strategies for wind power integration, in order to achieve coordinative control for a secure and efficient power system. The project basically contains three main aspects: studies on DGS (Distributed Generation System) characteristics, analysis of communication technologies...... simulation results reveal the important impact of the security configuration on improving the performance of the associated electric power system data communication systems. This PhD study explores a new aspect of the investigations of wind power system components characteristics, from communication...

  7. On the Effect of Populations in Evolutionary Multi-Objective Optimisation

    DEFF Research Database (Denmark)

    Giel, Oliver; Lehre, Per Kristian

    2010-01-01

    Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. An important open problem is to understand the role of populations in MOEAs. We present two simple bi-objective problems which emphasise when populations are needed....... Rigorous runtime analysis points out an exponential runtime gap between the population-based algorithm Simple Evolutionary Multi-objective Optimiser (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the population-based MOEA...

  8. Wheel Torque Distribution of Four-Wheel-Drive Electric Vehicles Based on Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    Cheng Lin

    2015-04-01

    Full Text Available The wheel driving torque on four-wheel-drive electric vehicles (4WDEVs can be modulated precisely and continuously, therefore maneuverability and energy-saving control can be carried out at the same time. In this paper, a wheel torque distribution strategy is developed based on multi-objective optimization to improve vehicle maneuverability and reduce energy consumption. In the high-layer of the presented method, sliding mode control is used to calculate the desired yaw moment due to the model inaccuracy and parameter error. In the low-layer, mathematical programming with the penalty function consisting of the yaw moment control offset, the drive system energy loss and the slip ratio constraint is used for wheel torque control allocation. The programming is solved with the combination of off-line and on-line optimization to reduce the calculation cost, and the optimization results are sent to motor controllers as torque commands. Co-simulation based on MATLAB® and Carsim® proves that the developed strategy can both improve the vehicle maneuverability and reduce energy consumption.

  9. Pyranopterin Coordination Controls Molybdenum Electrochemistry in Escherichia coli Nitrate Reductase*

    Science.gov (United States)

    Wu, Sheng-Yi; Rothery, Richard A.; Weiner, Joel H.

    2015-01-01

    We test the hypothesis that pyranopterin (PPT) coordination plays a critical role in defining molybdenum active site redox chemistry and reactivity in the mononuclear molybdoenzymes. The molybdenum atom of Escherichia coli nitrate reductase A (NarGHI) is coordinated by two PPT-dithiolene chelates that are defined as proximal and distal based on their proximity to a [4Fe-4S] cluster known as FS0. We examined variants of two sets of residues involved in PPT coordination: (i) those interacting directly or indirectly with the pyran oxygen of the bicyclic distal PPT (NarG-Ser719, NarG-His1163, and NarG-His1184); and (ii) those involved in bridging the two PPTs and stabilizing the oxidation state of the proximal PPT (NarG-His1092 and NarG-His1098). A S719A variant has essentially no effect on the overall Mo(VI/IV) reduction potential, whereas the H1163A and H1184A variants elicit large effects (ΔEm values of −88 and −36 mV, respectively). Ala variants of His1092 and His1098 also elicit large ΔEm values of −143 and −101 mV, respectively. An Arg variant of His1092 elicits a small ΔEm of +18 mV on the Mo(VI/IV) reduction potential. There is a linear correlation between the molybdenum Em value and both enzyme activity and the ability to support anaerobic respiratory growth on nitrate. These data support a non-innocent role for the PPT moieties in controlling active site metal redox chemistry and catalysis. PMID:26297003

  10. Neurobiology: reconstructing the neural control of leg coordination.

    Science.gov (United States)

    Zill, Sasha N; Keller, Bridget R

    2009-05-12

    Walking is adaptable because the timing of movements of individual legs can be varied while maintaining leg coordination. Recent work in stick insects shows that leg coordination set by interactions of pattern generating circuits can be overridden by sensory feedback.

  11. 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.

  12. Automatic Multi-Level Thresholding Segmentation Based on Multi-Objective Optimization

    Directory of Open Access Journals (Sweden)

    L. DJEROU,

    2012-01-01

    Full Text Available In this paper, we present a new multi-level image thresholding technique, called Automatic Threshold based on Multi-objective Optimization "ATMO" that combines the flexibility of multi-objective fitness functions with the power of a Binary Particle Swarm Optimization algorithm "BPSO", for searching the "optimum" number of the thresholds and simultaneously the optimal thresholds of three criteria: the between-class variances criterion, the minimum error criterion and the entropy criterion. Some examples of test images are presented to compare our segmentation method, based on the multi-objective optimization approach with Otsu’s, Kapur’s and Kittler’s methods. Our experimental results show that the thresholding method based on multi-objective optimization is more efficient than the classical Otsu’s, Kapur’s and Kittler’s methods.

  13. 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...

  14. Sensitivity analysis of multi-objective optimization of CPG parameters for quadruped robot locomotion

    Science.gov (United States)

    Oliveira, Miguel; Santos, Cristina P.; Costa, Lino

    2012-09-01

    In this paper, a study based on sensitivity analysis is performed for a gait multi-objective optimization system that combines bio-inspired Central Patterns Generators (CPGs) and a multi-objective evolutionary algorithm based on NSGA-II. In this system, CPGs are modeled as autonomous differential equations, that generate the necessary limb movement to perform the required walking gait. In order to optimize the walking gait, a multi-objective problem with three conflicting objectives is formulated: maximization of the velocity, the wide stability margin and the behavioral diversity. The experimental results highlight the effectiveness of this multi-objective approach and the importance of the objectives to find different walking gait solutions for the quadruped robot.

  15. Multi-objective Optimization of Process Performances when Cutting Carbon Steel with Abrasive Water Jet

    Directory of Open Access Journals (Sweden)

    M. Radovanović

    2016-12-01

    Full Text Available Multi-objective optimization of process performances (perpendicularity deviation, surface roughness and productivity when cutting carbon steel EN S235 with abrasive water jet is presented in this paper. Cutting factors (abrasive flow rate, traverse rate and standoff distance were determined when perpendicularity deviation and surface roughness are minimal and productivity is maximal. Multi-objective genetic algorithm (MOGA was used for the determination set of nondominated optimal points, known as Pareto front.

  16. Fuzzy Multi-Objective Decision Model of Supplier Selection with Preference Information

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Supplier selection is a multi-objective decision problem, which must be considered many objectives, someobjectives are qualitative, and others are quantitative. Meanwhile, manufacturer has preference for different suppliers.In this paper, a new multi-objective decision model with preference information of supplier is established. A practicalexample of supplier selection problem utilizing this model is studied. The result demonstrates the feasibility andeffectiveness of the methods proposed in the paper.

  17. 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.

  18. A Multi-Objective Optimal Evolutionary Algorithm Based on Tree-Ranking

    Institute of Scientific and Technical Information of China (English)

    Shi Chuan; Kang Li-shan; Li Yan; Yan Zhen-yu

    2003-01-01

    Multi-objective optimal evolutionary algorithms (MOEAs) are a kind of new effective algorithms to solve Multi-objective optimal problem (MOP). Because ranking, a method which is used by most MOEAs to solve MOP, has some shortcoming s, in this paper, we proposed a new method using tree structure to express the relationship of solutions. Experiments prove that the method can reach the Pare to front, retain the diversity of the population, and use less time.

  19. Simulation and experimental validation of powertrain mounting bracket design obtained from multi-objective topology optimization

    OpenAIRE

    Qinghai Zhao; Xiaokai Chen; Lu Wang; Jianfeng Zhu; Zheng-Dong Ma; Yi Lin

    2015-01-01

    A framework of multi-objective topology optimization for vehicle powertrain mounting bracket design with consideration of multiple static and dynamic loading conditions is developed in this article. Incorporating into the simplified isotropic material with penalization model, compromise programming method is employed to describe the multi-objective and multi-stiffness topology optimization under static loading conditions, whereas mean eigenvalue formulation is proposed to analyze vibration op...

  20. Application of a fast and elitist multi-objective genetic algorithm to Reactive Power Dispatch

    OpenAIRE

    2009-01-01

    This paper presents an Elitist Non-Dominated Sorting Genetic Algorithm version II (NSGA-II), for solving the Reactive Power Dispatch (RPD) problem. The optimal RPD problem is a nonlinear constrained multi-objective optimization problem where the real power loss and the bus voltage deviations are to be minimized. Since the problem is treated as a true multi-objective optimization problem, different trade-off solutions are provided. The decision maker has an option to choose a solution among th...

  1. Genetic Algorithm-Based Multi-objective Optimisation for QoS-Aware Web Services Composition

    Science.gov (United States)

    Li, Li; Yang, Pengyi; Ou, Ling; Zhang, Zili; Cheng, Peng

    Finding an optimal solution for QoS-aware Web service composition with various restrictions on qualities is a multi-objective optimisation problem. A popular multi-objective genetic algorithm, NSGA-II, is studied in order to provide a set of optimal solutions for QoS-based service composition. Experiments with different numbers of abstract and concrete services confirm the expected behaviour of the algorithm.

  2. Controlling the Emission of Electromagnetic Sources by Coordinate transformation

    CERN Document Server

    Luo, Yu; Ran, Lixin; Chen, Hongsheng; Kong, Jin Au

    2007-01-01

    The coordinate transformation on the space that contains electromagnetic sources is studied. We find that, not only the permittivity and permeability tensors of the media, but also the sources inside the media will take another form in order to behave equivalently as the original case. It is demonstrated that, a source of arbitrary shape and position in the free space can be replaced by an appropriately designed metamaterial coating with current distributed on the inner surface and would not be detected by outer observers, because the emission of the source can be controlled at will in this way. As examples, we show how to design conformal antennas by covering the sources with transformation media. The method proposed in this letter provides a completely new approach to develop novel active EM devices.

  3. An adaptive evolutionary multi-objective approach based on simulated annealing.

    Science.gov (United States)

    Li, H; Landa-Silva, D

    2011-01-01

    A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.

  4. Transactive Control and Coordination of Distributed Assets for Ancillary Services

    Energy Technology Data Exchange (ETDEWEB)

    Subbarao, Krishnappa [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Fuller, Jason C. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Kalsi, Karanjit [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Somani, Abhishek [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Pratt, Robert G. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Widergren, Steven E. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Chassin, David P. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2013-09-18

    The need to diversify energy supplies, the need to mitigate energy-related environmental impact, and the entry of electric vehicles in large numbers present challenges and opportunities to power system professionals. Wind and solar power provide many benefits, and to reap the benefits the resulting increased variability—forecasted as well as unforecasted—should be addressed. A majority of states and the District of Columbia, representing over half of the total load, have passed renewable portfolio standards. California’s plans call for 33% renewable energy by 2020. For grid balancing and for meeting reliability standards, ancillary services are needed. The needs for these services are poised to increase significantly. Demand resources are receiving increasing attention as one means of providing the ancillary services. Control and coordination of a large number (~millions) of distributed smart grid assets requires innovative approaches. One such approach is transactive control and coordination (TC2)—a distributed, hierarchical, agent-based incentive and control system. The TC2 paradigm is to create a market-like control system in which participation is voluntary and the participant sets the price for participation. For transactions that are frequent, automation of bids and responses is necessary. Such an approach has been developed and demonstrated at the Pacific Northwest National Laboratory. The devices, typically thermostatically controlled heating, ventilating, and air conditioning (HVAC) loads, send their bids—the quantity of energy they need and, based on the consumer preferences encoded in a simple user interface, the price they are willing to pay. The typical bid period is 5 minutes. By aggregating all the bids, a demand curve is generated by the aggregating entity, and matched with a supply curve or supply constraint. The aggregator transmits the clearing price to the devices. The winning devices proceed to consume the energy they bid for and won

  5. Bimanual motor coordination controlled by cooperative interactions in intrinsic and extrinsic coordinates.

    Science.gov (United States)

    Sakurada, Takeshi; Ito, Koji; Gomi, Hiroaki

    2016-01-01

    Although strong motor coordination in intrinsic muscle coordinates has frequently been reported for bimanual movements, coordination in extrinsic visual coordinates is also crucial in various bimanual tasks. To explore the bimanual coordination mechanisms in terms of the frame of reference, here we characterized implicit bilateral interactions in visuomotor tasks. Visual perturbations (finger-cursor gain change) were applied while participants performed a rhythmic tracking task with both index fingers under an in-phase or anti-phase relationship in extrinsic coordinates. When they corrected the right finger's amplitude, the left finger's amplitude unintentionally also changed [motor interference (MI)], despite the instruction to keep its amplitude constant. Notably, we observed two specificities: one was large MI and low relative-phase variability (PV) under the intrinsic in-phase condition, and the other was large MI and high PV under the extrinsic in-phase condition. Additionally, using a multiple-interaction model, we successfully decomposed MI into intrinsic components caused by motor correction and extrinsic components caused by visual-cursor mismatch of the right finger's movements. This analysis revealed that the central nervous system facilitates MI by combining intrinsic and extrinsic components in the condition with in-phases in both intrinsic and extrinsic coordinates, and that under-additivity of the effects is explained by the brain's preference for the intrinsic interaction over extrinsic interaction. In contrast, the PV was significantly correlated with the intrinsic component, suggesting that the intrinsic interaction dominantly contributed to bimanual movement stabilization. The inconsistent features of MI and PV suggest that the central nervous system regulates multiple levels of bilateral interactions for various bimanual tasks.

  6. Prediction and optimization of fuel cell performance using a multi-objective genetic algorithm

    Directory of Open Access Journals (Sweden)

    Gustavo Marques Hobold, Ramesh K. Agarwal

    2013-01-01

    Full Text Available The attention that is currently being given to the emission of pollutant gases in the atmosphere has made the fuel cell (FC, an energy conversion device that cleanly converts chemical energy into electrical energy, a good alternative to other technologies that still use carbon-based fuels. The temperature plays an important role on the efficiency of an FC as it influences directly the humidity of the membrane, the reversible thermodynamic potential and the partial pressure of water; therefore the thermal control of the fuel cell is the focus of this paper. We present models for both high and low temperature fuel cells based on the solid-oxide fuel cell (SOFC and the polymer electrolyte membrane fuel cell (PEMFC. A thermodynamic analysis is performed on the cells and the methods of controlling their temperature are discussed. The cell parameters are optimized for both high and low temperatures using a Java-based multi-objective genetic algorithm, which makes use of the logic of the biological theory of evolution to classify individual parameters based on a fitness function in order to maximize the power of the fuel cell. Applications to high and low temperature fuel cells are discussed.

  7. New Multi-objective Uncertainty-based Algorithm for Water Resource Models' Calibration

    Science.gov (United States)

    Keshavarz, Kasra; Alizadeh, Hossein

    2017-04-01

    Water resource models are powerful tools to support water management decision making process and are developed to deal with a broad range of issues including land use and climate change impacts analysis, water allocation, systems design and operation, waste load control and allocation, etc. These models are divided into two categories of simulation and optimization models whose calibration has been addressed in the literature where great relevant efforts in recent decades have led to two main categories of auto-calibration methods of uncertainty-based algorithms such as GLUE, MCMC and PEST and optimization-based algorithms including single-objective optimization such as SCE-UA and multi-objective optimization such as MOCOM-UA and MOSCEM-UA. Although algorithms which benefit from capabilities of both types, such as SUFI-2, were rather developed, this paper proposes a new auto-calibration algorithm which is capable of both finding optimal parameters values regarding multiple objectives like optimization-based algorithms and providing interval estimations of parameters like uncertainty-based algorithms. The algorithm is actually developed to improve quality of SUFI-2 results. Based on a single-objective, e.g. NSE and RMSE, SUFI-2 proposes a routine to find the best point and interval estimation of parameters and corresponding prediction intervals (95 PPU) of time series of interest. To assess the goodness of calibration, final results are presented using two uncertainty measures of p-factor quantifying percentage of observations covered by 95PPU and r-factor quantifying degree of uncertainty, and the analyst has to select the point and interval estimation of parameters which are actually non-dominated regarding both of the uncertainty measures. Based on the described properties of SUFI-2, two important questions are raised, answering of which are our research motivation: Given that in SUFI-2, final selection is based on the two measures or objectives and on the other

  8. Optimal air quality policies and health: a multi-objective nonlinear approach.

    Science.gov (United States)

    Relvas, Helder; Miranda, Ana Isabel; Carnevale, Claudio; Maffeis, Giuseppe; Turrini, Enrico; Volta, Marialuisa

    2017-05-01

    The use of modelling tools to support decision-makers to plan air quality policies is now quite widespread in Europe. In this paper, the Regional Integrated Assessment Tool (RIAT+), which was designed to support policy-maker decision on optimal emission reduction measures to improve air quality at minimum costs, is applied to the Porto Urban Area (Portugal). In addition to technological measures, some local measures were included in the optimization process. Case study results are presented for a multi-objective approach focused on both NO2 and PM10 control measures, assuming equivalent importance in the optimization process. The optimal set of air quality measures is capable to reduce simultaneously the annual average concentrations values of PM10 and NO2 in 1.7 and 1.0 μg/m(3), respectively. This paper illustrates how the tool could be used to prioritize policy objectives and help making informed decisions about reducing air pollution and improving public health.

  9. Multi-objective reservoir operation during flood season considering spillway optimization

    Science.gov (United States)

    Liu, Xinyuan; Chen, Lu; Zhu, Yonghui; Singh, Vijay P.; Qu, Geng; Guo, Xiaohu

    2017-09-01

    Flood control and hydropower generation are two main functions of Three Gorges Reservoir (TGR) in China. In this study, a multi-objective operation model for TGR considering these two functions was developed. Since the optimal results of reservoir operation are mostly in the form of gross outflow which is hardly used to directly guide reservoir operation, the optimization of spillways operation was taken into account. For observed historical flood hydrographs and design flood hydrographs, the progressive optimality algorithm (POA) was employed to determine the optimal operation of spillways. For the real-time reservoir operation, a smooth support vector machine (SSVM) model was applied to abstract the optimal operation rules which consider the order and the number of spillways put into use. Results demonstrate that the use of different spillways has a significant impact on reservoir operation. Therefore, it is necessary to consider the order and number of spillways that should be used. Instead of optimizing outflow, direct optimization of the order and number of spillways can yield most reasonable results. The SSVM model simulates the relationship among inflow, water level and outflow satisfactorily and can be used for real-time or short term reservoir operation. Application of the SSVM model can also reduce flood risk and increase hydropower generation during the flood season.

  10. Improving Genetic Algorithm to Solve Multi-objectives Optimal of Upgrading Infrastructure in NGWN

    Directory of Open Access Journals (Sweden)

    Dac-Nhuong Le

    2013-11-01

    Full Text Available A problem of upgrading to the Next Generation Wireless Network (NGWN is backward compatibility with pre-existing networks, the cost and operational benefit of gradually enhancing networks, by replacing, upgrading and installing new wireless network infrastructure elements that can accommodate both voice and data demand. In this paper, I propose a new genetic algorithm based on a combination of two populations to solve multi-objective optimization infrastructure upgrade problem in NGWN. Network topology model has two levels in which mobile users are sources and both base stations and base station controllers are concentrators. My objective function is the costs of connection from sources to concentrators such as the cost of the installation, connection, replacement, and capacity upgrade of infrastructure equipment. I generate two populations satisfies constraints and combine its to build solutions and evaluate the performance of my algorithm with data randomly generated. The experimental results show that this approach is appropriate and effective Finally, I have applied this algorithm to planning of upgrade infrastructure in telecommunication networks in Haiphong city.

  11. Multi-objective optimisation with stochastic discrete-event simulation in retail banking: a case study

    Directory of Open Access Journals (Sweden)

    E Scholtz

    2012-12-01

    Full Text Available The cash management of an autoteller machine (ATM is a multi-objective optimisation problem which aims to maximise the service level provided to customers at minimum cost. This paper focus on improved cash management in a section of the South African retail banking industry, for which a decision support system (DSS was developed. This DSS integrates four Operations Research (OR methods: the vehicle routing problem (VRP, the continuous review policy for inventory management, the knapsack problem and stochastic, discrete-event simulation. The DSS was applied to an ATM network in the Eastern Cape, South Africa, to investigate 90 different scenarios. Results show that the application of a formal vehicle routing method consistently yields higher service levels at lower cost when compared to two other routing approaches, in conjunction with selected ATM reorder levels and a knapsack-based notes dispensing algorithm. It is concluded that the use of vehicle routing methods is especially beneficial when the bank has substantial control over transportation cost.

  12. Effective multi-objective optimization with the coral reefs optimization algorithm

    Science.gov (United States)

    Salcedo-Sanz, S.; Pastor-Sánchez, A.; Portilla-Figueras, J. A.; Prieto, L.

    2016-06-01

    In this article a new algorithm for multi-objective optimization is presented, the Multi-Objective Coral Reefs Optimization (MO-CRO) algorithm. The algorithm is based on the simulation of processes in coral reefs, such as corals' reproduction and fight for space in the reef. The adaptation to multi-objective problems is a process based on domination or non-domination during the process of fight for space in the reef. The final MO-CRO is an easily-implemented and fast algorithm, simple and robust, since it is able to keep diversity in the population of corals (solutions) in a natural way. The experimental evaluation of this new approach for multi-objective optimization problems is carried out on different multi-objective benchmark problems, where the MO-CRO has shown excellent performance in cases with limited computational resources, and in a real-world problem of wind speed prediction, where the MO-CRO algorithm is used to find the best set of features to predict the wind speed, taking into account two objective functions related to the performance of the prediction and the computation time of the regressor.

  13. 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.

  14. Multi-objective optimization of empirical hydrological model for streamflow prediction

    Science.gov (United States)

    Guo, Jun; Zhou, Jianzhong; Lu, Jiazheng; Zou, Qiang; Zhang, Huajie; Bi, Sheng

    2014-04-01

    Traditional calibration of hydrological models is performed with a single objective function. Practical experience with the calibration of hydrologic models reveals that single objective functions are often inadequate to properly measure all of the characteristics of the hydrologic system. To circumvent this problem, in recent years, a lot of studies have looked into the automatic calibration of hydrological models with multi-objective functions. In this paper, the multi-objective evolution algorithm MODE-ACM is introduced to solve the multi-objective optimization of hydrologic models. Moreover, to improve the performance of the MODE-ACM, an Enhanced Pareto Multi-Objective Differential Evolution algorithm named EPMODE is proposed in this research. The efficacy of the MODE-ACM and EPMODE are compared with two state-of-the-art algorithms NSGA-II and SPEA2 on two case studies. Five test problems are used as the first case study to generate the true Pareto front. Then this approach is tested on a typical empirical hydrological model for monthly streamflow forecasting. The results of these case studies show that the EPMODE, as well as MODE-ACM, is effective in solving multi-objective problems and has great potential as an efficient and reliable algorithm for water resources applications.

  15. A multi-objective genetic algorithm model for time-cost trade-off analysis of construction projects

    OpenAIRE

    Senouci, Ahmed; Al-Derham, Hassan R.

    2006-01-01

    Time-cost trade-off analysis is one of the most important aspects of construction project planning and control. There are trade-offs between time and cost to complete the activities of a project. Existing methods for time-cost trade-off analysis are not efficient enough to solve large-scale CPM networks (hundreds of activities or more). This paper presents an advanced and robust multi-objective genetic algorithm model for the time-cost trade-off analysis of construction projects. The model al...

  16. Transactive Control and Coordination of Distributed Assets for Ancillary Services

    Energy Technology Data Exchange (ETDEWEB)

    Subbarao, Krishnappa; Fuller, Jason C.; Kalsi, Karanjit; Somani, Abhishek; Pratt, Robert G.; Widergren, Steven E.; Chassin, David P.

    2013-09-18

    The need to diversify energy supplies, the need to mitigate energy-related environmental impact, and the entry of electric vehicles in large numbers present challenges and opportunities to power system professionals. Wind and solar power provide many benefits, and to reap the benefits the resulting increased variability—forecasted as well as unforecasted—should be addressed. Demand resources are receiving increasing attention as one means of providing the grid balancing services. Control and coordination of a large number (~millions) of distributed smart grid assets requires innovative approaches. One such is transactive control and coordination (TC2)—a distributed, agent-based incentive and control system. The TC2 paradigm is to create a market system with the following characteristics: • Participation should be entirely voluntary. • The participant decides at what price s/he is willing to participate. • The bids and responses are automated. Such an approach has been developed and demonstrated by Pacific Northwest National Laboratory for energy markets. It is the purpose of this project to develop a similar approach for ancillary services. In this report, the following ancillary services are considered: • spinning reserve • ramping • regulation. These services are to be provided by the following devices: • refrigerators • water heaters • clothes dryers • variable speed drives. The important results are summarized below: The regulation signal can be divided into an energy-neutral high frequency component and a low frequency component. The high frequency component is particularly well suited for demand resources. The low frequency component, which carries energy non-neutrality, can be handled by a combination of generators and demand resources. An explicit method for such a separation is obtained from an exponentially weighted moving average filter. Causal filters (i.e., filters that process only present and past values of a signal

  17. Developing a taxonomy of coordination behaviours in nuclear power plant control rooms during emergencies.

    Science.gov (United States)

    Wang, Dunxing; Gao, Qin; Li, Zhizhong; Song, Fei; Ma, Liang

    2017-05-19

    This study aims to develop a taxonomy of coordination behaviours during emergencies in nuclear power plants (NPPs). We summarised basic coordination behaviours from literature in aviation, health care and nuclear field and identified coordination behaviours specific to the nuclear domain by interviewing and surveying control crew operators. The established taxonomy includes 7 workflow stages and 24 basic coordination behaviours. To evaluate the reliability and feasibility of the taxonomy, we analysed 12 videos of operators' training sessions by coding coordination behaviours with the taxonomy and the inter-rater reliability was acceptable. Further analysis of the frequency, the duration and the direction of the coordination behaviours revealed four coordination problems. This taxonomy provides a foundation of systematic observation of coordination behaviours among NPP crews, advances researchers' understanding of the coordination mechanism during emergencies in NPPs and facilitate the possibility to deepen the understanding of the relationships between coordination behaviours and team performance. Practitioner Summary: A taxonomy of coordination behaviours during emergencies in nuclear power plants was developed. Reliability and feasibility of the taxonomy was verified through the analysis of 12 training sessions. The taxonomy can serve as an observation system for analysis of coordination behaviours and help to identify coordination problems of control crews.

  18. A coordinated MIMO control design for a power plant using improved sliding mode controller.

    Science.gov (United States)

    Ataei, Mohammad; Hooshmand, Rahmat-Allah; Samani, Siavash Golmohammadi

    2014-03-01

    For the participation of the steam power plants in regulating the network frequency, boilers and turbines should be co-ordinately controlled in addition to the base load productions. Lack of coordinated control over boiler-turbine may lead to instability; oscillation in producing power and boiler parameters; reduction in the reliability of the unit; and inflicting thermodynamic tension on devices. This paper proposes a boiler-turbine coordinated multivariable control system based on improved sliding mode controller (ISMC). The system controls two main boiler-turbine parameters i.e., the turbine revolution and superheated steam pressure of the boiler output. For this purpose, a comprehensive model of the system including complete and exact description of the subsystems is extracted. The parameters of this model are determined according to our case study that is the 320MW unit of Islam-Abad power plant in Isfahan/Iran. The ISMC method is simulated on the power plant and its performance is compared with the related real PI (proportional-integral) controllers which have been used in this unit. The simulation results show the capability of the proposed controller system in controlling local network frequency and superheated steam pressure in the presence of load variations and disturbances of boiler.

  19. Fuzzy Coordinated PI Controller: Application to the Real-Time Pressure Control Process

    Directory of Open Access Journals (Sweden)

    N. Kanagaraj

    2008-01-01

    Full Text Available This paper presents the real-time implementation of a fuzzy coordinated classical PI control scheme for controlling the pressure in a pilot pressure tank system. The fuzzy system has been designed to track the variation parameters in a feedback loop and tune the classical controller to achieve a better control action for load disturbances and set point changes. The error and process inputs are chosen as the inputs of fuzzy system to tune the conventional PI controller according to the process condition. This online conventional controller tuning technique will reduce the human involvement in controller tuning and increase the operating range of the conventional controller. The proposed control algorithm is experimentally implemented for the real-time pressure control of a pilot air tank system and validated using a high-speed 32-bit ARM7 embedded microcontroller board (ATMEL AT91M55800A. To demonstrate the performance of the fuzzy coordinated PI control scheme, results are compared with a classical PI and PI-type fuzzy control method. It is observed that the proposed controller structure is able to quickly track the parameter variation and perform better in load disturbances and also for set point changes.

  20. Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing.

    Directory of Open Access Journals (Sweden)

    Ahmad Abubaker

    Full Text Available This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, "MOPSOSA". The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO and the Multi-Objective Simulated Annealing (MOSA. Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets.

  1. Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing.

    Science.gov (United States)

    Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud

    2015-01-01

    This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, "MOPSOSA". The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets.

  2. 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.)

  3. CONCEPTUAL FRAMEWORK OF MULTI-OBJECTIVE PLANNING WITH A CASE STUDY

    Directory of Open Access Journals (Sweden)

    Mehmet Mısır

    2005-04-01

    Full Text Available Forests management design of the day focuses on protection as well as the sustainable use of forest values. The basic requirement of multi-objective forest management planning is identify and quantify forest values and to determine management objectives. The priorities of management objectives, however, must be decided. Decision support tools such as operation research techniques and GIS, therefore, have effectively been used in management planning process over the last decade. Designing spatial data base including graphical data such as stand map, soil map, road map and attribute data such as stand volume, increment, number of trees and determining forest values are necessary steps for preparing a comprehensive forest management plan. This study aims to; establish conceptual framework of Multi-objective planning and prepare forest values maps necessary for management planning by using multi-objective planning models.

  4. 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.

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

    Science.gov (United States)

    Trager, S. C.

    2016-10-01

    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 driving the field of large-scale multi-object spectroscopic surveys; (2) statistics is important to drive conclusions, but inference is as or even more important; (3) multi-wavelength surveys are necessary, particularly for understanding galaxies and cosmology; and (4) a large number of new multi-object spectrographs at a wide variety of wavelengths are either already here or will rapidly be available. This conference shows that we are just learning how to get the most (astrophysics) out of these instruments.

  6. Multi-Objective Feature Subset Selection using Non-dominated Sorting Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    A. Khan

    2015-02-01

    Full Text Available This paper presents an evolutionary algorithm based technique to solve multi-objective feature subset selection problem. The data used for classification contains large number of features called attributes. Some of these attributes are not relevant and needs to be eliminated. In classification procedure, each feature has an effect on the accuracy, cost and learning time of the classifier. So, there is a strong requirement to select a subset of the features before building the classifier. This proposed technique treats feature subset selection as multi-objective optimization problem. This research uses one of the latest multi-objective genetic algorithms (NSGA - II. The fitness value of a particular feature subset is measured by using ID3. The testing accuracy acquired is then assigned to the fitness value. This technique is tested on several datasets taken from the UCI machine repository. The experiments demonstrate the feasibility of using NSGA-II for feature subset selection.

  7. A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios

    Science.gov (United States)

    Yue, Wei; Wang, Yuping

    2017-01-01

    Due to the important effect of the higher order moments to portfolio returns, the aim of this paper is to make use of the third and fourth moments for fuzzy multi-objective portfolio selection model. Firstly, in order to overcome the low diversity of the obtained solution set and lead to corner solutions for the conventional higher moment portfolio selection models, a new entropy function based on Minkowski measure is proposed as a new objective function and a novel fuzzy multi-objective weighted possibilistic higher order moment portfolio model is presented. Secondly, to solve the proposed model efficiently, a new multi-objective evolutionary algorithm is designed. Thirdly, several portfolio performance evaluation techniques are used to evaluate the performance of the portfolio models. Finally, some experiments are conducted by using the data of Shanghai Stock Exchange and the results indicate the efficiency and effectiveness of the proposed model and algorithm.

  8. 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 ...

  9. Multi-objective metaheuristics for preprocessing EEG data in brain-computer interfaces

    Science.gov (United States)

    Aler, Ricardo; Vega, Alicia; Galván, Inés M.; Nebro, Antonio J.

    2012-03-01

    In the field of brain-computer interfaces, one of the main issues is to classify the electroencephalogram (EEG) accurately. EEG signals have a good temporal resolution, but a low spatial one. In this article, metaheuristics are used to compute spatial filters to improve the spatial resolution. Additionally, from a physiological point of view, not all frequency bands are equally relevant. Both spatial filters and relevant frequency bands are user-dependent. In this article a multi-objective formulation for spatial filter optimization and frequency-band selection is proposed. Several multi-objective metaheuristics have been tested for this purpose. The experimental results show, in general, that multi-objective algorithms are able to select a subset of the available frequency bands, while maintaining or improving the accuracy obtained with the whole set. Also, among the different metaheuristics tested, GDE3, which is based on differential evolution, is the most useful algorithm in this context.

  10. Two-phase application of multi-objective genetic algorithms in green building design

    Energy Technology Data Exchange (ETDEWEB)

    Wang, W.; Zmeureanu, R. [Concordia Univ., Centre for Building Studies, Montreal, PQ (Canada). Dept. of Building, Civil and Environmental Engineering; Rivard, H. [Ecole de Technologie Superieure, Montreal, PQ (Canada). Dept. of Construction Engineering

    2005-07-01

    The application of multi-objective genetic algorithms for green building design in two phases were presented in order to better help designers in the decision-making process. The purpose is to minimize two conflicting criteria: the life-cycle cost and the life-cycle environmental impact. Environmental impact criteria examined include energy and non-energy natural resources, global warming, and acidification. Variables focus on building envelope-related parameters. The application of multi-objective genetic algorithms is divided into two phases. The first phase intends to help designers in understanding the trade-off relationship between the two conflicting criteria. The second phase intends to refine the performance region that is of the designer's interest. The results after the two-phase application of the multi objective genetic algorithm were then presented. 13 refs., 4 tabs., 3 figs.

  11. Multi-Objective Optimization of Mechanical Running Conditions of Large Scale Statically Indeterminate Rotary Kiln

    Institute of Scientific and Technical Information of China (English)

    Hu Xiaoping; Xiao Yougang; Wang Guangbin

    2006-01-01

    Combined with the second rotary kiln of Alumina Factory in Great Wall Aluminum Company, the mechanics characteristics of statically indeterminate large-scale rotary kiln with variable cross-sections is analyzed. In order to adjusting the runing axis of rotary kiln, taking the force equilibrium of the rollers and the minimum of relative axis deflection as the optimization goal, the multi-objective optimization model of mechanical running conditions of kiln rotary is set up. The mechanical running conditions of the second rotary kiln after multi-objective optimization adjustment are compared with those before adjustment and after routine adjustment. It shows that multi-objective optimization adjustment can make axis as direct as possible and can distribute kiln loads equally.

  12. Application of a fast and elitist multi-objective genetic algorithm to Reactive Power Dispatch

    Directory of Open Access Journals (Sweden)

    Subramanian Ramesh

    2009-01-01

    Full Text Available This paper presents an Elitist Non-Dominated Sorting Genetic Algorithm version II (NSGA-II, for solving the Reactive Power Dispatch (RPD problem. The optimal RPD problem is a nonlinear constrained multi-objective optimization problem where the real power loss and the bus voltage deviations are to be minimized. Since the problem is treated as a true multi-objective optimization problem, different trade-off solutions are provided. The decision maker has an option to choose a solution among the different trade-off solutions provided in the pareto-optimal front. The standard IEEE 30-bus test system is used and the results show the effectiveness of NSGA-II and confirm its potential to solve the multi-objective RPD problem. The results obtained by NSGA-II are compared and validated with conventional weighted sum method using Real-coded Genetic Algorithm (RGA and NSGA.

  13. 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.

  14. Greedy Set Cover Field Selection for Multi-object Spectroscopy in C++ MPI

    Science.gov (United States)

    Stenborg, T. N.

    2015-09-01

    Multi-object spectrographs allow efficient observation of clustered targets. Observational programs of many targets not encompassed within a telescope's field of view, however, require multiple pointings. Here, a greedy set cover algorithmic approach to efficient field selection in such a scenario is examined. The goal of this approach is not to minimize the total number of pointings needed to cover a given target set, but rather maximize the observational return for a restricted number of pointings. Telescope field of view and maximum targets per field are input parameters, allowing algorithm application to observation planning for the current range of active multi-object spectrographs (e.g. the 2dF/AAOmega, Fiber Large Array Multi Element Spectrograph, Fiber Multi-Object Spectrograph, Hectochelle, Hectospec and Hydra systems), and for any future systems. A parallel version of the algorithm is implemented with the message passing interface, facilitating execution on both shared and distributed memory systems.

  15. Multi Objective Optimization Using Biogeography Based Optimization and Differentional Evolution Algorithm

    Directory of Open Access Journals (Sweden)

    Samira Abdi

    2012-11-01

    The proposed algorithm (MOBBO/DE makes the use of nondominated sorting approach improve the convergence ability efficiently and hence it can generate the promising candidate solutions. It also combines crowding distance to guarantee the diversity of Pareto optimal solutions. The proposed approach is validated using several test functions and some metrics taken from the standard literature on evolutionary multi-objective optimization. Results indicate that the approach is highly competitive and that can be considered a viable alternative to solve multi-objective optimization problems.

  16. 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.

  17. Recognition of Gene Acceptor Site Based on Multi-objective Optimization

    Institute of Scientific and Technical Information of China (English)

    Jing ZHAO; Yue-Min ZHU; Pei-Ming SONG; Qing FANG; Jian-Hua LUO

    2005-01-01

    A new method for predicting the gene acceptor site based on multi-objective optimization is introduced in this paper. The models for the acceptor, branch and distance between acceptor site and branch site were constructed according to the characteristics of the sequences from the exon-intron database and using common biological knowledge. The acceptor function, branch function and distance function were defined respectively, and the multi-objective optimization model was constructed to recognize the splice site. The test results show that the algorithm used in this study performs better than the SplicePredictor,which is one of the leading acceptor site detectors.

  18. Visual Multi-Object Tracking in the Presence of Cluttered Scenes

    Directory of Open Access Journals (Sweden)

    Xu-Sheng Gan

    2013-07-01

    Full Text Available The aim of this study was to investigate the visual multi-object tracking in the presence of cluttered scenes. A improved algorithm of fusing multi-source information including location and color evidences were introduced based on Dezert-Smarandache Theory (DSmT and Particle Filters (PF. Results showed that the conflict strategy and DSmT combination model were available and the proposed approach exhibited a significantly better performance for dealing with high conflict between evidences than a conventional PF. The suggested approach can easily be generalized to deal with larger number of visual multi-object and additional cues in the presence of cluttered scenes.

  19. 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

  20. Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network

    Institute of Scientific and Technical Information of China (English)

    ZHANG Jun-hong; XIE An-guo; SHEN Feng-man

    2007-01-01

    A multi-objective optimization and analysis model of the sintering process based on BP neural network is presented. Genetic algorithms are combined to simplify the BP neural network, which can reduce the learning time and increase the forecasting accuracy of the network model. This model has been experimented in the sintering process, and the production cost, the energy consumption, the quality (revolving intensity), and the output are considered at the same time. Moreover, the relation between some factors and the multi-objectives has been analyzed, and the results are consistent with the process. Different objectives are emphasized at different practical periods, and this can provide a theoretical basis for the manager.

  1. A New Definition and Calculation Model for Evolutionary Multi-Objective Optimization

    Institute of Scientific and Technical Information of China (English)

    Zhou Ai-min; Kang Li-shan; Chen Yu-ping; Huang Yu-zhen

    2003-01-01

    We present a new definition (Evolving Solutions) for Multi objective Optimization Problem (MOP) to answer the basic question (what's multi-objective optimal solution?) and advance an asynchronous evolutionary model (MINT Model) to solve MOPs. The new theory is based on our understanding of the natural evolution and the analysis of the difference between natural evolution and MOP, thus it is not only different from the Converting Optimization but also different from Pareto Optimization.Some tests prove that our new theory may conquer disadvantages of the upper two methods to some extent.

  2. Multi-objective dynamic optimization model for China's road transport energy technology switching

    Institute of Scientific and Technical Information of China (English)

    Dan GAO; Zheng LI; Feng FU; Linwei MA

    2009-01-01

    Deducting the future switching of the road transport energy technology is one of the key preconditions for relative technology development planning. However,one of the difficulties is to address the issue of multi-objective and conflicting constrains, e.g., minimizing the climate mitigation or minimizing economic cost. In this paper, a dynamic optimization model was established, which can be used to analyze the road transport energy technology switching under multi-objective constrains.Through one case study, a series of solutions could be derived to provide decision-makers with the flexibility to choose the appropriate solution with respect to the given situation.

  3. 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 aimi...... calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method....

  4. Multi-Objective Optimization of A Semisubmersible for Ultra-Deep Water

    Institute of Scientific and Technical Information of China (English)

    CHEN Xin-quan; TAN Jia-hua

    2008-01-01

    Semisubmersible will work well when oil exploitation goes to ultra-deep water because of its variable load capacities, and good motion performance in extreme waves. It is considered to be a main type of platform while the water depth is more than 3000 meters. This paper establishes a multi-objective optimization model of semisubmersible for ultra-deep water, and it is solved by a multi-objective genetic algorithm-NSGA-II. The model is applied to a practical design, and Pareto results are obtained. The effectiveness of the method is verified by hydrodynamic analysis.

  5. Confronting Decision Cliffs: Diagnostic Assessment of Multi-Objective Evolutionary Algorithms' Performance for Addressing Uncertain Environmental Thresholds

    Science.gov (United States)

    Ward, V. L.; Singh, R.; Reed, P. M.; Keller, K.

    2014-12-01

    As water resources problems typically involve several stakeholders with conflicting objectives, multi-objective evolutionary algorithms (MOEAs) are now key tools for understanding management tradeoffs. Given the growing complexity of water planning problems, it is important to establish if an algorithm can consistently perform well on a given class of problems. This knowledge allows the decision analyst to focus on eliciting and evaluating appropriate problem formulations. This study proposes a multi-objective adaptation of the classic environmental economics "Lake Problem" as a computationally simple but mathematically challenging MOEA benchmarking problem. The lake problem abstracts a fictional town on a lake which hopes to maximize its economic benefit without degrading the lake's water quality to a eutrophic (polluted) state through excessive phosphorus loading. The problem poses the challenge of maintaining economic activity while confronting the uncertainty of potentially crossing a nonlinear and potentially irreversible pollution threshold beyond which the lake is eutrophic. Objectives for optimization are maximizing economic benefit from lake pollution, maximizing water quality, maximizing the reliability of remaining below the environmental threshold, and minimizing the probability that the town will have to drastically change pollution policies in any given year. The multi-objective formulation incorporates uncertainty with a stochastic phosphorus inflow abstracting non-point source pollution. We performed comprehensive diagnostics using 6 algorithms: Borg, MOEAD, eMOEA, eNSGAII, GDE3, and NSGAII to ascertain their controllability, reliability, efficiency, and effectiveness. The lake problem abstracts elements of many current water resources and climate related management applications where there is the potential for crossing irreversible, nonlinear thresholds. We show that many modern MOEAs can fail on this test problem, indicating its suitability as a

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

    KAUST Repository

    Abdelfattah, Ahmad M.

    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.

  7. Multi-Objective Reinforcement Learning-based Deep Neural Networks for Cognitive Space Communications

    Science.gov (United States)

    Ferreria, Paulo; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy; Bilen, Sven; Reinhart, Richard; Mortensen, Dale

    2017-01-01

    Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

  8. Multi-Objective Reinforcement Learning-Based Deep Neural Networks for Cognitive Space Communications

    Science.gov (United States)

    Ferreria, Paulo Victor R.; Paffenroth, Randy; Wyglinski, Alexander M.; Hackett, Timothy M.; Bilen, Sven G.; Reinhart, Richard C.; Mortensen, Dale J.

    2017-01-01

    Future communication subsystems of space exploration missions can potentially benefit from software-defined radios (SDRs) controlled by machine learning algorithms. In this paper, we propose a novel hybrid radio resource allocation management control algorithm that integrates multi-objective reinforcement learning and deep artificial neural networks. The objective is to efficiently manage communications system resources by monitoring performance functions with common dependent variables that result in conflicting goals. The uncertainty in the performance of thousands of different possible combinations of radio parameters makes the trade-off between exploration and exploitation in reinforcement learning (RL) much more challenging for future critical space-based missions. Thus, the system should spend as little time as possible on exploring actions, and whenever it explores an action, it should perform at acceptable levels most of the time. The proposed approach enables on-line learning by interactions with the environment and restricts poor resource allocation performance through virtual environment exploration. Improvements in the multiobjective performance can be achieved via transmitter parameter adaptation on a packet-basis, with poorly predicted performance promptly resulting in rejected decisions. Simulations presented in this work considered the DVB-S2 standard adaptive transmitter parameters and additional ones expected to be present in future adaptive radio systems. Performance results are provided by analysis of the proposed hybrid algorithm when operating across a satellite communication channel from Earth to GEO orbit during clear sky conditions. The proposed approach constitutes part of the core cognitive engine proof-of-concept to be delivered to the NASA Glenn Research Center SCaN Testbed located onboard the International Space Station.

  9. Coordinated Voltage Control of a Wind Farm based on Model Predictive Control

    DEFF Research Database (Denmark)

    Zhao, Haoran; Wu, Qiuwei; Guo, Qinglai

    2016-01-01

    This paper presents an autonomous wind farm voltage controller based on Model Predictive Control (MPC). The reactive power compensation and voltage regulation devices of the wind farm include Static Var Compensators (SVCs), Static Var Generators (SVGs), Wind Turbine Generators (WTGs) and On......-Load Tap Changing (OLTC) Transformer, and they are coordinated to keep the voltages of all the buses within the feasible range. Moreover, the reactive power distribution is optimized throughout the wind farm in order to maximize the dynamic reactive power reserve. The sensitivity coefficients...... are calculated based on an analytical method to improve the computation efficiency and overcome the convergence problem. Two control modes are designed for both voltage violated and normal operation conditions. A wind farm with 20 wind turbines was used to conduct case studies to verify the proposed coordinated...

  10. A Coordination Scheme for Distributed Model Predictive Control: Integration of Flexible DERs

    DEFF Research Database (Denmark)

    Costanzo, Giuseppe Tommaso; Gehrke, Oliver; Bondy, Daniel Esteban Morales

    2013-01-01

    between the grid and a cluster of units in the same feeder in pricedriven demand response applications. Preliminary simulations prove that the proposed coordination scheme for DMPC succeeds in coordinating flexible DER unit, achieving significant peak shaving when required. The rationale of this approach...... consists in coordinating independent units equipped with local MPC controller via simple information passing and hiding in the local controllers the units’ dynamics....

  11. Multi-objective comprehensive optimization of fuel consumption and emission for hybrid electric vehicles 

    Institute of Scientific and Technical Information of China (English)

    WEI Han-bing; LIU Xiao-fei; HE Yi-tuan; PENG Zhi-yuan

    2014-01-01

    Aiming to reduce fuel consumption and emissions of a dual-clutch hybrid electric vehicle during cold start, multi-objective optimization for fuel consumption and HC/CO emission from a TWC (three-way catalytic converter) outlet is presented in this paper. DP (dynamic programming) considering dual-state variables is proposed based on the Bellman optimality principle. Both the battery SOC (state of charge) and the temperature of TWC monolith are considered in the algorithm simultaneously. In this way the global optimal control strategy and the Pareto optimal solution of multi-objective function are derived. Simulation results show that the proposed method is able to promote the TWC light-off significantly by decreasing the engine’s load and improving exhaust temperature from the outlet of the engine, in comparison with original DP considering the single battery SOC. Compared to the results achieved by rule-based control strategy, fuel economy and emission of TWC outlet for cold start are optimized comprehensively. Each indicator of Pareto solution set shows the significant improvement.

  12. 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

  13. 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.

  14. Accelerating solving the dynamic multi-objective nework design problem using response surface methods

    NARCIS (Netherlands)

    Wismans, Luc J.J.; Berkum, van Eric C.; Bliemer, Michiel C.J.

    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 me

  15. Multi-objective optimization in formation tasks of leather and fur materials

    Directory of Open Access Journals (Sweden)

    Ольга Викторовна Сангинова

    2014-09-01

    Full Text Available The comparative analysis of the efficiency of different ways to obtain a compromise solution in the multi-objective constrained optimization tasks has been conducted. The analysis was performed for a number of innovative technologies of leather and fur materials forming.

  16. Analysis of Various Multi-Objective Optimization Evolutionary Algorithms for Monte Carlo Treatment Planning System

    CERN Document Server

    Tydrichova, Magdalena

    2017-01-01

    In this project, various available multi-objective optimization evolutionary algorithms were compared considering their performance and distribution of solutions. The main goal was to select the most suitable algorithms for applications in cancer hadron therapy planning. For our purposes, a complex testing and analysis software was developed. Also, many conclusions and hypothesis have been done for the further research.

  17. 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.

    2012-01-01

    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

  18. 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.

  19. 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 r

  20. An interactive visualization tool for the analysis of multi-objective embedded systems design space exploration

    NARCIS (Netherlands)

    Taghavi, T.; Pimentel, A.D.

    2011-01-01

    The design of today’s embedded systems involves a complex Design Space Exploration (DSE) process. Typically, multiple and conflicting criteria (objectives) should be optimized simultaneously such as performance, power, cost, etc. Usually, Multi-Objective Evolutionary Algorithms (MOEAs) are used to

  1. An environmental-economic framework to support multi-objective policy-making

    NARCIS (Netherlands)

    Pacini, G.C.

    2003-01-01

    Keywords: environmental accounting, environmental indicators, farming systems, sustainability, organic farming, ecological-economic modelling, spatial analysis, multi-objective policy-making, opportunity cost.There is a growing awareness in present-day society of the potent

  2. 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...

  3. 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...

  4. Solutions of Multi Objective Fuzzy Transportation Problems with Non-Linear Membership Functions

    Directory of Open Access Journals (Sweden)

    Dr. M. S. Annie Christi

    2016-11-01

    Full Text Available Multi-objective transportation problem with fuzzy interval numbers are considered. The solution of linear MOTP is obtained by using non-linear membership functions. The optimal compromise solution obtained is compared with the solution got by using a linear membership function. Some numerical examples are presented to illustrate this.

  5. SOME RATIONALITY CONDITIONS OF JOINT EFFICIENT MAPPING IN GROUP MULTI-OBJECTIVE PROGRAMMING

    Institute of Scientific and Technical Information of China (English)

    Jing LI; Yuda HU

    2007-01-01

    The joint efficient ordering method is a fundamental method of ordering alternatives in group multi-objective programming problems. In this paper, the rational properties of the joint efficient mapping corresponding to the joint efficient ordering method are studied, and some necessary conditions of this mapping are proven.

  6. Study on the Reliability Evaluation of Qualitative Indices in Multi-Objective Decision

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    Qualitative indices in multi-objective decision can usually be evaluated and measured by mathematical methods or models, but the obtained results are sometimes inaccurate because of fuzziness of indices. To improve the accuracy and reliability of the evaluation results, set-value statistic principle is applied, and accordingly four evaluation methods are obtained. Meanwhile, these methods are compared briefly.

  7. 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

  8. 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...

  9. Multi-objective Optimization of Process Parameters in Friction Stir Welding

    DEFF Research Database (Denmark)

    Tutum, Cem Celal; Hattel, Jesper Henri

    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...

  10. 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.

  11. 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.

  12. 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.

  13. Design Optimization of an Axial Fan Blade Through Multi-Objective Evolutionary Algorithm

    Science.gov (United States)

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

    2010-06-01

    This paper presents design optimization of an axial fan blade with hybrid multi-objective evolutionary algorithm (hybrid MOEA). Reynolds-averaged Navier-Stokes equations with shear stress transport turbulence model are discretized by the finite volume approximations and solved on hexahedral grids for the flow analyses. The validation of the numerical results was performed with the experimental data for the axial and tangential velocities. Six design variables related to the blade lean angle and blade profile are selected and the Latin hypercube sampling of design of experiments is used to generate design points within the selected design space. Two objective functions namely total efficiency and torque are employed and the multi-objective optimization is carried out to enhance total efficiency and to reduce the torque. The flow analyses are performed numerically at the designed points to obtain values of the objective functions. The Non-dominated Sorting of Genetic Algorithm (NSGA-II) with ɛ -constraint strategy for local search coupled with surrogate model is used for multi-objective optimization. The Pareto-optimal solutions are presented and trade-off analysis is performed between the two competing objectives in view of the design and flow constraints. It is observed that total efficiency is enhanced and torque is decreased as compared to the reference design by the process of multi-objective optimization. The Pareto-optimal solutions are analyzed to understand the mechanism of the improvement in the total efficiency and reduction in torque.

  14. 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

  15. MOONS: the Multi-Object Optical and Near-infrared Spectrograph for the VLT

    NARCIS (Netherlands)

    M. Cirasuolo; . et al.; L. Kaper; B. Lemasle

    2014-01-01

    MOONS is a new Multi-Object Optical and Near-infrared Spectrograph selected by ESO as a third generation instrument for the Very Large Telescope (VLT). The grasp of the large collecting area offered by the VLT (8.2m diameter), combined with the large multiplex and wavelength coverage (optical to nea

  16. 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.

  17. Robust Multi-Objective PQ Scheduling for Electric Vehicles in Flexible Unbalanced Distribution Grids

    DEFF Research Database (Denmark)

    Knezovic, Katarina; Soroudi, Alireza; Marinelli, Mattia

    2017-01-01

    With increased penetration of distributed energy resources and electric vehicles (EVs), different EV management strategies can be used for mitigating adverse effects and supporting the distribution grid. This paper proposes a robust multi-objective methodology for determining the optimal day...

  18. Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis

    Directory of Open Access Journals (Sweden)

    Sang Yong Han

    2009-05-01

    Full Text Available This paper applies the Differential Evolution (DE algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II and Multi-Objective Clustering with an unknown number of Clusters K (MOCK. Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.

  19. Performance of a genetic algorithm for solving the multi-objective, multimodel transportation network design problem

    NARCIS (Netherlands)

    Brands, T.; Berkum, van E.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 st

  20. Multi-objective differential evolution for automatic clustering with application to micro-array data analysis.

    Science.gov (United States)

    Suresh, Kaushik; Kundu, Debarati; Ghosh, Sayan; Das, Swagatam; Abraham, Ajith; Han, Sang Yong

    2009-01-01

    This paper applies the Differential Evolution (DE) algorithm to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performances of two multi-objective variants of DE over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation of the search variables, accommodating variable number of cluster centers, is used for DE. The performances of the multi-objective DE-variants have also been contrasted to that of two most well-known schemes of MO clustering, namely the Non Dominated Sorting Genetic Algorithm (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK). Experimental results using six artificial and four real life datasets of varying range of complexities indicate that DE holds immense promise as a candidate algorithm for devising MO clustering schemes.

  1. 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.

  2. Multi Objective Optimization for Calibration and Efficient Uncertainty Analysis of Computationally Expensive Watershed Models

    Science.gov (United States)

    Akhtar, T.; Shoemaker, C. A.

    2011-12-01

    Assessing the sensitivity of calibration results to different calibration criteria can be done through multi objective optimization that considers multiple calibration criteria. This analysis can be extended to uncertainty analysis by comparing the results of simulation of the model with parameter sets from many points along a Pareto Front. In this study we employ multi-objective optimization in order to understand which parameter values should be used for flow parameters of a SWAT model, (Soil and Water Assessment Tool) designed to simulate flow in the Cannonsville Reservoir in upstate New York. The comprehensive analysis procedure encapsulates identification of suitable objectives, analysis of trade-offs obtained through multi-objective optimization, and the impact of the trade-offs uncertainty. Examples of multiple criteria can include a) quality of the fit in different seasons, b) quality of the fit for high flow events and for low flow events, c) quality of the fit for different constituents (e.g. water versus nutrients). Many distributed watershed models are computationally expensive and include a large number of parameters that are to be calibrated. Efficient optimization algorithms are hence needed to find good solutions to multi-criteria calibration problems in a feasible amount of time. We apply a new algorithm called Gap Optimized Multi-Objective Optimization using Response Surfaces (GOMORS), for efficient multi-criteria optimization of the Cannonsville SWAT watershed calibration problem. GOMORS is a stochastic optimization method, which makes use of Radial Basis Functions for approximation of the computationally expensive objectives. GOMORS performance is also compared against other multi-objective algorithms ParEGO and NSGA-II. ParEGO is a kriging based efficient multi-objective optimization algorithm, whereas NSGA-II is a well-known multi-objective evolutionary optimization algorithm. GOMORS is more efficient than both ParEGO and NSGA-II in providing

  3. Multi-objective global optimization of a butterfly valve using genetic algorithms.

    Science.gov (United States)

    Corbera, Sergio; Olazagoitia, José Luis; Lozano, José Antonio

    2016-07-01

    A butterfly valve is a type of valve typically used for isolating or regulating flow where the closing mechanism takes the form of a disc. For a long time, the attention of many researchers has focused on carrying out structural (FEM) and computational fluid dynamics (CFD) analysis in order to increase the performance of this type of flow-control device. This paper proposes a novel multi-objective approach for the design optimization of a butterfly valve using advanced genetic algorithms based on Pareto dominance. Firstly, after defining the need for this study and analyzing previous papers on the subject, the initial butterfly valve is presented and the initial fluid and structural analysis are carried out. Secondly, the optimization problem is defined and the optimization strategy is presented. The design variables are identified and a parameterization model of the valve is made. Thirdly, initial design candidates are generated by DOE and design optimization using genetic algorithms is performed. In this part of the process structural and CFD analysis are calculated for each candidate simultaneously. The optimization process involves various types of software and Python scripts are needed for their interaction and the connection of all steps. Finally, a set of optimal solutions is obtained and the optimum design that provides a 65.4% stress reduction, a 5% mass reduction and a 11.3% flow increase is selected in accordance with manufacturer preferences. Validation of the results is provided by comparing experimental test results with the values obtained for the initial design. The results demonstrate the capability and potential of the proposed methodology.

  4. Recent testing of a micro autonomous positioning system for multi-object instrumentation

    Science.gov (United States)

    Cochrane, W. A.; Atkinson, D. C.; Bailie, T. E. C.; Dickson, C.; Lim, T.; Luo, X.; Montgomery, D. M.; Schnetler, H.; Taylor, W. D.; Wilson, B.

    2012-09-01

    A multiple pick off mirror positioning sub-system has been developed as a solution for the deployment of mirrors within multi-object instrumentation such as the EAGLE instrument in the European Extremely Large Telescope (E-ELT). The positioning sub-system is a two wheeled differential steered friction drive robot with a footprint of approximately 20 x 20 mm. Controlled by RF communications there are two versions of the robot that exist. One is powered by a single cell lithium ion battery and the other utilises a power floor system. The robots use two brushless DC motors with 125:1 planetary gear heads for positioning in the coarse drive stages. A unique power floor allows the robots to be positioned at any location in any orientation on the focal plane. The design, linear repeatability tests, metrology and power continuity of the robot will be evaluated and presented in this paper. To gather photons from the objects of interest it is important to position POMs within a sphere of confusion of less than 10 μm, with an angular alignment better than 1 mrad. The robots potential of meeting these requirements will be described through the open-loop repeatability tests conducted with a Faro laser beam tracker. Tests have involved sending the robot step commands and automatically taking continuous measurements every three seconds. Currently the robot is capable of repeatedly travelling 233 mm within 0.307 mm at 5 mm/s. An analysis of the power floors reliability through the continuous monitoring of the voltage across the tracks with a Pico logger will also be presented.

  5. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)

    Institute of Scientific and Technical Information of China (English)

    Xiang-Qun Cui; Xiao-Zheng Xing; Xin-Nan Li; Yong-Tian Zhu; Gang Wang; Bo-Zhong Gu; A-Li Luo; Xin-Qi Xu; Zhen-Chao Zhang; Gen-Rong Liu; Hao-Tong Zhang; Yong-Heng Zhao; De-Hua Yang; Shu-Yun Cao; Hai-Yuan Chen; Jian-Jun Chen; Kun-Xin Chen; Ying Chen; Jia-Ru Chu; Lei Feng; Xue-Fei Gong; Yong-Hui Hou; Yao-Quan Chu; Hong-Zhuan Hu; Ning-Sheng Hu; Zhong-Wen Hu; Lei Jia; Fang-Hua Jiang; Xiang Jiang; Zi-Bo Jiang; Ge Jin; Ai-Hua Li; Yan Li; Guo-Ping Li; Ye-Ping Li; Guan-Qun Liu; Zhi-Gang Liu; Wen-Zhi Lu; Yin-Dun Mao; Li Men; Yong-Jun Qi; Zhao-Xiang Qi; Huo-Ming Shi; Zheng-Hong Tang; Qi Li; Qing-Sheng Tao; Da-Qi Wang; Dan Wang; Guo-Min Wang; Hai Wang; Jia-Ning Wang; Jian Wang; Jian-Ling Wang; Jian-Ping Wang; Lei Wang; Li-Ping Zhang; Shu-Qing Wang; You Wang; Yue-Fei Wang; Ling-Zhe Xu; Yan Xu; Shi-Hai Yang; Yong Yu; Hui Yuan; Xiang-Yan Yuan; Chao Zhai; Hong-Jun Su; Jing Zhang; Yan-Xia Zhang; Yong Zhang; Ming Zhao; Fang Zhou; Guo-Hua Zhou; Jie Zhu; Si-Cheng Zou; Zheng-Qiu Yao; Ya-Nan Wang

    2012-01-01

    The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST,also called the Guo Shou Jing Telescope) is a special reflecting Schmidt telescope.LAMOST's special design allows both a large aperture (effective aperture of 3.6m-4.9m) and a wide field of view (FOV) (5°).It has an innovative active reflecting Schmidt configuration which continuously changes the mirror's surface that adjusts during the observation process and combines thin deformable mirror active optics with segmented active optics.Its primary mirror (6.67 m ×6.05 m) and active Schmidt mirror (5.74m×4.40m) are both segmented,and composed of 37 and 24 hexagonal sub-mirrors respectively.By using a parallel controllable fiber positioning technique,the focal surface of 1.75 m in diameter can accommodate 4000 optical fibers.Also,LAMOST has 16 spectrographs with 32 CCD cameras.LAMOST will be the telescope with the highest rate of spectral acquisition.As a national large scientific project,the LAMOST project was formally proposed in 1996,and approved by the Chinese government in 1997.The construction started in 2001,was completed in 2008 and passed the official acceptance in June 2009.The LAMOST pilot survey was started in October 2011 and the spectroscopic survey will launch in September 2012.Up to now,LAMOST has released more than 480 000 spectra of objects.LAMOST will make an important contribution to the study of the large-scale structure of the Universe,structure and evolution of the Galaxy,and cross-identification of multiwaveband properties in celestial objects.

  6. 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

  7. 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.

  8. Seeking urbanization security and sustainability: Multi-objective optimization of rainwater harvesting systems in China

    Science.gov (United States)

    Li, Yi; Ye, Quanliang; Liu, An; Meng, Fangang; Zhang, Wenlong; Xiong, Wei; Wang, Peifang; Wang, Chao

    2017-07-01

    Urban rainwater management need to achieve an optimal compromise among water resource augmentation, water loggings alleviation, economic investment and pollutants reduction. Rainwater harvesting (RWH) systems, such as green rooftops, porous pavements, and green lands, have been successfully implemented as viable approaches to alleviate water-logging disasters and water scarcity problems caused by rapid urbanization. However, there is limited guidance to determine the construction areas of RWH systems, especially for stormwater runoff control due to increasing extreme precipitation. This study firstly developed a multi-objective model to optimize the construction areas of green rooftops, porous pavements and green lands, considering the trade-offs among 24 h-interval RWH volume, stormwater runoff volume control ratio (R), economic cost, and rainfall runoff pollutant reduction. Pareto fronts of RWH system areas for 31 provinces of China were obtained through nondominated sorting genetic algorithm. On the national level, the control strategies for the construction rate (the ratio between the area of single RWH system and the total areas of RWH systems) of green rooftops (ηGR), porous pavements (ηPP) and green lands (ηGL) were 12%, 26% and 62%, and the corresponding RWH volume and total suspended solids reduction was 14.84 billion m3 and 228.19 kilotons, respectively. Optimal ηGR , ηPP and ηGL in different regions varied from 1 to 33%, 6 to 54%, and 30 to 89%, respectively. Particularly, green lands were the most important RWH system in 25 provinces with ηGL more than 50%, ηGR mainly less than 15%, and ηPP mainly between 10 and 30%. Results also indicated whether considering the objective MaxR made a non-significant difference for RWH system areas whereas exerted a great influence on the result of stormwater runoff control. Maximum daily rainfall under control increased, exceeding 200% after the construction of the optimal RWH system compared with that before

  9. Integrated Laboratory Demonstrations of Multi-Object Adaptive Optics on a Simulated 10-Meter Telescope at Visible Wavelengths

    CERN Document Server

    Ammons, S Mark; Laag, Edward A; Kupke, Renate; Gavel, Donald T; Bauman, Brian J; Max, Claire E

    2009-01-01

    One important frontier for astronomical adaptive optics (AO) involves methods such as Multi-Object AO and Multi-Conjugate AO that have the potential to give a significantly larger field of view than conventional AO techniques. A second key emphasis over the next decade will be to push astronomical AO to visible wavelengths. We have conducted the first laboratory simulations of wide-field, laser guide star adaptive optics at visible wavelengths on a 10-meter-class telescope. These experiments, utilizing the UCO/Lick Observatory's Multi-Object / Laser Tomography Adaptive Optics (MOAO/LTAO) testbed, demonstrate new techniques in wavefront sensing and control that are crucial to future on-sky MOAO systems. We (1) test and confirm the feasibility of highly accurate atmospheric tomography with laser guide stars, (2) demonstrate key innovations allowing open-loop operation of Shack-Hartmann wavefront sensors (with errors of ~30 nm) as will be needed for MOAO, and (3) build a complete error budget model describing sy...

  10. Application of Multi-Objective Evolutionary Algorithm for Optimal Reactive Power Dispatch with Flexible AC Transmission System Devices

    Directory of Open Access Journals (Sweden)

    Abdarrazak OUALI

    2011-12-01

    Full Text Available Because their capability to change the network parameters with a rapid response and enhanced flexibility, flexible AC transmission system (FACTS devices have taken more attention in power systems operations as improvement of voltage profile and minimizing system losses. In this way, this paper presents a multi-objective evolutionary algorithm (MOEA to solve optimal reactive power dispatch (ORPD problem with FACTS devices. This nonlinear multi-objective problem (MOP consists to minimize simultaneously real power loss in transmission lines and voltage deviation at load buses, by tuning parameters and searching the location of FACTS devices. The constraints of this MOP are divided to equality constraints represented by load flow equations and inequality constraints such as, generation reactive power sources and security limits at load buses. Two types of FACTS devices, static synchronous series compensator (SSSC and unified power flow controller (UPFC are considered. A comparative study regarding the effects of an SSSC and an UPFC on voltage deviation and total transmission real losses is carried out. The design problem is tested on a 6-bus system.

  11. A multi-objective model for locating distribution centers in a supply chain network considering risk and inventory decisions

    Directory of Open Access Journals (Sweden)

    Sara Gharegozloo Hamedani

    2013-04-01

    Full Text Available This paper presents a multi-objective location problem in a three level supply chain network under uncertain environment considering inventory decisions. The proposed model of this paper considers uncertainty for different parameters including procurement, transportation costs, supply, demand and the capacity of various facilities. The proposed model presents a robust optimization model, which specifies locations of distribution centers to be opened, inventory control parameters (r, Q, and allocation of supply chain components, concurrently. The resulted mixed-integer nonlinear programming minimizes the expected total cost of such a supply chain network comprising location, procurement, transportation, holding, ordering, and shortage costs. The model also minimizes the variability of the total cost of relief chain and minimizes the financial risk or the probability of not meeting a certain budget. We use the ε-constraint method, which is a multi-objective technique with implicit trade-off information given, to solve the problem and using a couple of numerical instances, we examine the performance of the proposed approach.

  12. Coordinated control for regulation/protection mode-switching of ducted rockets

    Science.gov (United States)

    Qi, Yiwen; Bao, Wen; Zhao, Jun; Chang, Juntao

    2014-05-01

    This study is concerned with the coordinated control problem for regulation/protection mode-switching of a ducted rocket, in order to obtain the maximum system performance while ensuring safety. The proposed strategy has an inner/outer loop control structure which decomposes the contradiction between performance and safety into two modes of regulation and protection. Specifically, first, the mathematical model including the actuator (gas regulating system) and the plant (ducted rocket engine) is introduced. Second, taking the inlet buzz for instance, the ducted rocket coordinated control problem for thrust regulation and inlet buzz limit protection is formulated and discussed. Third, to solve the problem, based on the main inner loop, a limit protection controller (outer loop) design method is developed utilizing a linear quadratic optimal control technique, and a coordinated control logic is then presented. At last, the whole coordinated control strategy is applied to the ducted rocket control model, and simulation results demonstrate its effectiveness.

  13. Economic and environmental multi-objective optimization to evaluate the impact of Belgian policy on solar power and electric vehicles

    OpenAIRE

    De Schepper, Ellen; Van Passel, Steven; Lizin, Sebastien; Vincent, Thomas; Martin, Benjamin; Gandibleux, Xavier

    2015-01-01

    This research uses multi-objective optimization to determine the optimal mixture of energy and transportation technologies, while optimizing economic and environmental impacts. We demonstrate the added value of using multi-objective mixed integer linear programming (MOMILP) considering economies of scale versus using continuous multi-objective linear programming (MOLP) assuming average cost intervals. This paper uses an improved version to solve MOMILPs exactly (Vincent, et al. 2013). To diff...

  14. An overview of coordinated control for multi-agent systems subject to input saturation

    Directory of Open Access Journals (Sweden)

    Xiaoling Wang

    2016-03-01

    Full Text Available Coordinated control of multi-agent systems has widespread application in engineering such as unmanned aerial vehicles and unmanned ground vehicles. Due to the fact that input saturation can lead a control system to deterioration and instability, a lot of efforts have been devoted to investigating this subject of great importance. The present article offers a survey of recent developments on coordinated control of multi-agents systems subject to input saturation. Some preliminaries about graph theory, stability theory and input saturation are first provided, followed by some important results in the area, which are categorized into semi-global and global coordinated controls. Future research topics are finally discussed.

  15. Transparency and Coordinated Effects in European Merger Control

    DEFF Research Database (Denmark)

    Albæk, Svend; Møllgaard, H. Peter; Overgaard, Per Baltzer

    2010-01-01

    In this paper, we first outline the foundations in economic theory of so-called coordinated effects with a particular view to mergers and with a special focus on transparency. Then, we review a number of seminal merger cases in EU competition policy (Airtours, Sony/BMG, ABF/GBI Business) in light...... widespread in the market in question. Finally, we briefly present a few other cases in which transparency has been an issue....

  16. An Approach to Continuous Approximation of Pareto Front Using Geometric Support Vector Regression for Multi-objective Optimization of Fermentation Process

    Institute of Scientific and Technical Information of China (English)

    Jiahuan Wu; Jianlin Wang; Tao Yu; Liqiang Zhao

    2014-01-01

    The approaches to discrete approximation of Pareto front using multi-objective evolutionary algorithms have the problems of heavy computation burden, long running time and missing Pareto optimal points. In order to overcome these problems, an approach to continuous approximation of Pareto front using geometric support vector regression is presented. The regression model of the small size approximate discrete Pareto front is constructed by geometric support vector regression modeling and is described as the approximate continuous Pareto front. In the process of geometric support vector regression modeling, considering the distribution characteristic of Pareto optimal points, the separable augmented training sample sets are constructed by shifting original training sample points along multiple coordinated axes. Besides, an interactive decision-making (DM) procedure, in which the continuous approximation of Pareto front and decision-making is performed interactive-ly, is designed for improving the accuracy of the preferred Pareto optimal point. The correctness of the continuous approximation of Pareto front is demonstrated with a typical multi-objective optimization problem. In addition, combined with the interactive decision-making procedure, the continuous approximation of Pareto front is applied in the multi-objective optimization for an industrial fed-batch yeast fermentation process. The experi-mental results show that the generated approximate continuous Pareto front has good accuracy and complete-ness. Compared with the multi-objective evolutionary algorithm with large size population, a more accurate preferred Pareto optimal point can be obtained from the approximate continuous Pareto front with less compu-tation and shorter running time. The operation strategy corresponding to the final preferred Pareto optimal point generated by the interactive DM procedure can improve the production indexes of the fermentation process effectively.

  17. Time-Coordination Strategies and Control Laws for Multi-Agent Unmanned Systems

    Science.gov (United States)

    Puig-Navarro, Javier; Hovakimyan, Naira; Allen, B. Danette

    2017-01-01

    Time-critical coordination tools for unmanned systems can be employed to enforce the type of temporal constraints required in terminal control areas, ensure minimum distance requirements among vehicles are satisfied, and successfully perform coordinated missions. In comparison with previous literature, this paper presents an ampler spectrum of coordination and temporal specifications for unmanned systems, and proposes a general control law that can enforce this range of constraints. The constraint classification presented con- siders the nature of the desired arrival window and the permissible coordination errors to define six different types of time-coordination strategies. The resulting decentralized coordination control law allows the vehicles to negotiate their speeds along their paths in response to information exchanged over the communication network. This control law organizes the different members in the fleet hierarchically per their behavior and informational needs as reference agent, leaders, and followers. Examples and simulation results for all the coordination strategies presented demonstrate the applicability and efficacy of the coordination control law for multiple unmanned systems.

  18. Multi-Objective Optimization with Function Approximation Including Application to Computationally Expensive Groundwater Remediation Design

    Science.gov (United States)

    Akhtar, T.; Shoemaker, C. A.

    2009-12-01

    Water Resources design decisions frequently entail trade-offs between conflicting objectives, for instance cost minimization and contaminant(s) concentration minimization. Multi-objective optimization methods (including those based on evolutionary methods) typically require a very large number of simulations to find a solution. Many groundwater remediation problems are modeled by computationally intensive systems of Partial Differential Equations and simulations. Hence it is desirable that these models are calibrated via algorithms that require less number of simulations. A new strategy called Gap Optimized Multi-Objective Optimization using Response Surfaces (GOMORS) is proposed for multi-objective optimization of computationally expensive problems. A multi-objective management framework is devised to analyze the trade-offs between conflicting objectives. We will present applications to test functions and to a groundwater contamination problem. The pumping rates at different well locations and management periods are the decision variables, and cost and contaminant concentration are the objectives to be minimized. The optimization strategy is iterative and makes use of Radial Basic Functions to develop response surfaces as an approximation of the computationally expensive objectives. A novel method called the Gap Optimization method is introduced. The gap optimization method incorporates use of a multi-objective evolutionary optimization (MOEA) method that is applied to select the next point for expensive evaluation and consequent improvement of the surrogate model. In order to provide sound alternatives to the decision makers, the evaluation point selection procedure strives to ensure that the final trade-off curve generated from the algorithm is close to the true Pareto front and includes a diverse set of solutions. After the final iteration, a set of candidate solutions is selected via the iterative Gap Optimization procedure and the last MOEA iteration, and

  19. Multi-Index Nonlinear Coordinated Control for Battery Energy Storage System and Generator Excitation

    Science.gov (United States)

    Lingyi, Kong; Liying, Liao

    A multi-index nonlinear coordinated control scheme for BESS and generator excitation is proposed. The proposed multi-index nonlinear coordinated controller can effectively coordinate the dynamic and steady-state performance of the controlled system. It can enhance the stability of the system, improve the dynamic characteristics of state variables, and can improve the control accuracy of output variables such as terminal voltage, active power output of the generator. Simulation results show that to control BESS and generator coordinately has the advantage of enhancing the stability of the system. With the ability of BESS to control the active power and reactive power, and the regulate of generator excitation, the dynamic characteristics of state variables can changes more smoothness, responds more speediness.

  20. Finite time coordinated formation control for spacecraft formation flying under directed communication topology

    Science.gov (United States)

    Ran, Dechao; Chen, Xiaoqian; Misra, Arun K.

    2017-07-01

    This paper investigates the finite time coordinated formation control problem for spacecraft formation flying (SFF) under the assumption of directed communication topology. By using the neighborhood state measurements, a robust finite time coordinated formation controller is firstly designed based on the nonsingular terminal sliding mode surface. To address the special case that the desired trajectory of the formation is only accessible to a subset of spacecraft in the formation, an adaptive finite time coordinated formation controller is also proposed by designing a novel sliding mode surface. In both cases, the external disturbances are explicitly taken into account. Rigorous theoretical analysis proves that the proposed control schemes ensure that the closed-loop system can track the desired time-varying trajectory in finite time. Numerical simulations are presented that not only highlights the closed-loop performance benefits from the proposed control algorithms, but also illustrates the effectiveness in the presence of external disturbances when compared with the existing coordinated formation control schemes.

  1. A multi-objective optimization model with conditional value-at-risk constraints for water allocation equality

    Science.gov (United States)

    Hu, Zhineng; Wei, Changting; Yao, Liming; Li, Ling; Li, Chaozhi

    2016-11-01

    Water scarcity is a global problem which causes economic and political conflicts as well as degradation of ecosystems. Moreover, the uncertainty caused by extreme weather increases the risk of economic inefficiency, an essential consideration for water users. In this study, a multi-objective model involving water allocation equality and economic efficiency risk control is developed to help water managers mitigate these problems. Gini coefficient is introduced to optimize water allocation equality in water use sectors (agricultural, domestic, and industrial sectors), and CVaR is integrated into the model constraints to control the economic efficiency loss risk corresponding to variations in water availability. The case study demonstrates the practicability and rationality of the developed model, allowing the river basin authority to determine water allocation strategies for a single river basin.

  2. Hierarchical control framework for integrated coordination between distributed energy resources and demand response

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Di; Lian, Jianming; Sun, Yannan; Yang, Tao; Hansen, Jacob

    2017-09-01

    Demand response is representing a significant but largely untapped resource that can greatly enhance the flexibility and reliability of power systems. In this paper, a hierarchical control framework is proposed to facilitate the integrated coordination between distributed energy resources and demand response. The proposed framework consists of coordination and device layers. In the coordination layer, various resource aggregations are optimally coordinated in a distributed manner to achieve the system-level objectives. In the device layer, individual resources are controlled in real time to follow the optimal power generation or consumption dispatched from the coordination layer. For the purpose of practical applications, a method is presented to determine the utility functions of controllable loads by taking into account the real-time load dynamics and the preferences of individual customers. The effectiveness of the proposed framework is validated by detailed simulation studies.

  3. Multi-agent coordination strategy estimation method based on control domain

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    For estimation group competition and multiagent coordination strategy, this paper introduces a notion based on multiagent group. According to the control domain, it analyzes the multiagent strategy during competi tion in the macroscopic. It has been adopted in robot soccer and result enunciates that our method does not de pend on competition result. It can objectively quantitatively estimate coordination strategy.

  4. Distributed voltage control coordination between renewable generation plants in MV distribution grids

    DEFF Research Database (Denmark)

    Petersen, Lennart; Iov, Florin

    2017-01-01

    This study focuses on distributed voltage control coordination between renewable generation plants in medium-voltage distribution grids (DGs). A distributed offline coordination concept has been defined in a previous publication, leading to satisfactory voltage regulation in the DG. However, here...

  5. Coordinated Resolved Motion Control of Dual-arm Manipulators with Closed Chain

    OpenAIRE

    Tianliang Liu; Yan Lei; Liang Han; Wenfu Xu; Huaiwu Zou

    2016-01-01

    When applied to some tasks, such as payload handling, assembling, repairing and so on, the two arms of a humanoid robot will form a closed kinematic chain. It makes the motion planning and control for dual‐arm coordination very complex and difficult. In this paper, we present three types of resolved motion control methods for a humanoid robot during coordinated manipulation. They are, respectively, position‐level, velocity‐level and acceleration‐level resolved motion control methods. The desi...

  6. A multi objective geometric programming approach for electronic product pricing problem

    Directory of Open Access Journals (Sweden)

    Mohsen Fathollah Bayati

    2011-07-01

    Full Text Available Nowadays electronic commerce plays an important role in many business activities, operations, and transaction processing. The recent advances on e-businesses have created tremendous opportunities to increase profitability. This paper presents a multi-objective marketing planning model which simultaneously determines efficient marketing expenditure, service cost and product's selling price in two competitive markets. To solve the proposed model, we discuss a multi-objective geometric programming (GP approach based on compromise programming method. Since our proposed model is a signomial GP and global optimality is not guaranteed for the problem, we transform the model to posynomial form. Finally, the solution procedure is illustrated via a numerical example and a sensitivity analysis is presented.

  7. 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.

  8. 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.

  9. Multi-objective process parameter optimization for energy saving in injection molding process

    Institute of Scientific and Technical Information of China (English)

    Ning-yun LU; Gui-xia GONG; Yi YANG; Jian-hua LU

    2012-01-01

    This paper deals with a multi-objective parameter optimization framework for energy saving in injection molding process.It combines an experimental design by Taguchi's method,a process analysis by analysis of variance (ANOVA),a process modeling algorithm by artificial neural network (ANN),and a multi-objective parameter optimization algorithm by genetic algorithm (GA)-based lexicographic method.Local and global Pareto analyses show the trade-off between product quality and energy consumption.The implementation of the proposed framework can reduce the energy consumption significantly in laboratory scale tests,and at the same time,the product quality can meet the pre-determined requirements.

  10. A modified interactive procedure to solve multi-objective group decision making problem

    Directory of Open Access Journals (Sweden)

    Mohammad Izadikhah

    2014-08-01

    Full Text Available Multi-objective optimization and multiple criteria decision making problems are the process of designing the best alternative by considering the incommensurable and conflicting objectives simultaneously. One of the first interactive procedures to solve multiple criteria decision making problems is STEM method. In this paper we propose a modified interactive procedure based on STEM method by calculating the weight vector of objectives which emphasize that more important objectives be closer to ideal one. We use the AHP and TOPSIS method to find these weights and develop a multi-objective group decision making procedure. Therefore the presented method tries to increase the rate of satisfactoriness of the obtained solution. Finally, a numerical example for illustration of the new method is given to clarify the main results developed in this paper.

  11. Extraction of battery parameters of the equivalent circuit model using a multi-objective genetic algorithm

    Science.gov (United States)

    Brand, Jonathan; Zhang, Zheming; Agarwal, Ramesh K.

    2014-02-01

    A simple but reasonably accurate battery model is required for simulating the performance of electrical systems that employ a battery for example an electric vehicle, as well as for investigating their potential as an energy storage device. In this paper, a relatively simple equivalent circuit based model is employed for modeling the performance of a battery. A computer code utilizing a multi-objective genetic algorithm is developed for the purpose of extracting the battery performance parameters. The code is applied to several existing industrial batteries as well as to two recently proposed high performance batteries which are currently in early research and development stage. The results demonstrate that with the optimally extracted performance parameters, the equivalent circuit based battery model can accurately predict the performance of various batteries of different sizes, capacities, and materials. Several test cases demonstrate that the multi-objective genetic algorithm can serve as a robust and reliable tool for extracting the battery performance parameters.

  12. 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

    acids, while maintaining sufficiently high growth rate and minimizing the secretion of undesired byproducts. Homo-productions of acetic, lactic and succinic acids were targeted as examples. Engineered E. coli strains capable of producing homo-acetic and homo-lactic acids could be developed by taking...... this systems approach for the minimal identification of gene knockout targets. Also, failure to predict effective gene knockout targets for the homo-succinic acid production suggests that the multi-objective optimization is useful in assessing the suitability of a microorganism as a host strain......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...

  13. Learned filters for object detection in multi-object visual tracking

    Science.gov (United States)

    Stamatescu, Victor; Wong, Sebastien; McDonnell, Mark D.; Kearney, David

    2016-05-01

    We investigate the application of learned convolutional filters in multi-object visual tracking. The filters were learned in both a supervised and unsupervised manner from image data using artificial neural networks. This work follows recent results in the field of machine learning that demonstrate the use learned filters for enhanced object detection and classification. Here we employ a track-before-detect approach to multi-object tracking, where tracking guides the detection process. The object detection provides a probabilistic input image calculated by selecting from features obtained using banks of generative or discriminative learned filters. We present a systematic evaluation of these convolutional filters using a real-world data set that examines their performance as generic object detectors.

  14. A Hybrid Multi Objective Particle Swarm Optimization Method to Discover Biclusters in Microarray Data

    CERN Document Server

    lashkargir, Mohsen; Dastjerdi, Ahmad Baraani

    2009-01-01

    In recent years, with the development of microarray technique, discovery of useful knowledge from microarray data has become very important. Biclustering is a very useful data mining technique for discovering genes which have similar behavior. In microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A Multi Objective model is capable of solving such problems. Our method proposes a Hybrid algorithm which is based on the Multi Objective Particle Swarm Optimization for discovering biclusters in gene expression data. In our method, we will consider a low level of overlapping amongst the biclusters and try to cover all elements of the gene expression matrix. Experimental results in the bench mark database show a significant improvement in both overlap among biclusters and coverage of elements in the gene expression matrix.

  15. A MULTI-OBJECTIVE ROBUST OPERATION MODEL FORELECTRONIC MARKET ENABLED SUPPLY CHAIN WITH UNCERTAIN DEMANDS

    Institute of Scientific and Technical Information of China (English)

    Jiawang XU; Xiaoyuan HUANG; Nina YAN

    2007-01-01

    A multi-objective robust operation model is proposed in this paper for an electronic market enabled supply chain consisting of multi-supplier and multi-customer with uncertain demands.Suppliers in this supply chain provide many kinds of products to different customers directly or through electronic market.Uncertain demands are described as a scenario set with certain probability; the supply chain operation model is constructed by using the robust optimization method based on scenario analyses.The operation model we proposed is a multi-objective programming problem satisfying several conflict objectives,such as meeting the demands of all customers,minimizing the system cost,the availabilities of suppliers' capacities not below a certain level,and robustness of decision to uncertain demands.The results of numerical examples proved that the solution of the model is most conservative; however,it can ensure the robustness of the operation of the supply chain effectively.

  16. 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

    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...

  17. 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.

  18. Multi-objective Optimization of Industrial Purified Terephthalic Acid Oxidation Process

    Institute of Scientific and Technical Information of China (English)

    牟盛静; 苏宏业; 古勇; 褚健

    2003-01-01

    Multi-objective optimization of a purified terephthalic acid (PTA) oxidation unit is carried out in this paper by using a process model that has been proved to describe industrial process quite well. The model is a semiempirical structured into two series ideal continuously stirred tank reactor (CSTR) models. The optimal objectives include maximizing the yield or inlet rate and minimizing the concentration of 4-carboxy-benzaldhyde, which is the main undesirable intermediate product in the reaction process. The multi-objective optimization algorithm applied in this study is non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ). The performance of NSGA-II is further illustrated by application to the title process.

  19. 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)

  20. Multi-objective Evolutionary Algorithms for MILP and MINLP in Process Synthesis

    Institute of Scientific and Technical Information of China (English)

    2001-01-01

    Steady-state non-dominated sorting genetic algorithm (SNSGA), a new form of multi-objective genetic algorithm, is implemented by combining the steady-state idea in steady-state genetic algorithms (SSGA) and the fitness assignment strategy of non-dominated sorting genetic algorithm (NSGA). The fitness assignment strategy is improved and a new self-adjustment scheme of σshare is proposed. This algorithm is proved to be very efficient both computationally and in terms of the quality of the Pareto fronts produced with five test problems including GA difficult problem and GA deceptive one. Finally, SNSGA is introduced to solve multi-objective mixed integer linear programming (MILP) and mixed integer non-linear programming (MINLP) problems in process synthesis.

  1. 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.

  2. An Archived Multi Objective Simulated Annealing Method to Discover Biclusters in Microarray Data

    Directory of Open Access Journals (Sweden)

    Mohsen Lashkargir

    2011-01-01

    Full Text Available With the advent of microarray technology it has been possible to measure thousands of expression values of genes in a single experiment. Analysis of large scale geonomics data, notably gene expression, has initially focused on clustering methods. Recently, biclustering techniques were proposed for revealing submatrices showing unique patterns. Biclustering or simultaneous clustering of both genes and conditions is challenging particularly for the analysis of high-dimensional gene expression data in information retrieval, knowledge discovery, and data mining. In biclustering of microarray data, several objectives have to be optimized simultaneously and often these objectives are in conflict with each other. A multi objective model is very suitable for solving this problem. Our method proposes a algorithm which is based on multi objective Simulated Annealing for discovering biclusters in gene expression data. Experimental result in bench mark data base present a significant improvement in overlap among biclusters and coverage of elements in gene expression and quality of biclusters.

  3. [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.

  4. Solving Multi Objective ORPD Problem Using AIS Based Clonal Selection Algorithm with UPFC

    Directory of Open Access Journals (Sweden)

    B. Srinivasa Rao

    2017-03-01

    Full Text Available In this paper, a solution for the multi objective optimal reactive power dispatch problem by using an artificial immune system (AIS based clonal selection algorithm was presented. The proposed AIS based clonal selection algorithm uses cloning of antibodies and followed by hyper maturation to minimize the voltage stability index (L-index, voltage deviations at all load buses and the transmission real power losses by incorporating the multi type FACTS device namely the UPFC. The proposed algorithm also uses concepts of non dominated sorting and crowding distance comparison procedures to solve the multi objective optimization problem. Finally, a fuzzy decision maker strategy is applied to find the best compromise solution. The algorithm was implemented and tested on two standard IEEE 30-bus and 57-bus test systems with UPFC. The proposed results are compared with and without placing the UPFC by considering two objectives for optimization.

  5. Simulation and experimental validation of powertrain mounting bracket design obtained from multi-objective topology optimization

    Directory of Open Access Journals (Sweden)

    Qinghai Zhao

    2015-06-01

    Full Text Available A framework of multi-objective topology optimization for vehicle powertrain mounting bracket design with consideration of multiple static and dynamic loading conditions is developed in this article. Incorporating into the simplified isotropic material with penalization model, compromise programming method is employed to describe the multi-objective and multi-stiffness topology optimization under static loading conditions, whereas mean eigenvalue formulation is proposed to analyze vibration optimization. To yield well-behaved optimal topologies, minimum member size and draw constraint are settled for meeting manufacturing feasibility requirements. The ultimate mounting bracket is reconstructed based on the optimum results. Numerical analyses of the bracket are performed, followed by physical tests. It is proven that topology optimization methodology is promising and effective for vehicle component design.

  6. Evolution strategies and multi-objective optimization of permanent magnet motor

    DEFF Research Database (Denmark)

    Andersen, Søren Bøgh; Santos, Ilmar

    2012-01-01

    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...... algorithm undergone a comprehensive parameter study for the parameters ρ and λ. The results of this parameter study show a significant improvement in stability and speed with the use of the modified ES version. To find the most effective selector for a multi-objective optimization, MOO, of the motor...... a performance examination of 4 different selectors from the group of programs called PISA has been made and compared for MOO of the efficiency and cost of the motor. This performance examination showed that the indicator based evolutionary algorithm, IBEA, and hypervolume estimation algorithm, HypE, selectors...

  7. MULTI-OBJECTIVE OPTIMIZATION OF EDM PARAMETERS USING GREY RELATION ANALYSIS

    Directory of Open Access Journals (Sweden)

    N. RADHIKA

    2015-01-01

    Full Text Available This paper involves the multi-objective optimization of process parameters of AlSi10Mg/9 wt% alumina/3 wt% graphite in Electrical Discharge Machining for obtaining minimum surface roughness, minimum tool wear rate and maximum material removal rate. The important machining parameters were selected as peak current, flushing pressure and pulse-on time. Experiments were conducted by selecting different operating levels for the three parameters according to Taguchi’s Design of Experiments. The multi-objective optimization was performed using Grey Relation Analysis to determine the optimal solution. The Grey Relation Grade values were then analysed using Analysis of Variance to determine the most contributing input parameter. On analysis it was found that peak current, flushing pressure and pulse-on time had an influence of 61.36%, 17.81% and 8.09% respectively on the optimal solution.

  8. Solving A Kind of High Complexity Multi-Objective Problems by A Fast Algorithm

    Institute of Scientific and Technical Information of China (English)

    Zeng San-you; Ding Li-xin; Kang Li-shan

    2003-01-01

    A fast algorithm is proposed to solve a kind of high complexity multi-objective problems in this paper. It takes advantages of both the orthogonal design method to search evenly, and the statistical optimal method to speed up the computation. It is very suitable for solving high complexity problems, and quickly yields solutions which converge to the Pareto-optimal set with high precision and uniform distribution. Some complicated multi objective problems are solved by the algorithm and the results show that the algorithm is not only fast but also superior to other MCGAS and MOEAs, such as the currently efficient algorithm SPEA, in terms of the precision, quantity and distribution of solutions.

  9. A GA-based Multi-Objective Optimization for Service Restoration in Power Distribution Systems

    Science.gov (United States)

    Inagaki, Jun; Nakajima, Jun; Haseyama, Miki; Kitajima, Hideo

    Service restoration problem in distribution systems is formulated as a multi-objective optimization problem which is demanded not only for minimizing the amount of unrestored total loads but also for minimizing the number of the switching operations. The solution of the multi-objective optimization problem is usually obtained with a set of Pareto optimal solutions. The Pareto optimal solutions for the service restoration problem are useful for users to obtain their desired restoration by comparing a Pareto optimal solution with the others. However, the conventional methods cannot obtain plural Pareto optimal solutions in one trial. Therefore, this paper proposes a method for obtaining a Pareto optimal set for the service restoration problem with a genetic algorithm. The genetic algorithm produces many possible solutions in its search process. By utilizing this feature, the proposed method can obtain the Pareto optimal set.

  10. A multi-objective method for solving assembly line balancing problem

    Directory of Open Access Journals (Sweden)

    Hadi Pazoki Toroudi

    2017-01-01

    Full Text Available Modeling the simple assembly line balancing (SALB problem has covered a wide range of real-world applications. The recent advances in optimization problems have created the opportunities to tackle more challenging problems. This paper presents a multi-objective decision making problem to consider two objectives, cost and cycle time, for simple assembly line balancing. The problem is formulated as a mixed integer nonlinear optimization and the proposed study of this paper uses two metaheuristics to solve the resulted problem on some benchmark problems. The preliminary results have indicated that multi objective particle swarm optimization (MOPSO has provided better quality solutions while the hybrid method based on MOPSO and simulated annealing has yielded more non-dominated Pareto solutions.

  11. Multi-objective genetic algorithm for the optimization of a flat-plate solar thermal collector.

    Science.gov (United States)

    Mayer, Alexandre; Gaouyat, Lucie; Nicolay, Delphine; Carletti, Timoteo; Deparis, Olivier

    2014-10-20

    We present a multi-objective genetic algorithm we developed for the optimization of a flat-plate solar thermal collector. This collector consists of a waffle-shaped Al substrate with NiCrOx cermet and SnO(2) anti-reflection conformal coatings. Optimal geometrical parameters are determined in order to (i) maximize the solar absorptance α and (ii) minimize the thermal emittance ε. The multi-objective genetic algorithm eventually provides a whole set of Pareto-optimal solutions for the optimization of α and ε, which turn out to be competitive with record values found in the literature. In particular, a solution that enables α = 97.8% and ε = 4.8% was found.

  12. 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.

  13. Multi objective optimization of line pack management of gas pipeline system

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    Pang, X.; Rybarcyk, L. J.

    2014-03-01

    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.

  15. A multi-objective approach in the optimization of optical systems taking into account tolerancing

    Science.gov (United States)

    de Albuquerque, Bráulio F. C.; Liao, Lin-Yao; Montes, Amauri Silva; de Sousa, Fabiano Luis; Sasián, José

    2011-10-01

    A Multi-Objective approach for lens design optimization was verified. The optimization problem was approached by addressing simultaneously, but separately, image quality and system tolerancing. In contrast to other previous published methods, the error functions were not combined into a single merit function. As a result the method returns a set of nondominated solutions that generates a Pareto Front. Our method resulted in alternate and useful insights about the trade off solutions for a lens design problem. This Multi-objective optimization can conveniently be implemented with evolutionary methods of optimization that have established success in lens design. We provided an example of the insights and usefulness of our approach in the design of a Telephoto lens system using NSGA-II, a popular multiobjective evolutionary optimization algorithm.

  16. Multi-objective optimization of lithium-ion battery model using genetic algorithm approach

    Science.gov (United States)

    Zhang, Liqiang; Wang, Lixin; Hinds, Gareth; Lyu, Chao; Zheng, Jun; Li, Junfu

    2014-12-01

    A multi-objective parameter identification method for modeling of Li-ion battery performance is presented. Terminal voltage and surface temperature curves at 15 °C and 30 °C are used as four identification objectives. The Pareto fronts of two types of Li-ion battery are obtained using the modified multi-objective genetic algorithm NSGA-II and the final identification results are selected using the multiple criteria decision making method TOPSIS. The simulated data using the final identification results are in good agreement with experimental data under a range of operating conditions. The validation results demonstrate that the modified NSGA-II and TOPSIS algorithms can be used as robust and reliable tools for identifying parameters of multi-physics models for many types of Li-ion batteries.

  17. Multi-Objective Optimization Algorithms Design based on Support Vector Regression Metamodeling

    Directory of Open Access Journals (Sweden)

    Qi Zhang

    2013-11-01

    Full Text Available In order to solve the multi-objective optimization problem in the complex engineering, in this paper a NSGA-II multi-objective optimization algorithms based on Support Vector Regression Metamodeling is presented. Appropriate design parameter samples are selected by experimental design theories, and the response samples are obtained from the experiments or numerical simulations, used the SVM method to establish the metamodels of the objective performance functions and constraints, and reconstructed the original optimal problem. The reconstructed metamodels was solved by NSGA-II algorithm and took the structure optimization of the microwave power divider as an example to illustrate the proposed methodology and solve themulti-objective optimization problem. The results show that this methodology is feasible and highly effective, and thus it can be used in the optimum design of engineering fields.

  18. Multi-Objective Genetic Programming with Redundancy-Regulations for Automatic Construction of Image Feature Extractors

    Science.gov (United States)

    Watchareeruetai, Ukrit; Matsumoto, Tetsuya; Takeuchi, Yoshinori; Kudo, Hiroaki; Ohnishi, Noboru

    We propose a new multi-objective genetic programming (MOGP) for automatic construction of image feature extraction programs (FEPs). The proposed method was originated from a well known multi-objective evolutionary algorithm (MOEA), i.e., NSGA-II. The key differences are that redundancy-regulation mechanisms are applied in three main processes of the MOGP, i.e., population truncation, sampling, and offspring generation, to improve population diversity as well as convergence rate. Experimental results indicate that the proposed MOGP-based FEP construction system outperforms the two conventional MOEAs (i.e., NSGA-II and SPEA2) for a test problem. Moreover, we compared the programs constructed by the proposed MOGP with four human-designed object recognition programs. The results show that the constructed programs are better than two human-designed methods and are comparable with the other two human-designed methods for the test problem.

  19. Intra-task variability of trunk coordination during a rate-controlled bipedal dance jump.

    Science.gov (United States)

    Smith, Jo Armour; Siemienski, Adam; Popovich, John M; Kulig, Kornelia

    2012-01-01

    In this study, we investigated trunk coordination during rate-controlled bipedal vertical dance jumps. The aims of the study were to investigate the pattern of coordination and the magnitude of coordination variability within jump phases and relative to phase-defining events during the jump. Lumbar and thoracic kinematics were collected from seven dancers during a series of jumps at 95 beats per minute. The vector coding technique was used to quantify the pattern and variability of trunk coordination. Coordination was predominantly anti-phase during propulsion and landing. Mean coordination variability peaked just before the landing phase and at the transition from landing to propulsion phases, and was lowest during the propulsion phase just before toe-off. The results indicate that peaks in variability could be explained by task and phase-specific biomechanical demands.

  20. Assessment of the operating conditions of coordinated Q-V controller within secondary voltage control system

    Directory of Open Access Journals (Sweden)

    Arnautović Dušan

    2014-01-01

    Full Text Available The paper, discusses the possibility to use coordinated Q-V controller (CQVC to perform secondary voltage control at the power plant level. The CQVC performs the coordination of the synchronous generators' (SG reactive power outputs in order to maintain the same total reactive power delivered by the steam power plant (SPP, while at the same time maintaining a constant voltage with programmed reactive droop characteristic at the SPP HV busbar. This busbar is the natural pilot node for secondary voltage control at HV level as the node with maximum power production and maximum power consumption. In addition to voltage control, the CQVC maintains the uniform allocation of reactive power reserves at all SGs in the power plant. This is accomplished by setting the reactive power of each SG at given operating point in accordance to the available reactive power of the same SG at that point. Different limitations imposed by unit's and plant equipment are superimposed on original SG operating chart (provided by the manufacturer in order to establish realistic limits of SG operation at given operating point. The CQVC facilitates: i practical implementation of secondary voltage control in power system, as it is capable of ensuring delivery of reactive power as requested by regional/voltage control while maintaining voltage at system pilot node, ii the full deployment of available reactive power of SGs which in turn contributes to system stability, iii assessment of the reactive power impact/contribution of each generator in providing voltage control as ancillary service. Furthermore, it is also possible to use CQVC to pricing reactive power production cost at each SG involved and to design reactive power bidding structure for transmission network devices by using recorded data. Practical exploitation experience acquired during CQVC continuous operation for over two years enabled implementation of the optimal setting of reference voltage and droop on daily

  1. Temperature-controlled Hydrothermal Synthesis of Copper(Ⅰ or Ⅱ) Coordination Polymers via a Variety of Copper Coordination Modes

    Institute of Scientific and Technical Information of China (English)

    QU Xue-jian; WANG Shuang; ZHANG Dao-jun; JING Xue-min; ZHANG Li-rong; LI Guang-hua; HUO Qi-sheng; LIU Yun-ling

    2012-01-01

    Two 1D coordination polymers Cu2Ⅰ(C6N3H4)2(1) and CuⅡ(C6N3H4)2·H2O(2) based on benzotriazole(Bta)were hydrothermally synthesized by controlling the crystallization temperature.Single-crystal X-ray diffraction (XRD) analyses reveal that compound 1 is a 1D tubular structure constructed from two types of 1D chains {—Cu—N=N—N—}n,where the Cu(Ⅰ) ions adopt linear,triangular,and tetrahedral coordination modes to connect two types of Bta ligands via π-π interaction inside the tubular-like chain.For compound 2,the Cu(Ⅱ) ions assume a quadrilateral coordination mode linking to the Bta ligands to give 1D straight chains,which stacks through π-π interactions to construct a 2D layer structure.Further characterizations including elemental analyses,infrared IR spectra,thermogravimetric(TG) analyses and luminescence properties have been done.

  2. 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

  3. A modified interactive procedure to solve multi-objective group decision making problem

    OpenAIRE

    Mohammad Izadikhah

    2014-01-01

    Multi-objective optimization and multiple criteria decision making problems are the process of designing the best alternative by considering the incommensurable and conflicting objectives simultaneously. One of the first interactive procedures to solve multiple criteria decision making problems is STEM method. In this paper we propose a modified interactive procedure based on STEM method by calculating the weight vector of objectives which emphasize that more important objectives be closer to...

  4. Collaborative Emission Reduction Model Based on Multi-Objective Optimization for Greenhouse Gases and Air Pollutants.

    Directory of Open Access Journals (Sweden)

    Qing-chun Meng

    Full Text Available 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.

  5. Genetic algorithm-based multi-objective model for scheduling of linear construction projects

    OpenAIRE

    Senouci, Ahmed B.; Al-Derham, H.R.

    2007-01-01

    This paper presents a genetic algorithm-based multi-objective optimization model for the scheduling of linear construction projects. The model allows construction planners to generate and evaluate optimal/near-optimal construction scheduling plans that minimize both project time and cost. The computations in the present model are organized in three major modules. A scheduling module that develops practical schedules for linear construction projects. A cost module that computes the project's c...

  6. Multi-objective Optimization of a Parallel Ankle Rehabilitation Robot Using Modified Differential Evolution Algorithm

    Institute of Scientific and Technical Information of China (English)

    WANG Congzhe; FANG Yuefa; GUO Sheng

    2015-01-01

    Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution(DE) algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.

  7. Low-Carbon Based Multi-Objective Bi-Level Power Dispatching under Uncertainty

    OpenAIRE

    2016-01-01

    This research examines a low-carbon power dispatch problem under uncertainty. A hybrid uncertain multi-objective bi-level model with one leader and multiple followers is established to support the decision making of power dispatch and generation. The upper level decision maker is the regional power grid corporation which allocates power quotas to each follower based on the objectives of reasonable returns, a small power surplus and low carbon emissions. The lower level decision makers are the...

  8. 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.

  9. Multi-objective Truss Optimization Using Different Types of the BB-BC Algorithm

    Directory of Open Access Journals (Sweden)

    Milajić Aleksandar

    2016-01-01

    Full Text Available Optimum design of truss structures is considered as a benchmark problem in the field of the structural optimization. In order to solve this hard combinatorial problem, it is necessary to implement adequate optimization tool that would provide sufficiently wide range of possible solutions within a reasonable time as well as to obtain good exploration and exploitation of search space. The aim of presented study was to compare efficiency of different multi-objective algorithms in solving this task.

  10. 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.

  11. Using multi-objective optimisation to integrate alpine regions in groundwater flow models

    Directory of Open Access Journals (Sweden)

    V. Rojanschi

    2005-01-01

    Full Text Available Within the research project GLOWA Danube, a groundwater flow model was developed for the Upper Danube basin. This paper reports on a preliminary study to include the alpine part of the catchment in the model. A conceptual model structure was implemented and tested using multi-objective optimisation analysis. The performance of the model and the identifiability of the parameters were studied. A possible over-parameterisation of the model was also tested using principal component analysis.

  12. A New Non-dominated Sorting Genetic Algorithm for Multi-Objective Optimization

    OpenAIRE

    2010-01-01

    This study imitates the gene-therapy process at the forefront of medicine and proposes an innovative evaluative crossover operator. The evaluative crossover integrates a geneevaluation method with a gene-therapy approach in the traditional NSGA-II for finding uniformly distributed Pareto-optimal front of multi-objective optimization problems. To further enhance the advantages of fast non-dominate sorting and diversity preservation in NSGA-II, the proposed gene-evaluation method partially eval...

  13. Comparison of intelligent fuzzy based AGC coordinated PID controlled and PSS controlled AVR system

    Energy Technology Data Exchange (ETDEWEB)

    Mukherjee, V. [Department of Electrical Engineering, Asansol Engineering College, Asansol, West Bengal (India); Ghoshal, S.P. [Department of Electrical Engineering, National Institute of Technology, Durgapur, West Bengal (India)

    2007-11-15

    This paper attempts to investigate the performance of intelligent fuzzy based coordinated control of the Automatic Generation Control (AGC) loop and the excitation loop equipped with Proportional Integral Derivative (PID) controlled Automatic Voltage Regulator (AVR) system and Power System Stabilizer (PSS) controlled AVR system. The work establishes that PSS controlled AVR system is much more robust in dynamic performance of the system over a wide range of system operating configurations. Thus, it is revealed that PSS equipped AVR is much more superior than PID equipped AVR in damping the oscillation resulting in improved transient response. The paper utilizes a novel class of Particle Swarm Optimization (PSO) termed as Craziness based Particle Swarm Optimization (CRPSO) as optimizing tool to get optimal tuning of PSS parameters as well as the gains of PID controllers. For on-line, off-nominal operating conditions Takagi Sugeno Fuzzy Logic (TSFL) has been applied to obtain the off-nominal optimal gains of PID controllers and parameters of PSS. Implementation of TSFL helps to achieve very fast dynamic response. Fourth order model of generator with AVR and high gain thyristor excitation system is considered for PSS controlled system while normal gain exciter is considered for PID controlled system. Simulation study also reveals that with high gain exciter, PID control is not at all effective. Transient responses are achieved by using modal analysis. (author)

  14. Multi-objective optimization to predict muscle tensions in a pinch function using genetic algorithm

    Science.gov (United States)

    Bensghaier, Amani; Romdhane, Lotfi; Benouezdou, Fethi

    2012-03-01

    This work is focused on the determination of the thumb and the index finger muscle tensions in a tip pinch task. A biomechanical model of the musculoskeletal system of the thumb and the index finger is developed. Due to the assumptions made in carrying out the biomechanical model, the formulated force analysis problem is indeterminate leading to an infinite number of solutions. Thus, constrained single and multi-objective optimization methodologies are used in order to explore the muscular redundancy and to predict optimal muscle tension distributions. Various models are investigated using the optimization process. The basic criteria to minimize are the sum of the muscle stresses, the sum of individual muscle tensions and the maximum muscle stress. The multi-objective optimization is solved using a Pareto genetic algorithm to obtain non-dominated solutions, defined as the set of optimal distributions of muscle tensions. The results show the advantage of the multi-objective formulation over the single objective one. The obtained solutions are compared to those available in the literature demonstrating the effectiveness of our approach in the analysis of the fingers musculoskeletal systems when predicting muscle tensions.

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

    Directory of Open Access Journals (Sweden)

    Warid Warid

    Full Text Available 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.

  16. Mauna Kea Spectrographic Explorer (MSE): a conceptual design for multi-object high resolution spectrograph

    Science.gov (United States)

    Zhang, Kai; Zhu, Yongtian; Hu, Zhongwen

    2016-08-01

    The Maunakea Spectroscopic Explorer (MSE) project will transform the CFHT 3.6m optical telescope into a 10m class dedicated multi-object spectroscopic facility, with an ability to simultaneously measure thousands of objects with a spectral resolution range spanning 2,000 to 40,000. MSE will develop two spectrographic facilities to meet the science requirements. These are respectively, the Low/Medium Resolution spectrographs (LMRS) and High Resolution spectrographs (HRS). Multi-object high resolution spectrographs with total of 1,156 fibers is a big challenge, one that has never been attempted for a 10m class telescope. To date, most spectral survey facilities work in single order low/medium resolution mode, and only a few Wide Field Spectrographs (WFS) provide a cross-dispersion high resolution mode with a limited number of orders. Nanjing Institute of Astronomical Optics and Technology (NIAOT) propose a conceptual design with the use of novel image slicer arrays and single order immersed Volume Phase Holographic (VPH) grating for the MSE multi-object high resolution spectrographs. The conceptual scheme contains six identical fiber-link spectrographs, each of which simultaneously covers three restricted bands (λ/30, λ/30, λ/15) in the optical regime, with spectral resolution of 40,000 in Blue/Visible bands (400nm / 490nm) and 20,000 in Red band (650nm). The details of the design is presented in this paper.

  17. Multi-Objective Synthesis of Filtering Dipole Array Based on Tuning-Space Mapping

    Directory of Open Access Journals (Sweden)

    P. Vsetula

    2015-09-01

    Full Text Available In the paper, we apply tuning-space mapping to multi-objective synthesis of a filtering antenna. The antenna is going to be implemented as a planar dipole array with serial feeding. Thanks to the multi-objective approach, we can deal with conflicting requirements on gain, impedance matching, side-lobe level, and main-lobe direction. MOSOMA algorithm is applied to compute Pareto front of optimal solutions by changing lengths of dipoles and parameters of transmission lines connecting them into a serial array. Exploitation of tuning space mapping significantly reduces CPU-time demands of the multi-objective synthesis: a coarse optimization evaluates objectives using a wire model of the filtering array (4NEC2, method of moments, and a fine optimization exploits a realistic planar model of the array (CST Microwave Studio, finite integration technique. The synthesized filtering antenna was manufactured, and its parameters were measured to be compared with objectives. The number of dipoles in the array is shown to influence the match of measured parameters and objectives.

  18. 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.

  19. Relevance and Applicability of Multi-objective Resource Constrained Project Scheduling Problem: Review Article

    Directory of Open Access Journals (Sweden)

    V. Oladokun

    2011-12-01

    Full Text Available Resource-Constrained Project Scheduling Problem (RCPSP is a Non Polynomial (NP - Hard optimization problem that considers how to assign activities to available resources in order to meet predefined objectives. The problem is usually characterized by precedence relationship between activities with limited capacity of renewable resources. In an environment where resources are limited, projects still have to be finished on time, within the approved budget and in accordance with the preset specifications. Inherently, these tend to make RCPSP, a multi-objective problem. However, it has been treated as a single objective problem with project makespan often recognized as the most relevant objective. As a result of not understanding the multi-objective dimension of some projects, where these objectives need to be simultaneously considered, distraction and conflict of interest have ultimately lead to abandoned or totally failed projects. The aim of this article is to holistically review the relevance and applicability of multi-objective performance dimension of RCPSP in an environment where optimal use of limited resources is important.

  20. CFD-based multi-objective optimization method for ship design

    Science.gov (United States)

    Tahara, Yusuke; Tohyama, Satoshi; Katsui, Tokihiro

    2006-10-01

    This paper concerns development and demonstration of a computational fluid dynamics (CFD)-based multi-objective optimization method for ship design. Three main components of the method, i.e. computer-aided design (CAD), CFD, and optimizer modules are functionally independent and replaceable. The CAD used in the present study is NAPA system, which is one of the leading CAD systems in ship design. The CFD method is FLOWPACK version 2004d, a Reynolds-averaged Navier-Stokes (RaNS) solver developed by the present authors. The CFD method is implemented into a self-propulsion simulator, where the RaNS solver is coupled with a propeller-performance program. In addition, a maneuvering simulation model is developed and applied to predict ship maneuverability performance. Two nonlinear optimization algorithms are used in the present study, i.e. the successive quadratic programming and the multi-objective genetic algorithm, while the former is mainly used to verify the results from the latter. For demonstration of the present method, a multi-objective optimization problem is formulated where ship propulsion and maneuverability performances are considered. That is, the aim is to simultaneously minimize opposite hydrodynamic performances in design tradeoff. In the following, an overview of the present method is given, and results are presented and discussed for tanker stern optimization problem including detailed verification work on the present numerical schemes.

  1. Multi-objective calibration of a distributed hydrological model (WetSpa using a genetic algorithm

    Directory of Open Access Journals (Sweden)

    M. Shafii

    2009-01-01

    Full Text Available A multi-objective genetic algorithm, NSGA-II, is applied to calibrate a distributed hydrological model (WetSpa for predicting river discharge. The evaluation criteria considered are the model bias (mass balance, the model efficiency (Nash-Sutcliffe efficiency, and a logarithmic transformed model efficiency (to emphasize low-flow values. The concept of Pareto dominance is used to solve the multi-objective optimization problem and derive Pareto-optimal parameter sets. In order to analyze the applicability of the approach, a comparison is made with another calibration routine using the parameter estimator PEST to minimize the model efficiency. The two approaches are evaluated by applying the WetSpa model to the Hornad River (Slovakia for which observations of daily precipitation, temperature, potential evapotranspiration, and discharge are available for a 10 year period (1991–2000. The first 5 years of the data series are used for model calibration, while the second 5 years for model validation. The results revealed that the quality of the solutions obtained with NSGA-II is comparable or even better to what can be obtained with PEST, considering the same assumptions. Hence, NSGA-II is capable of locating Pareto optimal solutions in the parameter search space and the results obtained prove the excellent performance of the multi-objective model calibration methodology.

  2. An archived multi-objective simulated annealing for a dynamic cellular manufacturing system

    Science.gov (United States)

    Shirazi, Hossein; Kia, Reza; Javadian, Nikbakhsh; Tavakkoli-Moghaddam, Reza

    2014-05-01

    To design a group layout of a cellular manufacturing system (CMS) in a dynamic environment, a multi-objective mixed-integer non-linear programming model is developed. The model integrates cell formation, group layout and production planning (PP) as three interrelated decisions involved in the design of a CMS. This paper provides an extensive coverage of important manufacturing features used in the design of CMSs and enhances the flexibility of an existing model in handling the fluctuations of part demands more economically by adding machine depot and PP decisions. Two conflicting objectives to be minimized are the total costs and the imbalance of workload among cells. As the considered objectives in this model are in conflict with each other, an archived multi-objective simulated annealing (AMOSA) algorithm is designed to find Pareto-optimal solutions. Matrix-based solution representation, a heuristic procedure generating an initial and feasible solution and efficient mutation operators are the advantages of the designed AMOSA. To demonstrate the efficiency of the proposed algorithm, the performance of AMOSA is compared with an exact algorithm (i.e., ∈-constraint method) solved by the GAMS software and a well-known evolutionary algorithm, namely NSGA-II for some randomly generated problems based on some comparison metrics. The obtained results show that the designed AMOSA can obtain satisfactory solutions for the multi-objective model.

  3. Multi-objective calibration of a distributed hydrological model (WetSpa) using a genetic algorithm

    Science.gov (United States)

    Shafii, M.; de Smedt, F.

    2009-11-01

    A multi-objective genetic algorithm, NSGA-II, is applied to calibrate a distributed hydrological model (WetSpa) for prediction of river discharges. The goals of this study include (i) analysis of the applicability of multi-objective approach for WetSpa calibration instead of the traditional approach, i.e. the Parameter ESTimator software (PEST), and (ii) identifiability assessment of model parameters. The objective functions considered are model efficiency (Nash-Sutcliffe criterion) known to be biased for high flows, and model efficiency for logarithmic transformed discharges to emphasize low-flow values. For the multi-objective approach, Pareto-optimal parameter sets are derived, whereas for the single-objective formulation, PEST is applied to give optimal parameter sets. The two approaches are evaluated by applying the WetSpa model to predict daily discharges in the Hornad River (Slovakia) for a 10 year period (1991-2000). The results reveal that NSGA-II performs favourably well to locate Pareto optimal solutions in the parameters search space. Furthermore, identifiability analysis of the WetSpa model parameters shows that most parameters are well-identifiable. However, in order to perform an appropriate model evaluation, more efforts should be focused on improving calibration concepts and to define robust methods to quantify different sources of uncertainties involved in the calibration procedure.

  4. Multi-Objective Differential Evolution for Voltage Security Constrained Optimal Power Flow in Deregulated Power Systems

    Science.gov (United States)

    Roselyn, J. Preetha; Devaraj, D.; Dash, Subhransu Sekhar

    2013-11-01

    Voltage stability is an important issue in the planning and operation of deregulated power systems. The voltage stability problems is a most challenging one for the system operators in deregulated power systems because of the intense use of transmission line capabilities and poor regulation in market environment. This article addresses the congestion management problem avoiding offline transmission capacity limits related to voltage stability by considering Voltage Security Constrained Optimal Power Flow (VSCOPF) problem in deregulated environment. This article presents the application of Multi Objective Differential Evolution (MODE) algorithm to solve the VSCOPF problem in new competitive power systems. The maximum of L-index of the load buses is taken as the indicator of voltage stability and is incorporated in the Optimal Power Flow (OPF) problem. The proposed method in hybrid power market which also gives solutions to voltage stability problems by considering the generation rescheduling cost and load shedding cost which relieves the congestion problem in deregulated environment. The buses for load shedding are selected based on the minimum eigen value of Jacobian with respect to the load shed. In the proposed approach, real power settings of generators in base case and contingency cases, generator bus voltage magnitudes, real and reactive power demands of selected load buses using sensitivity analysis are taken as the control variables and are represented as the combination of floating point numbers and integers. DE/randSF/1/bin strategy scheme of differential evolution with self-tuned parameter which employs binomial crossover and difference vector based mutation is used for the VSCOPF problem. A fuzzy based mechanism is employed to get the best compromise solution from the pareto front to aid the decision maker. The proposed VSCOPF planning model is implemented on IEEE 30-bus system, IEEE 57 bus practical system and IEEE 118 bus system. The pareto optimal

  5. Optimal Design of Groundwater Remediation Problems under Uncertainty Using Probabilistic Multi-objective Evolutionary Technique

    Science.gov (United States)

    Yang, Y.; Wu, J.

    2011-12-01

    The previous work in the field of multi-objective optimization under uncertainty has concerned with the probabilistic multi-objective algorithm itself, how to effectively evaluate an estimate of uncertain objectives and identify a set of reliable Pareto optimal solutions. However, the design of a robust and reliable groundwater remediation system encounters major difficulties owing to the inherent uncertainty of hydrogeological parameters such as hydraulic conductivity (K). Thus, we need to make reduction of uncertainty associated with the site characteristics of the contaminated aquifers. In this study, we first use the Sequential Gaussian Simulation (SGSIM) to generate 1000 conditional realizations of lnK based on the sampled conditioning data acquired by field test. It is worthwhile to note that the cost for field test often weighs heavily upon the remediation cost and must thus be taken into account in the tradeoff between the solution reliability and remedial cost optimality. In this situation, we perform Monte Carlo simulation to make an uncertainty analysis of lnK realizations associated with the different number of conditioning data points. The results indicate that the uncertainty of the site characteristics and the contaminant concentration output from transport model is decreasing and then tends toward stabilization with the increase of conditioning data. This study presents a probabilistic multi-objective evolutionary algorithm (PMOEA) that integrates noisy genetic algorithm (NGA) and probabilistic multi-objective genetic algorithm (MOGA). The evident difference between deterministic MOGA and probabilistic MOGA is the use of probabilistic Pareto domination ranking and niche technique to ensure that each solution found is most reliable and robust. The proposed algorithm is then evaluated through a synthetic pump-and-treat (PAT) groundwater remediation test case. The 1000 lnK realizations generated by SGSIM with appropriate number of conditioning data (30

  6. 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

  7. Bi-Level Multi-Objective Absolute-Value Fractional programming Problems: A Fuzzy Goal Programming approach

    Directory of Open Access Journals (Sweden)

    Mansour Saraj

    2012-06-01

    Full Text Available In this paper we propose a fuzzy goal programming method for obtaining a satisfactory solution to a bi-level multi-objective absolute-value fractional programming (BLMO-A-FP problems. In the proposed approach, the membership functions for the de ned fuzzy goals of all objective functions at the two levels as well as the membership functions for vector of fuzzy goals of the decision variables controlled by upper level decision maker (ULDM are developed in the model formulation of the problem. Then fuzzy goal programming technique is used for achieving highest degree of each of the membership goals by minimizing negative and positive deviational variables. The method of variable change on the under- and over-deviational variables of the membership goals associated with the fuzzy goals of the model is introduced to solve the problem eciently by using linear goal programming methodology. Theoretical results is illustrated with the help of a numerical.

  8. Optimized two-level placement of test points for multi-objective air monitoring of the Three Gorges Reservoir area

    Institute of Scientific and Technical Information of China (English)

    XIAO Dong-hai; TAN Chun-lu; WANG Jun-qiang; ZHONG Yuan-chang

    2007-01-01

    To fit the complicated geographic conditions of the Three Gorges Reservoir area, a two-level multi-objective monitoring system was developed to monitor the atmosphere of the area. Statistical analysis of environmental monitoring data and the macro control principle were employed to configure the upper layer. The lower layer was designed by the application of the thumb rule to a local terrain and specific point sources of pollution therein. The optimized two-level system comprises an upper layer of 16 monitoring stations distributed at places of diverse geographical, ecological, economical and social characteristics, and a lower layer of 16 sub-machines at each monitoring station of the upper layer. This optimal outcome fits the complicated conditions of the Three Gorges Reservoir area, substantially cuts down the installation cost and the operation cost, and provides accurate monitoring data of atmosphere over the entire area with a high resolution.

  9. Concepts and Methods in Multi-Person Coordination and Control.

    Science.gov (United States)

    1981-10-01

    IEEE Transactions on Automatic Control , vol AC-25, no. 6, pp. 1154...two-person quadratic deci- sion problems with static information structures", IEEE Transactions on Automatic Control , vol. AC-20, pp. 320-328. [6] Baar...T. (1978a), "Decentralized multicriteria optimization of linear stochastic systems," IEEE Transactions on Automatic Control , vol. AC-23, no. 3,

  10. Coordinated alpha and gamma control of muscles and spindles in movement and posture

    OpenAIRE

    Si eLi; Cheng eZhuang; Manzhao eHao; Xin eHe; Juan Carlos eMarquez Ruiz; Chuanxin Minos Niu; Ning eLan

    2015-01-01

    Mounting evidence suggests that both α and γ motoneurons are active during movement and posture, but how does the central motor system coordinate the α-γ controls in these tasks remains sketchy due to lack of in vivo data. Here a computational model of α-γ control of muscles and spindles was used to investigate α-γ integration and coordination for movement and posture. The model comprised physiologically realistic spinal circuitry, muscles, proprioceptors, and skeletal biomechanics. In the mo...

  11. Wide-Area Robust Decentralized Coordinated Control of HVDC Power System Based on Polytopic System Theory

    Directory of Open Access Journals (Sweden)

    Shiyun Xu

    2015-01-01

    Full Text Available The present study proposes a hierarchical wide-area decentralized coordinated control framework for HVDC power system that is robust to multiple operating conditions. The upper level wide-area coordinated controller is designed in the form of dynamic output feedback control that coordinates the lower level HVDC supplementary controller, PSS, and SVC. In order to enhance the robustness of the designed controller under various operating conditions, the polytopic model is introduced such that the closed-loop control system can be operated under strong damping mode in virtue of the stability criterion based on damping ratio. Simulation results demonstrate that the proposed controller design algorithm is capable of enhancing the system damping over four different conditions.

  12. Multi Objective Genetic Algorithm for Congestion Management in Deregulated Power System Using Generator Rescheduling and Facts Devices

    Directory of Open Access Journals (Sweden)

    S. Sivakumar

    2014-10-01

    Full Text Available The problem of congestion management is more pronounced in deregulated environment as the participants of the energy market are market oriented rather than socially responsible-as exhibited by the government operated bundled system. Customers would like to purchase the electricity from the cheapest available sources. The seller in energy market would like to derive more benefit out of their investments, engages with contracts that may lead to overloading of the transmission elements of the power system. An Independent System Operator (ISO who has no vested interest in the energy market, coordinates the trades and make sure that the interconnected power system always operates in a secure state at a minimum cost by meeting the all the load requirements and losses. In this proposed study, Congestion is mitigated by Generator Rescheduling and implementation of FACTS devices. Minimization of rescheduling costs of the generator and minimization of the cost of deploying FACTS devices are taken as the objectives of the given multi-objective optimization problem. Non-dominated sorting genetic algorithm II is used to solve this problem by implementing the series FACTS device namely TCSC and shunt FACTS device namely SVC. The proposed algorithm is tested on IEEE 30 bus system.

  13. Coordinated joint motion control system with position error correction

    Energy Technology Data Exchange (ETDEWEB)

    Danko, George (Reno, NV)

    2011-11-22

    Disclosed are an articulated hydraulic machine supporting, control system and control method for same. The articulated hydraulic machine has an end effector for performing useful work. The control system is capable of controlling the end effector for automated movement along a preselected trajectory. The control system has a position error correction system to correct discrepancies between an actual end effector trajectory and a desired end effector trajectory. The correction system can employ one or more absolute position signals provided by one or more acceleration sensors supported by one or more movable machine elements. Good trajectory positioning and repeatability can be obtained. A two-joystick controller system is enabled, which can in some cases facilitate the operator's task and enhance their work quality and productivity.

  14. Coordinated joint motion control system with position error correction

    Energy Technology Data Exchange (ETDEWEB)

    Danko, George L.

    2016-04-05

    Disclosed are an articulated hydraulic machine supporting, control system and control method for same. The articulated hydraulic machine has an end effector for performing useful work. The control system is capable of controlling the end effector for automated movement along a preselected trajectory. The control system has a position error correction system to correct discrepancies between an actual end effector trajectory and a desired end effector trajectory. The correction system can employ one or more absolute position signals provided by one or more acceleration sensors supported by one or more movable machine elements. Good trajectory positioning and repeatability can be obtained. A two joystick controller system is enabled, which can in some cases facilitate the operator's task and enhance their work quality and productivity.

  15. A Novel Torque Coordination Control Strategy of a Single-Shaft Parallel Hybrid Electric Vehicle Based on Model Predictive Control

    Directory of Open Access Journals (Sweden)

    Jing Sun

    2015-01-01

    Full Text Available The torque coordination control during mode transition is a very important task for hybrid electric vehicle (HEV with a clutch serving as the key enabling actuator element. Poor coordination will deteriorate the drivability of the driver and lead to excessive wearing to the clutch friction plates. In this paper, a novel torque coordination control strategy for a single-shaft parallel hybrid electric vehicle is presented to coordinate the motor torque, engine torque, and clutch torque so that the seamless mode switching can be achieved. Different to the existing model predictive control (MPC methods, only one model predictive controller is needed and the clutch torque is taken as an optimized variable rather than a known parameter. Furthermore, the successful idea of model reference control (MRC is also used for reference to generate the set-point signal required by MPC. The parameter sensitivity is studied for better performance of the proposed model predictive controller. The simulation results validate that the proposed novel torque coordination control strategy has less vehicle jerk, less torque interruption, and smaller clutch frictional losses, compared with the baseline method. In addition, the sensitivity and adaptiveness of the proposed novel torque coordination control strategy are evaluated.

  16. Evaluating the epsilon-domination based multi-objective evolutionary algorithm for a quick computation of Pareto-optimal solutions.

    Science.gov (United States)

    Deb, Kalyanmoy; Mohan, Manikanth; Mishra, Shikhar

    2005-01-01

    Since the suggestion of a computing procedure of multiple Pareto-optimal solutions in multi-objective optimization problems in the early Nineties, researchers have been on the look out for a procedure which is computationally fast and simultaneously capable of finding a well-converged and well-distributed set of solutions. Most multi-objective evolutionary algorithms (MOEAs) developed in the past decade are either good for achieving a well-distributed solutions at the expense of a large computational effort or computationally fast at the expense of achieving a not-so-good distribution of solutions. For example, although the Strength Pareto Evolutionary Algorithm or SPEA (Zitzler and Thiele, 1999) produces a much better distribution compared to the elitist non-dominated sorting GA or NSGA-II (Deb et al., 2002a), the computational time needed to run SPEA is much greater. In this paper, we evaluate a recently-proposed steady-state MOEA (Deb et al., 2003) which was developed based on the epsilon-dominance concept introduced earlier(Laumanns et al., 2002) and using efficient parent and archive update strategies for achieving a well-distributed and well-converged set of solutions quickly. Based on an extensive comparative study with four other state-of-the-art MOEAs on a number of two, three, and four objective test problems, it is observed that the steady-state MOEA is a good compromise in terms of convergence near to the Pareto-optimal front, diversity of solutions, and computational time. Moreover, the epsilon-MOEA is a step closer towards making MOEAs pragmatic, particularly allowing a decision-maker to control the achievable accuracy in the obtained Pareto-optimal solutions.

  17. Coordinated Voltage Control in Offshore HVDC Connected Cluster of Wind Power Plants

    DEFF Research Database (Denmark)

    Sakamuri, Jayachandra N.; Rather, Zakir Hussain; Rimez, Johan;

    2016-01-01

    This paper presents a coordinated voltage control scheme (CVCS) for a cluster of offshore wind power plants (OWPPs) connected to a VSC HVDC system. The primary control point of the proposed voltage control scheme is the introduced Pilot bus, which is having the highest short circuit capacity...

  18. A control architecture to coordinate distributed generators and active power filters coexisting in a microgrid

    DEFF Research Database (Denmark)

    Hashempour, Mohammad M.; Savaghebi, Mehdi; Quintero, Juan Carlos Vasquez

    2016-01-01

    This paper proposes a control architecture of distributed generators (DGs) inverters and shunt active power filters (APFs) in microgrids to compensate voltage harmonics in a coordinated way. For this, a hierarchical control structure is proposed that includes two control levels. The primary (local...

  19. A Control Architecture to Coordinate Distributed Generators and Active Power Filters Coexisting in a Microgrid

    DEFF Research Database (Denmark)

    Hashempour, Mohammad M.; Firoozabadi, Mehdi Savaghebi; Quintero, Juan Carlos Vasquez

    2016-01-01

    This paper proposes a control architecture of distributed generators (DGs) inverters and shunt active power filters (APFs) in microgrids to compensate voltage harmonics in a coordinated way. For this, a hierarchical control structure is proposed that includes two control levels. The primary (local...

  20. Intelligent system of coordination and control for manufacturing

    Science.gov (United States)

    Ciortea, E. M.

    2016-08-01

    This paper wants shaping an intelligent system monitoring and control, which leads to optimizing material and information flows of the company. The paper presents a model for tracking and control system using intelligent real. Production system proposed for simulation analysis provides the ability to track and control the process in real time. Using simulation models be understood: the influence of changes in system structure, commands influence on the general condition of the manufacturing process conditions influence the behavior of some system parameters. Practical character consists of tracking and real-time control of the technological process. It is based on modular systems analyzed using mathematical models, graphic-analytical sizing, configuration, optimization and simulation.