HEURISTIC APPROACHES FOR PORTFOLIO OPTIMIZATION
Manfred Gilli, Evis Kellezi
2000-01-01
The paper first compares the use of optimization heuristics to the classical optimization techniques for the selection of optimal portfolios. Second, the heuristic approach is applied to problems other than those in the standard mean-variance framework where the classical optimization fails.
Directory of Open Access Journals (Sweden)
Christopher Expósito-Izquierdo
2017-02-01
Full Text Available This paper summarizes the main contributions of the Ph.D. thesis of Christopher Exp\\'osito-Izquierdo. This thesis seeks to develop a wide set of intelligent heuristic and meta-heuristic algorithms aimed at solving some of the most highlighted optimization problems associated with the transshipment and storage of containers at conventional maritime container terminals. Under the premise that no optimization technique can have a better performance than any other technique under all possible assumptions, the main point of interest in the domain of maritime logistics is to propose optimization techniques superior in terms of effectiveness and computational efficiency to previous proposals found in the scientific literature when solving individual optimization problems under realistic scenarios. Simultaneously, these optimization techniques should be enough competitive to be potentially implemented in practice. }}
Directory of Open Access Journals (Sweden)
Jeevanandham Arumugam
2009-01-01
Full Text Available In this paper a classical lead-lag power system stabilizer is used for demonstration. The stabilizer parameters are selected in such a manner to damp the rotor oscillations. The problem of selecting the stabilizer parameters is converted to a simple optimization problem with an eigen value based objective function and it is proposed to employ simulated annealing and particle swarm optimization for solving the optimization problem. The objective function allows the selection of the stabilizer parameters to optimally place the closed-loop eigen values in the left hand side of the complex s-plane. The single machine connected to infinite bus system and 10-machine 39-bus system are considered for this study. The effectiveness of the stabilizer tuned using the best technique, in enhancing the stability of power system. Stability is confirmed through eigen value analysis and simulation results and suitable heuristic technique will be selected for the best performance of the system.
DEFF Research Database (Denmark)
Ding, Yi; Goel, Lalit; Wang, Peng
2012-01-01
cost of the system will also increase. The reserve structure of a MSS should be determined based on striking a balance between the required reliability and the reserve cost. The objective of reserve management for a MSS is to schedule the reserve at the minimum system reserve cost while maintaining......Electric power generating systems are typical examples of multi-state systems (MSS). Sufficient reserve is critically important for maintaining generating system reliabilities. The reliability of a system can be increased by increasing the reserve capacity, noting that at the same time the reserve...... the required level of supply reliability to its customers. In previous research, Genetic Algorithm (GA) has been used to solve most reliability optimization problems. However, the GA is not very computationally efficient in some cases. In this chapter a new heuristic optimization technique—the particle swarm...
International Nuclear Information System (INIS)
Castillo, Alejandro; Martín-del-Campo, Cecilia; Montes-Tadeo, José-Luis; François, Juan-Luis; Ortiz-Servin, Juan-José; Perusquía-del-Cueto, Raúl
2014-01-01
Highlights: • Different metaheuristic optimization techniques were compared. • The optimal enrichment and gadolinia distribution in a BWR fuel lattice was studied. • A decision making tool based on the Position Vector of Minimum Regret was applied. • Similar results were found for the different optimization techniques. - Abstract: In the present study a comparison of the performance of five heuristic techniques for optimization of combinatorial problems is shown. The techniques are: Ant Colony System, Artificial Neural Networks, Genetic Algorithms, Greedy Search and a hybrid of Path Relinking and Scatter Search. They were applied to obtain an “optimal” enrichment and gadolinia distribution in a fuel lattice of a boiling water reactor. All techniques used the same objective function for qualifying the different distributions created during the optimization process as well as the same initial conditions and restrictions. The parameters included in the objective function are the k-infinite multiplication factor, the maximum local power peaking factor, the average enrichment and the average gadolinia concentration of the lattice. The CASMO-4 code was used to obtain the neutronic parameters. The criteria for qualifying the optimization techniques include also the evaluation of the best lattice with burnup and the number of evaluations of the objective function needed to obtain the best solution. In conclusion all techniques obtain similar results, but there are methods that found better solutions faster than others. A decision analysis tool based on the Position Vector of Minimum Regret was applied to aggregate the criteria in order to rank the solutions according to three functions: neutronic grade at 0 burnup, neutronic grade with burnup and global cost which aggregates the computing time in the decision. According to the results Greedy Search found the best lattice in terms of the neutronic grade at 0 burnup and also with burnup. However, Greedy Search is
Comparison of Heuristics for Inhibitory Rule Optimization
Alsolami, Fawaz; Chikalov, Igor; Moshkov, Mikhail
2014-01-01
Friedman test with Nemenyi post-hoc are used to compare the greedy algorithms statistically against each other for length and coverage. The experiments are carried out on real datasets from UCI Machine Learning Repository. For leading heuristics, the constructed rules are compared with optimal ones obtained based on dynamic programming approach. The results seem to be promising for the best heuristics: the average relative difference between length (coverage) of constructed and optimal rules is at most 2.27% (7%, respectively). Furthermore, the quality of classifiers based on sets of inhibitory rules constructed by the considered heuristics are compared against each other, and the results show that the three best heuristics from the point of view classification accuracy coincides with the three well-performed heuristics from the point of view of rule length minimization.
Comparison of Heuristics for Inhibitory Rule Optimization
Alsolami, Fawaz
2014-09-13
Knowledge representation and extraction are very important tasks in data mining. In this work, we proposed a variety of rule-based greedy algorithms that able to obtain knowledge contained in a given dataset as a series of inhibitory rules containing an expression “attribute ≠ value” on the right-hand side. The main goal of this paper is to determine based on rule characteristics, rule length and coverage, whether the proposed rule heuristics are statistically significantly different or not; if so, we aim to identify the best performing rule heuristics for minimization of rule length and maximization of rule coverage. Friedman test with Nemenyi post-hoc are used to compare the greedy algorithms statistically against each other for length and coverage. The experiments are carried out on real datasets from UCI Machine Learning Repository. For leading heuristics, the constructed rules are compared with optimal ones obtained based on dynamic programming approach. The results seem to be promising for the best heuristics: the average relative difference between length (coverage) of constructed and optimal rules is at most 2.27% (7%, respectively). Furthermore, the quality of classifiers based on sets of inhibitory rules constructed by the considered heuristics are compared against each other, and the results show that the three best heuristics from the point of view classification accuracy coincides with the three well-performed heuristics from the point of view of rule length minimization.
Engineering applications of heuristic multilevel optimization methods
Barthelemy, Jean-Francois M.
1989-01-01
Some engineering applications of heuristic multilevel optimization methods are presented and the discussion focuses on the dependency matrix that indicates the relationship between problem functions and variables. Coordination of the subproblem optimizations is shown to be typically achieved through the use of exact or approximate sensitivity analysis. Areas for further development are identified.
Directory of Open Access Journals (Sweden)
Mohammad Dreidy
2017-01-01
Full Text Available Recently, several environmental problems are beginning to affect all aspects of life. For this reason, many governments and international agencies have expressed great interest in using more renewable energy sources (RESs. However, integrating more RESs with distribution networks resulted in several critical problems vis-à-vis the frequency stability, which might lead to a complete blackout if not properly treated. Therefore, this paper proposed a new Under Frequency Load Shedding (UFLS scheme for islanding distribution network. This scheme uses three meta-heuristics techniques, binary evolutionary programming (BEP, Binary genetic algorithm (BGA, and Binary particle swarm optimization (BPSO, to determine the optimal combination of loads that needs to be shed from the islanded distribution network. Compared with existing UFLS schemes using fixed priority loads, the proposed scheme has the ability to restore the network frequency without any overshooting. Furthermore, in terms of execution time, the simulation results show that the BEP technique is fast enough to shed the optimal combination of loads compared with BGA and BPSO techniques.
Chaudhuri, Sutapa; Goswami, Sayantika; Das, Debanjana; Middey, Anirban
2014-05-01
Forecasting summer monsoon rainfall with precision becomes crucial for the farmers to plan for harvesting in a country like India where the national economy is mostly based on regional agriculture. The forecast of monsoon rainfall based on artificial neural network is a well-researched problem. In the present study, the meta-heuristic ant colony optimization (ACO) technique is implemented to forecast the amount of summer monsoon rainfall for the next day over Kolkata (22.6°N, 88.4°E), India. The ACO technique belongs to swarm intelligence and simulates the decision-making processes of ant colony similar to other adaptive learning techniques. ACO technique takes inspiration from the foraging behaviour of some ant species. The ants deposit pheromone on the ground in order to mark a favourable path that should be followed by other members of the colony. A range of rainfall amount replicating the pheromone concentration is evaluated during the summer monsoon season. The maximum amount of rainfall during summer monsoon season (June—September) is observed to be within the range of 7.5-35 mm during the period from 1998 to 2007, which is in the range 4 category set by the India Meteorological Department (IMD). The result reveals that the accuracy in forecasting the amount of rainfall for the next day during the summer monsoon season using ACO technique is 95 % where as the forecast accuracy is 83 % with Markov chain model (MCM). The forecast through ACO and MCM are compared with other existing models and validated with IMD observations from 2008 to 2012.
DEFF Research Database (Denmark)
Vlachogiannis, Ioannis (John); Lee, KY
2009-01-01
In this paper an improved coordinated aggregation-based particle swarm optimization (ICA-PSO) algorithm is introduced for solving the optimal economic load dispatch (ELD) problem in power systems. In the ICA-PSO algorithm each particle in the swarm retains a memory of its best position ever...... encountered, and is attracted only by other particles with better achievements than its own with the exception of the particle with the best achievement, which moves randomly. Moreover, the population size is increased adaptively, the number of search intervals for the particles is selected adaptively...
Heuristic optimization in penumbral image for high resolution reconstructed image
International Nuclear Information System (INIS)
Azuma, R.; Nozaki, S.; Fujioka, S.; Chen, Y. W.; Namihira, Y.
2010-01-01
Penumbral imaging is a technique which uses the fact that spatial information can be recovered from the shadow or penumbra that an unknown source casts through a simple large circular aperture. The size of the penumbral image on the detector can be mathematically determined as its aperture size, object size, and magnification. Conventional reconstruction methods are very sensitive to noise. On the other hand, the heuristic reconstruction method is very tolerant of noise. However, the aperture size influences the accuracy and resolution of the reconstructed image. In this article, we propose the optimization of the aperture size for the neutron penumbral imaging.
Proximity search heuristics for wind farm optimal layout
DEFF Research Database (Denmark)
Fischetti, Martina; Monaci, Michele
2016-01-01
A heuristic framework for turbine layout optimization in a wind farm is proposed that combines ad-hoc heuristics and mixed-integer linear programming. In our framework, large-scale mixed-integer programming models are used to iteratively refine the current best solution according to the recently...
HEURISTIC OPTIMIZATION AND ALGORITHM TUNING APPLIED TO SORPTIVE BARRIER DESIGN
While heuristic optimization is applied in environmental applications, ad-hoc algorithm configuration is typical. We use a multi-layer sorptive barrier design problem as a benchmark for an algorithm-tuning procedure, as applied to three heuristics (genetic algorithms, simulated ...
Directory of Open Access Journals (Sweden)
Jafar Jallad
2018-05-01
Full Text Available In a radial distribution network integrated with distributed generation (DG, frequency and voltage instability could occur due to grid disconnection, which would result in an islanded network. This paper proposes an optimal load shedding scheme to balance the electricity demand and the generated power of DGs. The integration of the Firefly Algorithm and Particle Swarm Optimization (FAPSO is proposed for the application of the planned load shedding and under frequency load shedding (UFLS scheme. In planning mode, the hybrid optimization maximizes the amount of load remaining and improves the voltage profile of load buses within allowable limits. Moreover, the hybrid optimization can be used in UFLS scheme to identify the optimal combination of loads that need to be shed from a network in operation mode. In order to assess the capabilities of the hybrid optimization, the IEEE 33-bus radial distribution system and part of the Malaysian distribution network with different types of DGs were used. The response of the proposed optimization method in planning and operation were compared with other optimization techniques. The simulation results confirmed the effectiveness of the proposed hybrid optimization in planning mode and demonstrated that the proposed UFLS scheme is quick enough to restore the system frequency without overshooting in less execution time.
Theory of Randomized Search Heuristics in Combinatorial Optimization
DEFF Research Database (Denmark)
The rigorous mathematical analysis of randomized search heuristics(RSHs) with respect to their expected runtime is a growing research area where many results have been obtained in recent years. This class of heuristics includes well-known approaches such as Randomized Local Search (RLS), the Metr......The rigorous mathematical analysis of randomized search heuristics(RSHs) with respect to their expected runtime is a growing research area where many results have been obtained in recent years. This class of heuristics includes well-known approaches such as Randomized Local Search (RLS...... analysis of randomized algorithms to RSHs. Mostly, the expected runtime of RSHs on selected problems is analzyed. Thereby, we understand why and when RSHs are efficient optimizers and, conversely, when they cannot be efficient. The tutorial will give an overview on the analysis of RSHs for solving...
Directory of Open Access Journals (Sweden)
Vimal J. Savsani
2017-04-01
The static and dynamic responses to the TTO problems are challenging due to its search space, which is implicit, non-convex, non-linear, and often leading to divergence. Modified meta-heuristics are effective optimization methods to handle such problems in actual fact. In this paper, modified versions of Teaching–Learning-Based Optimization (TLBO, Heat Transfer Search (HTS, Water Wave Optimization (WWO, and Passing Vehicle Search (PVS are proposed by integrating the random mutation-based search technique with them. This paper compares the performance of four modified and four basic meta-heuristics to solve discrete TTO problems.
Using heuristic search for optimizing maintenance plans
International Nuclear Information System (INIS)
Mutanen, Teemu
2012-01-01
This work addresses the maintenance action selection process. Maintenance personnel need to evaluate maintenance actions and costs to keep the machines in working condition. Group of actions are evaluated together as maintenance plans. The maintenance plans as output provide information to the user about which actions to take if any and what future actions should be prepared for. The heuristic search method is implemented as part of general use toolbox for analysis of measurements from movable work machines. Impacts from machine's usage restrictions and maintenance activities are analysed. The results show that once put on a temporal perspective, the prioritized order of the actions is different and provide additional information to the user.
Optimizing Linear Functions with Randomized Search Heuristics - The Robustness of Mutation
DEFF Research Database (Denmark)
Witt, Carsten
2012-01-01
The analysis of randomized search heuristics on classes of functions is fundamental for the understanding of the underlying stochastic process and the development of suitable proof techniques. Recently, remarkable progress has been made in bounding the expected optimization time of the simple (1...
Nuclear fuel management optimization using adaptive evolutionary algorithms with heuristics
International Nuclear Information System (INIS)
Axmann, J.K.; Van de Velde, A.
1996-01-01
Adaptive Evolutionary Algorithms in combination with expert knowledge encoded in heuristics have proved to be a robust and powerful optimization method for the design of optimized PWR fuel loading pattern. Simple parallel algorithmic structures coupled with a low amount of communications between computer processor units in use makes it possible for workstation clusters to be employed efficiently. The extension of classic evolution strategies not only by new and alternative methods but also by the inclusion of heuristics with effects on the exchange probabilities of the fuel assemblies at specific core positions leads to the RELOPAT optimization code of the Technical University of Braunschweig. In combination with the new, neutron-physical 3D nodal core simulator PRISM developed by SIEMENS the PRIMO loading pattern optimization system has been designed. Highly promising results in the recalculation of known reload plans for German PWR's new lead to a commercially usable program. (author)
Heuristic versus statistical physics approach to optimization problems
International Nuclear Information System (INIS)
Jedrzejek, C.; Cieplinski, L.
1995-01-01
Optimization is a crucial ingredient of many calculation schemes in science and engineering. In this paper we assess several classes of methods: heuristic algorithms, methods directly relying on statistical physics such as the mean-field method and simulated annealing; and Hopfield-type neural networks and genetic algorithms partly related to statistical physics. We perform the analysis for three types of problems: (1) the Travelling Salesman Problem, (2) vector quantization, and (3) traffic control problem in multistage interconnection network. In general, heuristic algorithms perform better (except for genetic algorithms) and much faster but have to be specific for every problem. The key to improving the performance could be to include heuristic features into general purpose statistical physics methods. (author)
Multiobjective hyper heuristic scheme for system design and optimization
Rafique, Amer Farhan
2012-11-01
As system design is becoming more and more multifaceted, integrated, and complex, the traditional single objective optimization trends of optimal design are becoming less and less efficient and effective. Single objective optimization methods present a unique optimal solution whereas multiobjective methods present pareto front. The foremost intent is to predict a reasonable distributed pareto-optimal solution set independent of the problem instance through multiobjective scheme. Other objective of application of intended approach is to improve the worthiness of outputs of the complex engineering system design process at the conceptual design phase. The process is automated in order to provide the system designer with the leverage of the possibility of studying and analyzing a large multiple of possible solutions in a short time. This article presents Multiobjective Hyper Heuristic Optimization Scheme based on low level meta-heuristics developed for the application in engineering system design. Herein, we present a stochastic function to manage meta-heuristics (low-level) to augment surety of global optimum solution. Generic Algorithm, Simulated Annealing and Swarm Intelligence are used as low-level meta-heuristics in this study. Performance of the proposed scheme is investigated through a comprehensive empirical analysis yielding acceptable results. One of the primary motives for performing multiobjective optimization is that the current engineering systems require simultaneous optimization of conflicting and multiple. Random decision making makes the implementation of this scheme attractive and easy. Injecting feasible solutions significantly alters the search direction and also adds diversity of population resulting in accomplishment of pre-defined goals set in the proposed scheme.
a new meta-heuristic optimization algorithm
Indian Academy of Sciences (India)
N Archana
programming obtain optimal solution to the problem by rigorous methods supplemented by gradient information. Classical methods are good for solving problems with only ... ronment for their survival and apply the concepts in finding.
Energy Technology Data Exchange (ETDEWEB)
Cruz Castrejon, J. A; Islas Perez, E; Espinosa Reza, A; Garcia Mendoza, R [Instituto de Investigaciones Electricas, Cuernavaca, Morelos (Mexico)]. E-mails: adrian.cruz@iie.org.mx; eislas@iie.org.mx; aer@iie.org.mx; rgarcia@iie.org.mx
2013-03-15
In this paper we present a proposed solution to the problem of finding alternatives to reset faults in radial distribution networks power systems. This solution uses a deterministic method based on the definition of heuristics and whose main objectives are to improve execution time and solution quality. This search is based on the alternate repetition of two stages: a stage that attempts to reset the unconnected areas and other areas trying ballasting overloaded. [Spanish] En este articulo se presenta una propuesta de solucion al problema de busqueda de alternativas de restablecimiento para fallas en redes de distribucion radiales en sistemas electricos de potencia. Esta solucion utiliza un metodo deterministico basado en la definicion de heuristicas y cuyos objetivos principales son: mejorar el tiempo de ejecucion y calidad de la solucion. Esta busqueda se basa en la repeticion alternada de dos etapas: una etapa que intenta restablecer las areas desconectadas y otra que intenta deslastrar las areas sobrecargadas.
A Heuristics Approach for Classroom Scheduling Using Genetic Algorithm Technique
Ahmad, Izah R.; Sufahani, Suliadi; Ali, Maselan; Razali, Siti N. A. M.
2018-04-01
Reshuffling and arranging classroom based on the capacity of the audience, complete facilities, lecturing time and many more may lead to a complexity of classroom scheduling. While trying to enhance the productivity in classroom planning, this paper proposes a heuristic approach for timetabling optimization. A new algorithm was produced to take care of the timetabling problem in a university. The proposed of heuristics approach will prompt a superior utilization of the accessible classroom space for a given time table of courses at the university. Genetic Algorithm through Java programming languages were used in this study and aims at reducing the conflicts and optimizes the fitness. The algorithm considered the quantity of students in each class, class time, class size, time accessibility in each class and lecturer who in charge of the classes.
Heuristics for NP-hard optimization problems - simpler is better!?
Directory of Open Access Journals (Sweden)
Žerovnik Janez
2015-11-01
Full Text Available We provide several examples showing that local search, the most basic metaheuristics, may be a very competitive choice for solving computationally hard optimization problems. In addition, generation of starting solutions by greedy heuristics should be at least considered as one of very natural possibilities. In this critical survey, selected examples discussed include the traveling salesman, the resource-constrained project scheduling, the channel assignment, and computation of bounds for the Shannon capacity.
Heuristic and optimal policy computations in the human brain during sequential decision-making.
Korn, Christoph W; Bach, Dominik R
2018-01-23
Optimal decisions across extended time horizons require value calculations over multiple probabilistic future states. Humans may circumvent such complex computations by resorting to easy-to-compute heuristics that approximate optimal solutions. To probe the potential interplay between heuristic and optimal computations, we develop a novel sequential decision-making task, framed as virtual foraging in which participants have to avoid virtual starvation. Rewards depend only on final outcomes over five-trial blocks, necessitating planning over five sequential decisions and probabilistic outcomes. Here, we report model comparisons demonstrating that participants primarily rely on the best available heuristic but also use the normatively optimal policy. FMRI signals in medial prefrontal cortex (MPFC) relate to heuristic and optimal policies and associated choice uncertainties. Crucially, reaction times and dorsal MPFC activity scale with discrepancies between heuristic and optimal policies. Thus, sequential decision-making in humans may emerge from integration between heuristic and optimal policies, implemented by controllers in MPFC.
Combining heuristic and statistical techniques in landslide hazard assessments
Cepeda, Jose; Schwendtner, Barbara; Quan, Byron; Nadim, Farrokh; Diaz, Manuel; Molina, Giovanni
2014-05-01
As a contribution to the Global Assessment Report 2013 - GAR2013, coordinated by the United Nations International Strategy for Disaster Reduction - UNISDR, a drill-down exercise for landslide hazard assessment was carried out by entering the results of both heuristic and statistical techniques into a new but simple combination rule. The data available for this evaluation included landslide inventories, both historical and event-based. In addition to the application of a heuristic method used in the previous editions of GAR, the availability of inventories motivated the use of statistical methods. The heuristic technique is largely based on the Mora & Vahrson method, which estimates hazard as the product of susceptibility and triggering factors, where classes are weighted based on expert judgment and experience. Two statistical methods were also applied: the landslide index method, which estimates weights of the classes for the susceptibility and triggering factors based on the evidence provided by the density of landslides in each class of the factors; and the weights of evidence method, which extends the previous technique to include both positive and negative evidence of landslide occurrence in the estimation of weights for the classes. One key aspect during the hazard evaluation was the decision on the methodology to be chosen for the final assessment. Instead of opting for a single methodology, it was decided to combine the results of the three implemented techniques using a combination rule based on a normalization of the results of each method. The hazard evaluation was performed for both earthquake- and rainfall-induced landslides. The country chosen for the drill-down exercise was El Salvador. The results indicate that highest hazard levels are concentrated along the central volcanic chain and at the centre of the northern mountains.
Directory of Open Access Journals (Sweden)
Nadeem Javaid
2017-03-01
Full Text Available In recent years, demand side management (DSM techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA, the binary particle swarm optimization (BPSO algorithm, the bacterial foraging optimization algorithm (BFOA, the wind-driven optimization (WDO algorithm and our proposed hybrid genetic wind-driven (GWD algorithm are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs and off-peak hours (OPHs in a real-time pricing (RTP environment while maximizing user comfort (UC and minimizing both electricity cost and the peak to average ratio (PAR. Moreover, these algorithms are tested in two scenarios: (i scheduling the load of a single home and (ii scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics.
Heuristic Optimization for the Discrete Virtual Power Plant Dispatch Problem
DEFF Research Database (Denmark)
Petersen, Mette Kirschmeyer; Hansen, Lars Henrik; Bendtsen, Jan Dimon
2014-01-01
We consider a Virtual Power Plant, which is given the task of dispatching a fluctuating power supply to a portfolio of flexible consumers. The flexible consumers are modeled as discrete batch processes, and the associated optimization problem is denoted the Discrete Virtual Power Plant Dispatch...... Problem. First NP-completeness of the Discrete Virtual Power Plant Dispatch Problem is proved formally. We then proceed to develop tailored versions of the meta-heuristic algorithms Hill Climber and Greedy Randomized Adaptive Search Procedure (GRASP). The algorithms are tuned and tested on portfolios...... of varying sizes. We find that all the tailored algorithms perform satisfactorily in the sense that they are able to find sub-optimal, but usable, solutions to very large problems (on the order of 10 5 units) at computation times on the scale of just 10 seconds, which is far beyond the capabilities...
Directory of Open Access Journals (Sweden)
N. Okati
2017-12-01
Full Text Available Node cooperation can protect wireless networks from eavesdropping by using the physical characteristics of wireless channels rather than cryptographic methods. Allocating the proper amount of power to cooperative nodes is a challenging task. In this paper, we use three cooperative nodes, one as relay to increase throughput at the destination and two friendly jammers to degrade eavesdropper’s link. For this scenario, the secrecy rate function is a non-linear non-convex problem. So, in this case, exact optimization methods can only achieve suboptimal solution. In this paper, we applied different meta-heuristic optimization techniques, like Genetic Algorithm (GA, Partial Swarm Optimization (PSO, Bee Algorithm (BA, Tabu Search (TS, Simulated Annealing (SA and Teaching-Learning-Based Optimization (TLBO. They are compared with each other to obtain solution for power allocation in a wiretap wireless network. Although all these techniques find suboptimal solutions, but they appear superlative to exact optimization methods. Finally, we define a Figure of Merit (FOM as a rule of thumb to determine the best meta-heuristic algorithm. This FOM considers quality of solution, number of required iterations to converge, and CPU time.
Cost optimization model and its heuristic genetic algorithms
International Nuclear Information System (INIS)
Liu Wei; Wang Yongqing; Guo Jilin
1999-01-01
Interest and escalation are large quantity in proportion to the cost of nuclear power plant construction. In order to optimize the cost, the mathematics model of cost optimization for nuclear power plant construction was proposed, which takes the maximum net present value as the optimization goal. The model is based on the activity networks of the project and is an NP problem. A heuristic genetic algorithms (HGAs) for the model was introduced. In the algorithms, a solution is represented with a string of numbers each of which denotes the priority of each activity for assigned resources. The HGAs with this encoding method can overcome the difficulty which is harder to get feasible solutions when using the traditional GAs to solve the model. The critical path of the activity networks is figured out with the concept of predecessor matrix. An example was computed with the HGAP programmed in C language. The results indicate that the model is suitable for the objectiveness, the algorithms is effective to solve the model
Optimal Control of Complex Systems Based on Improved Dual Heuristic Dynamic Programming Algorithm
Directory of Open Access Journals (Sweden)
Hui Li
2017-01-01
Full Text Available When applied to solving the data modeling and optimal control problems of complex systems, the dual heuristic dynamic programming (DHP technique, which is based on the BP neural network algorithm (BP-DHP, has difficulty in prediction accuracy, slow convergence speed, poor stability, and so forth. In this paper, a dual DHP technique based on Extreme Learning Machine (ELM algorithm (ELM-DHP was proposed. Through constructing three kinds of network structures, the paper gives the detailed realization process of the DHP technique in the ELM. The controller designed upon the ELM-DHP algorithm controlled a molecular distillation system with complex features, such as multivariability, strong coupling, and nonlinearity. Finally, the effectiveness of the algorithm is verified by the simulation that compares DHP and HDP algorithms based on ELM and BP neural network. The algorithm can also be applied to solve the data modeling and optimal control problems of similar complex systems.
Directory of Open Access Journals (Sweden)
Ricardo Faia
2017-06-01
Full Text Available The deregulation of the electricity sector has culminated in the introduction of competitive markets. In addition, the emergence of new forms of electric energy production, namely the production of renewable energy, has brought additional changes in electricity market operation. Renewable energy has significant advantages, but at the cost of an intermittent character. The generation variability adds new challenges for negotiating players, as they have to deal with a new level of uncertainty. In order to assist players in their decisions, decision support tools enabling assisting players in their negotiations are crucial. Artificial intelligence techniques play an important role in this decision support, as they can provide valuable results in rather small execution times, namely regarding the problem of optimizing the electricity markets participation portfolio. This paper proposes a heuristic method that provides an initial solution that allows metaheuristic techniques to improve their results through a good initialization of the optimization process. Results show that by using the proposed heuristic, multiple metaheuristic optimization methods are able to improve their solutions in a faster execution time, thus providing a valuable contribution for players support in energy markets negotiations.
A new hybrid meta-heuristic algorithm for optimal design of large-scale dome structures
Kaveh, A.; Ilchi Ghazaan, M.
2018-02-01
In this article a hybrid algorithm based on a vibrating particles system (VPS) algorithm, multi-design variable configuration (Multi-DVC) cascade optimization, and an upper bound strategy (UBS) is presented for global optimization of large-scale dome truss structures. The new algorithm is called MDVC-UVPS in which the VPS algorithm acts as the main engine of the algorithm. The VPS algorithm is one of the most recent multi-agent meta-heuristic algorithms mimicking the mechanisms of damped free vibration of single degree of freedom systems. In order to handle a large number of variables, cascade sizing optimization utilizing a series of DVCs is used. Moreover, the UBS is utilized to reduce the computational time. Various dome truss examples are studied to demonstrate the effectiveness and robustness of the proposed method, as compared to some existing structural optimization techniques. The results indicate that the MDVC-UVPS technique is a powerful search and optimization method for optimizing structural engineering problems.
Optimization of pressurized water reactor shuffling by simulated annealing with heuristics
International Nuclear Information System (INIS)
Stevens, J.G.; Smith, K.S.; Rempe, K.R.; Downar, T.J.
1995-01-01
Simulated-annealing optimization of reactor core loading patterns is implemented with support for design heuristics during candidate pattern generation. The SIMAN optimization module uses the advanced nodal method of SIMULATE-3 and the full cross-section detail of CASMO-3 to evaluate accurately the neutronic performance of each candidate, resulting in high-quality patterns. The use of heuristics within simulated annealing is explored. Heuristics improve the consistency of optimization results for both fast- and slow-annealing runs with no penalty from the exclusion of unusual candidates. Thus, the heuristic application of designer judgment during automated pattern generation is shown to be effective. The capability of the SIMAN module to find and evaluate families of loading patterns that satisfy design constraints and have good objective performance within practical run times is demonstrated. The use of automated evaluations of successive cycles to explore multicycle effects of design decisions is discussed
Heuristic rules embedded genetic algorithm to solve VVER loading pattern optimization problem
International Nuclear Information System (INIS)
Fatih, Alim; Kostandi, Ivanov
2006-01-01
Full text: Loading Pattern (LP) optimization is one of the most important aspects of the operation of nuclear reactors. A genetic algorithm (GA) code GARCO (Genetic Algorithm Reactor Optimization Code) has been developed with embedded heuristic techniques to perform optimization calculations for in-core fuel management tasks. GARCO is a practical tool that includes a unique methodology applicable for all types of Pressurized Water Reactor (PWR) cores having different geometries with an unlimited number of FA types in the inventory. GARCO was developed by modifying the classical representation of the genotype. Both the genotype representation and the basic algorithm have been modified to incorporate the in-core fuel management heuristics rules so as to obtain the best results in a shorter time. GARCO has three modes. Mode 1 optimizes the locations of the fuel assemblies (FAs) in the nuclear reactor core, Mode 2 optimizes the placement of the burnable poisons (BPs) in a selected LP, and Mode 3 optimizes simultaneously both the LP and the BP placement in the core. This study describes the basic algorithm for Mode 1. The GARCO code is applied to the VVER-1000 reactor hexagonal geometry core in this study. The M oby-Dick i s used as reactor physics code to deplete FAs in the core. It was developed to analyze the VVER reactors by SKODA Inc. To use these rules for creating the initial population with GA operators, the worth definition application is developed. Each FA has a worth value for each location. This worth is between 0 and 1. If worth of any FA for a location is larger than 0.5, this FA in this location is a good choice. When creating the initial population of LPs, a subroutine provides a percent of individuals, which have genes with higher than the 0.5 worth. The percentage of the population to be created without using worth definition is defined in the GARCO input. And also age concept has been developed to accelerate the GA calculation process in reaching the
A novel heuristic method for optimization of straight blade vertical axis wind turbine
International Nuclear Information System (INIS)
Tahani, Mojtaba; Babayan, Narek; Mehrnia, Seyedmajid; Shadmehri, Mehran
2016-01-01
Highlights: • A novel heuristic method has been proposed for optimization of VAWTs. • The proposed method is the combination of DMST model with heuristic algorithms. • A continuous/discrete optimization problem has been solved. • A novel continuous optimization algorithm has been developed. • The CFD simulation of the optimized geometry has been carried out. - Abstract: In this research study it is aimed to propose a novel heuristic method for optimizing the VAWT design. The method is the combination of continuous/discrete optimization algorithms with double multiple stream tube (DMST) theory. For this purpose a DMST code has been developed and is validated using available experimental data in literature. A novel continuous optimization algorithm is proposed which can be considered as the combination of three heuristic optimization algorithms namely elephant herding optimization, flower pollination algorithm and grey wolf optimizer. The continuous algorithm is combined with popular discrete ant colony optimization algorithm (ACO). The proposed method can be utilized for several engineering problems which are dealing with continuous and discrete variables. In this research study, chord and diameter of the turbine are selected as continuous decision variables and airfoil types and number of blades are selected as discrete decision variables. The average power coefficient between tip speed rations from 1.5 to 9.5 is considered as the objective function. The optimization results indicated that the optimized geometry can produce a maximum power coefficient, 44% higher than the maximum power coefficient of the original turbine. Also a CFD simulation of the optimized geometry is carried out. The CFD results indicated that the average vorticity magnitude around the optimized blade is larger than the original blade and this results greater momentum and power coefficient.
An Empirical Comparison of Seven Iterative and Evolutionary Function Optimization Heuristics
Baluja, Shumeet
1995-01-01
This report is a repository of the results obtained from a large scale empirical comparison of seven iterative and evolution-based optimization heuristics. Twenty-seven static optimization problems, spanning six sets of problem classes which are commonly explored in genetic algorithm literature, are examined. The problem sets include job-shop scheduling, traveling salesman, knapsack, binpacking, neural network weight optimization, and standard numerical optimization. The search spaces in these problems range from 2368 to 22040. The results indicate that using genetic algorithms for the optimization of static functions does not yield a benefit, in terms of the final answer obtained, over simpler optimization heuristics. Descriptions of the algorithms tested and the encodings of the problems are described in detail for reproducibility.
A nuclear heuristic for application to metaheuristics in-core fuel management optimization
Energy Technology Data Exchange (ETDEWEB)
Meneses, Anderson Alvarenga de Moura, E-mail: ameneses@lmp.ufrj.b [COPPE/Federal University of Rio de Janeiro, RJ (Brazil). Nuclear Engineering Program; Dalle Molle Institute for Artificial Intelligence (IDSIA), Manno-Lugano, TI (Switzerland); Gambardella, Luca Maria, E-mail: luca@idsia.c [Dalle Molle Institute for Artificial Intelligence (IDSIA), Manno-Lugano, TI (Switzerland); Schirru, Roberto, E-mail: schirru@lmp.ufrj.b [COPPE/Federal University of Rio de Janeiro, RJ (Brazil). Nuclear Engineering Program
2009-07-01
The In-Core Fuel Management Optimization (ICFMO) is a well-known problem of nuclear engineering whose features are complexity, high number of feasible solutions, and a complex evaluation process with high computational cost, thus it is prohibitive to have a great number of evaluations during an optimization process. Heuristics are criteria or principles for deciding which among several alternative courses of action are more effective with respect to some goal. In this paper, we propose a new approach for the use of relational heuristics for the search in the ICFMO. The Heuristic is based on the reactivity of the fuel assemblies and their position into the reactor core. It was applied to random search, resulting in less computational effort concerning the number of evaluations of loading patterns during the search. The experiments demonstrate that it is possible to achieve results comparable to results in the literature, for future application to metaheuristics in the ICFMO. (author)
A nuclear heuristic for application to metaheuristics in-core fuel management optimization
International Nuclear Information System (INIS)
Meneses, Anderson Alvarenga de Moura; Gambardella, Luca Maria; Schirru, Roberto
2009-01-01
The In-Core Fuel Management Optimization (ICFMO) is a well-known problem of nuclear engineering whose features are complexity, high number of feasible solutions, and a complex evaluation process with high computational cost, thus it is prohibitive to have a great number of evaluations during an optimization process. Heuristics are criteria or principles for deciding which among several alternative courses of action are more effective with respect to some goal. In this paper, we propose a new approach for the use of relational heuristics for the search in the ICFMO. The Heuristic is based on the reactivity of the fuel assemblies and their position into the reactor core. It was applied to random search, resulting in less computational effort concerning the number of evaluations of loading patterns during the search. The experiments demonstrate that it is possible to achieve results comparable to results in the literature, for future application to metaheuristics in the ICFMO. (author)
Directory of Open Access Journals (Sweden)
Chao-Chih Lin
2017-10-01
Full Text Available A new transient-based hybrid heuristic approach is developed to optimize a transient generation process and to detect leaks in pipe networks. The approach couples the ordinal optimization approach (OOA and the symbiotic organism search (SOS to solve the optimization problem by means of iterations. A pipe network analysis model (PNSOS is first used to determine steady-state head distribution and pipe flow rates. The best transient generation point and its relevant valve operation parameters are optimized by maximizing the objective function of transient energy. The transient event is created at the chosen point, and the method of characteristics (MOC is used to analyze the transient flow. The OOA is applied to sift through the candidate pipes and the initial organisms with leak information. The SOS is employed to determine the leaks by minimizing the sum of differences between simulated and computed head at the observation points. Two synthetic leaking scenarios, a simple pipe network and a water distribution network (WDN, are chosen to test the performance of leak detection ordinal symbiotic organism search (LDOSOS. Leak information can be accurately identified by the proposed approach for both of the scenarios. The presented technique makes a remarkable contribution to the success of leak detection in the pipe networks.
A derived heuristics based multi-objective optimization procedure for micro-grid scheduling
Li, Xin; Deb, Kalyanmoy; Fang, Yanjun
2017-06-01
With the availability of different types of power generators to be used in an electric micro-grid system, their operation scheduling as the load demand changes with time becomes an important task. Besides satisfying load balance constraints and the generator's rated power, several other practicalities, such as limited availability of grid power and restricted ramping of power output from generators, must all be considered during the operation scheduling process, which makes it difficult to decide whether the optimization results are accurate and satisfactory. In solving such complex practical problems, heuristics-based customized optimization algorithms are suggested. However, due to nonlinear and complex interactions of variables, it is difficult to come up with heuristics in such problems off-hand. In this article, a two-step strategy is proposed in which the first task deciphers important heuristics about the problem and the second task utilizes the derived heuristics to solve the original problem in a computationally fast manner. Specifically, the specific operation scheduling is considered from a two-objective (cost and emission) point of view. The first task develops basic and advanced level knowledge bases offline from a series of prior demand-wise optimization runs and then the second task utilizes them to modify optimized solutions in an application scenario. Results on island and grid connected modes and several pragmatic formulations of the micro-grid operation scheduling problem clearly indicate the merit of the proposed two-step procedure.
Indrayana, I. N. E.; P, N. M. Wirasyanti D.; Sudiartha, I. KG
2018-01-01
Mobile application allow many users to access data from the application without being limited to space, space and time. Over time the data population of this application will increase. Data access time will cause problems if the data record has reached tens of thousands to millions of records.The objective of this research is to maintain the performance of data execution for large data records. One effort to maintain data access time performance is to apply query optimization method. The optimization used in this research is query heuristic optimization method. The built application is a mobile-based financial application using MySQL database with stored procedure therein. This application is used by more than one business entity in one database, thus enabling rapid data growth. In this stored procedure there is an optimized query using heuristic method. Query optimization is performed on a “Select” query that involves more than one table with multiple clausa. Evaluation is done by calculating the average access time using optimized and unoptimized queries. Access time calculation is also performed on the increase of population data in the database. The evaluation results shown the time of data execution with query heuristic optimization relatively faster than data execution time without using query optimization.
Othman, Muhammad Murtadha; Abd Rahman, Nurulazmi; Musirin, Ismail; Fotuhi-Firuzabad, Mahmud; Rajabi-Ghahnavieh, Abbas
2015-01-01
This paper introduces a novel multiobjective approach for capacity benefit margin (CBM) assessment taking into account tie-line reliability of interconnected systems. CBM is the imperative information utilized as a reference by the load-serving entities (LSE) to estimate a certain margin of transfer capability so that a reliable access to generation through interconnected system could be attained. A new Pareto-based evolutionary programming (EP) technique is used to perform a simultaneous determination of CBM for all areas of the interconnected system. The selection of CBM at the Pareto optimal front is proposed to be performed by referring to a heuristic ranking index that takes into account system loss of load expectation (LOLE) in various conditions. Eventually, the power transfer based available transfer capability (ATC) is determined by considering the firm and nonfirm transfers of CBM. A comprehensive set of numerical studies are conducted on the modified IEEE-RTS79 and the performance of the proposed method is numerically investigated in detail. The main advantage of the proposed technique is in terms of flexibility offered to an independent system operator in selecting an appropriate solution of CBM simultaneously for all areas.
Directory of Open Access Journals (Sweden)
Muhammad Murtadha Othman
2015-01-01
Full Text Available This paper introduces a novel multiobjective approach for capacity benefit margin (CBM assessment taking into account tie-line reliability of interconnected systems. CBM is the imperative information utilized as a reference by the load-serving entities (LSE to estimate a certain margin of transfer capability so that a reliable access to generation through interconnected system could be attained. A new Pareto-based evolutionary programming (EP technique is used to perform a simultaneous determination of CBM for all areas of the interconnected system. The selection of CBM at the Pareto optimal front is proposed to be performed by referring to a heuristic ranking index that takes into account system loss of load expectation (LOLE in various conditions. Eventually, the power transfer based available transfer capability (ATC is determined by considering the firm and nonfirm transfers of CBM. A comprehensive set of numerical studies are conducted on the modified IEEE-RTS79 and the performance of the proposed method is numerically investigated in detail. The main advantage of the proposed technique is in terms of flexibility offered to an independent system operator in selecting an appropriate solution of CBM simultaneously for all areas.
Triangular Geometrized Sampling Heuristics for Fast Optimal Motion Planning
Directory of Open Access Journals (Sweden)
Ahmed Hussain Qureshi
2015-02-01
Full Text Available Rapidly-exploring Random Tree (RRT-based algorithms have become increasingly popular due to their lower computational complexity as compared with other path planning algorithms. The recently presented RRT* motion planning algorithm improves upon the original RRT algorithm by providing optimal path solutions. While RRT determines an initial collision-free path fairly quickly, RRT* guarantees almost certain convergence to an optimal, obstacle-free path from the start to the goal points for any given geometrical environment. However, the main limitations of RRT* include its slow processing rate and high memory consumption, due to the large number of iterations required for calculating the optimal path. In order to overcome these limitations, we present another improvement, i.e, the Triangular Geometerized-RRT* (TG-RRT* algorithm, which utilizes triangular geometrical methods to improve the performance of the RRT* algorithm in terms of the processing time and a decreased number of iterations required for an optimal path solution. Simulations comparing the performance results of the improved TG-RRT* with RRT* are presented to demonstrate the overall improvement in performance and optimal path detection.
Automated generation of constructive ordering heuristics for educational timetabling
Pillay, Nelishia; Özcan, Ender
2017-01-01
Construction heuristics play an important role in solving combinatorial optimization problems. These heuristics are usually used to create an initial solution to the problem which is improved using optimization techniques such as metaheuristics. For examination timetabling and university course timetabling problems essentially graph colouring heuristics have been used for this purpose. The process of deriving heuristics manually for educational timetabling is a time consuming task. Furthermor...
Directory of Open Access Journals (Sweden)
Tashkova Katerina
2011-10-01
Full Text Available Abstract Background We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. Results We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA, particle-swarm optimization (PSO, and differential evolution (DE, as well as a local-search derivative-based algorithm 717 (A717 to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Conclusions Overall, the global meta-heuristic methods (DASA, PSO, and DE clearly and significantly outperform the local derivative-based method (A717. Among the three meta-heuristics, differential evolution (DE performs best in terms of the objective function, i.e., reconstructing the output, and in terms of
Tashkova, Katerina; Korošec, Peter; Silc, Jurij; Todorovski, Ljupčo; Džeroski, Sašo
2011-10-11
We address the task of parameter estimation in models of the dynamics of biological systems based on ordinary differential equations (ODEs) from measured data, where the models are typically non-linear and have many parameters, the measurements are imperfect due to noise, and the studied system can often be only partially observed. A representative task is to estimate the parameters in a model of the dynamics of endocytosis, i.e., endosome maturation, reflected in a cut-out switch transition between the Rab5 and Rab7 domain protein concentrations, from experimental measurements of these concentrations. The general parameter estimation task and the specific instance considered here are challenging optimization problems, calling for the use of advanced meta-heuristic optimization methods, such as evolutionary or swarm-based methods. We apply three global-search meta-heuristic algorithms for numerical optimization, i.e., differential ant-stigmergy algorithm (DASA), particle-swarm optimization (PSO), and differential evolution (DE), as well as a local-search derivative-based algorithm 717 (A717) to the task of estimating parameters in ODEs. We evaluate their performance on the considered representative task along a number of metrics, including the quality of reconstructing the system output and the complete dynamics, as well as the speed of convergence, both on real-experimental data and on artificial pseudo-experimental data with varying amounts of noise. We compare the four optimization methods under a range of observation scenarios, where data of different completeness and accuracy of interpretation are given as input. Overall, the global meta-heuristic methods (DASA, PSO, and DE) clearly and significantly outperform the local derivative-based method (A717). Among the three meta-heuristics, differential evolution (DE) performs best in terms of the objective function, i.e., reconstructing the output, and in terms of convergence. These results hold for both real and
Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling
Directory of Open Access Journals (Sweden)
Helio Yochihiro Fuchigami
2018-04-01
Full Text Available The number of just-in-time jobs maximization in a permutation flow shop scheduling problem is considered. A mixed integer linear programming model to represent the problem as well as solution approaches based on enumeration and constructive heuristics were proposed and computationally implemented. Instances with up to 10 jobs and five machines are solved by the mathematical model in an acceptable running time (3.3 min on average while the enumeration method consumes, on average, 1.5 s. The 10 constructive heuristics proposed show they are practical especially for large-scale instances (up to 100 jobs and 20 machines, with very good-quality results and efficient running times. The best two heuristics obtain near-optimal solutions, with only 0.6% and 0.8% average relative deviations. They prove to be better than adaptations of the NEH heuristic (well-known for providing very good solutions for makespan minimization in flow shop for the considered problem.
Application of Fuzzy Sets for the Improvement of Routing Optimization Heuristic Algorithms
Directory of Open Access Journals (Sweden)
Mattas Konstantinos
2016-12-01
Full Text Available The determination of the optimal circular path has become widely known for its difficulty in producing a solution and for the numerous applications in the scope of organization and management of passenger and freight transport. It is a mathematical combinatorial optimization problem for which several deterministic and heuristic models have been developed in recent years, applicable to route organization issues, passenger and freight transport, storage and distribution of goods, waste collection, supply and control of terminals, as well as human resource management. Scope of the present paper is the development, with the use of fuzzy sets, of a practical, comprehensible and speedy heuristic algorithm for the improvement of the ability of the classical deterministic algorithms to identify optimum, symmetrical or non-symmetrical, circular route. The proposed fuzzy heuristic algorithm is compared to the corresponding deterministic ones, with regard to the deviation of the proposed solution from the best known solution and the complexity of the calculations needed to obtain this solution. It is shown that the use of fuzzy sets reduced up to 35% the deviation of the solution identified by the classical deterministic algorithms from the best known solution.
Sensitivity study on heuristic rules applied to the neutronic optimization of cells for BWR
International Nuclear Information System (INIS)
Gonzalez C, J.; Martin del Campo M, C.; Francois L, J.L.
2004-01-01
The objective of this work is to verify the validity of the heuristic rules that have been applied in the processes of radial optimization of fuel cells. It was examined the rule with respect to the accommodation of fuel in the corners of the cell and it became special attention on the influence of the position and concentration of those pellets with gadolinium in the reactivity of the cell and the safety parameters. The evaluation behaved on designed cells violating the heuristic rules. For both cases the cells were analyzed between infinite using the HELIOS code. Additionally, for the second case, it was behaved a stage more exhaustive where it was used one of the studied cells that it completed those safety parameters and of reactivity to generate the design of an assemble that was used to calculate with CM-PRESTO the behavior of the nucleus during three operation cycles. (Author)
Françoise Benz
2004-01-01
ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on natural annealing processes or Evolutionary Computation, based on biological evolution processes. Geneti...
Françoise Benz
2004-01-01
ENSEIGNEMENT ACADEMIQUE ACADEMIC TRAINING Françoise Benz 73127 academic.training@cern.ch ACADEMIC TRAINING LECTURE REGULAR PROGRAMME 1, 2, 3 and 4 June From 11:00 hrs to 12:00 hrs - Main Auditorium bldg. 500 Evolutionary Heuristic Optimization: Genetic Algorithms and Estimation of Distribution Algorithms V. Robles Forcada and M. Perez Hernandez / Univ. de Madrid, Spain In the real world, there exist a huge number of problems that require getting an optimum or near-to-optimum solution. Optimization can be used to solve a lot of different problems such as network design, sets and partitions, storage and retrieval or scheduling. On the other hand, in nature, there exist many processes that seek a stable state. These processes can be seen as natural optimization processes. Over the last 30 years several attempts have been made to develop optimization algorithms, which simulate these natural optimization processes. These attempts have resulted in methods such as Simulated Annealing, based on nat...
Performance of Optimization Heuristics for the Operational Planning of Multi-energy Storage Systems
Haas, J.; Schradi, J.; Nowak, W.
2016-12-01
In the transition to low-carbon energy sources, energy storage systems (ESS) will play an increasingly important role. Particularly in the context of solar power challenges (variability, uncertainty), ESS can provide valuable services: energy shifting, ramping, robustness against forecast errors, frequency support, etc. However, these qualities are rarely modelled in the operational planning of power systems because of the involved computational burden, especially when multiple ESS technologies are involved. This work assesses two optimization heuristics for speeding up the optimal operation problem. It compares their accuracy (in terms of costs) and speed against a reference solution. The first heuristic (H1) is based on a merit order. Here, the ESS are sorted from lower to higher operational costs (including cycling costs). For each time step, the cheapest available ESS is used first, followed by the second one and so on, until matching the net load (demand minus available renewable generation). The second heuristic (H2) uses the Fourier transform to detect the main frequencies that compose the net load. A specific ESS is assigned to each frequency range, aiming to smoothen the net load. Finally, the reference solution is obtained with a mixed integer linear program (MILP). H1, H2 and MILP are subject to technical constraints (energy/power balance, ramping rates, on/off states...). Costs due to operation, replacement (cycling) and unserved energy are considered. Four typical days of a system with a high share of solar energy were used in several test cases, varying the resolution from one second to fifteen minutes. H1 and H2 achieve accuracies of about 90% and 95% in average, and speed-up times of two to three and one to two orders of magnitude, respectively. The use of the heuristics looks promising in the context of planning the expansion of power systems, especially when their loss of accuracy is outweighed by solar or wind forecast errors.
Near-Optimal Tracking Control of Mobile Robots Via Receding-Horizon Dual Heuristic Programming.
Lian, Chuanqiang; Xu, Xin; Chen, Hong; He, Haibo
2016-11-01
Trajectory tracking control of wheeled mobile robots (WMRs) has been an important research topic in control theory and robotics. Although various tracking control methods with stability have been developed for WMRs, it is still difficult to design optimal or near-optimal tracking controller under uncertainties and disturbances. In this paper, a near-optimal tracking control method is presented for WMRs based on receding-horizon dual heuristic programming (RHDHP). In the proposed method, a backstepping kinematic controller is designed to generate desired velocity profiles and the receding horizon strategy is used to decompose the infinite-horizon optimal control problem into a series of finite-horizon optimal control problems. In each horizon, a closed-loop tracking control policy is successively updated using a class of approximate dynamic programming algorithms called finite-horizon dual heuristic programming (DHP). The convergence property of the proposed method is analyzed and it is shown that the tracking control system based on RHDHP is asymptotically stable by using the Lyapunov approach. Simulation results on three tracking control problems demonstrate that the proposed method has improved control performance when compared with conventional model predictive control (MPC) and DHP. It is also illustrated that the proposed method has lower computational burden than conventional MPC, which is very beneficial for real-time tracking control.
Tang, Jiafu; Liu, Yang; Fung, Richard; Luo, Xinggang
2008-12-01
Manufacturers have a legal accountability to deal with industrial waste generated from their production processes in order to avoid pollution. Along with advances in waste recovery techniques, manufacturers may adopt various recycling strategies in dealing with industrial waste. With reuse strategies and technologies, byproducts or wastes will be returned to production processes in the iron and steel industry, and some waste can be recycled back to base material for reuse in other industries. This article focuses on a recovery strategies optimization problem for a typical class of industrial waste recycling process in order to maximize profit. There are multiple strategies for waste recycling available to generate multiple byproducts; these byproducts are then further transformed into several types of chemical products via different production patterns. A mixed integer programming model is developed to determine which recycling strategy and which production pattern should be selected with what quantity of chemical products corresponding to this strategy and pattern in order to yield maximum marginal profits. The sales profits of chemical products and the set-up costs of these strategies, patterns and operation costs of production are considered. A simulated annealing (SA) based heuristic algorithm is developed to solve the problem. Finally, an experiment is designed to verify the effectiveness and feasibility of the proposed method. By comparing a single strategy to multiple strategies in an example, it is shown that the total sales profit of chemical products can be increased by around 25% through the simultaneous use of multiple strategies. This illustrates the superiority of combinatorial multiple strategies. Furthermore, the effects of the model parameters on profit are discussed to help manufacturers organize their waste recycling network.
Comparison of Heuristic Methods Applied for Optimal Operation of Water Resources
Directory of Open Access Journals (Sweden)
Alireza Borhani Dariane
2009-01-01
Full Text Available Water resources optimization problems are usually complex and hard to solve using the ordinary optimization methods, or they are at least not economically efficient. A great number of studies have been conducted in quest of suitable methods capable of handling such problems. In recent years, some new heuristic methods such as genetic and ant algorithms have been introduced in systems engineering. Preliminary applications of these methods in water resources problems have shown that some of them are powerful tools, capable of solving complex problems. In this paper, the application of such heuristic methods as Genetic Algorithm (GA and Ant Colony Optimization (ACO have been studied for optimizing reservoir operation. The Dez Dam reservoir inIranwas chosen for a case study. The methods were applied and compared using short-term (one year and long-term models. Comparison of the results showed that GA outperforms both DP and ACO in finding true global optimum solutions and operating rules.
Heuristic Optimization Approach to Selecting a Transport Connection in City Public Transport
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Kul’ka Jozef
2017-02-01
Full Text Available The article presents a heuristic optimization approach to select a suitable transport connection in the framework of a city public transport. This methodology was applied on a part of the public transport in Košice, because it is the second largest city in the Slovak Republic and its network of the public transport creates a complex transport system, which consists of three different transport modes, namely from the bus transport, tram transport and trolley-bus transport. This solution focused on examining the individual transport services and their interconnection in relevant interchange points.
Derived heuristics-based consistent optimization of material flow in a gold processing plant
Myburgh, Christie; Deb, Kalyanmoy
2018-01-01
Material flow in a chemical processing plant often follows complicated control laws and involves plant capacity constraints. Importantly, the process involves discrete scenarios which when modelled in a programming format involves if-then-else statements. Therefore, a formulation of an optimization problem of such processes becomes complicated with nonlinear and non-differentiable objective and constraint functions. In handling such problems using classical point-based approaches, users often have to resort to modifications and indirect ways of representing the problem to suit the restrictions associated with classical methods. In a particular gold processing plant optimization problem, these facts are demonstrated by showing results from MATLAB®'s well-known fmincon routine. Thereafter, a customized evolutionary optimization procedure which is capable of handling all complexities offered by the problem is developed. Although the evolutionary approach produced results with comparatively less variance over multiple runs, the performance has been enhanced by introducing derived heuristics associated with the problem. In this article, the development and usage of derived heuristics in a practical problem are presented and their importance in a quick convergence of the overall algorithm is demonstrated.
QuickVina: accelerating AutoDock Vina using gradient-based heuristics for global optimization.
Handoko, Stephanus Daniel; Ouyang, Xuchang; Su, Chinh Tran To; Kwoh, Chee Keong; Ong, Yew Soon
2012-01-01
Predicting binding between macromolecule and small molecule is a crucial phase in the field of rational drug design. AutoDock Vina, one of the most widely used docking software released in 2009, uses an empirical scoring function to evaluate the binding affinity between the molecules and employs the iterated local search global optimizer for global optimization, achieving a significantly improved speed and better accuracy of the binding mode prediction compared its predecessor, AutoDock 4. In this paper, we propose further improvement in the local search algorithm of Vina by heuristically preventing some intermediate points from undergoing local search. Our improved version of Vina-dubbed QVina-achieved a maximum acceleration of about 25 times with the average speed-up of 8.34 times compared to the original Vina when tested on a set of 231 protein-ligand complexes while maintaining the optimal scores mostly identical. Using our heuristics, larger number of different ligands can be quickly screened against a given receptor within the same time frame.
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Muhammad Farhan Ausaf
2015-12-01
Full Text Available Process planning and scheduling are two important components of a manufacturing setup. It is important to integrate them to achieve better global optimality and improved system performance. To find optimal solutions for integrated process planning and scheduling (IPPS problem, numerous algorithm-based approaches exist. Most of these approaches try to use existing meta-heuristic algorithms for solving the IPPS problem. Although these approaches have been shown to be effective in optimizing the IPPS problem, there is still room for improvement in terms of quality of solution and algorithm efficiency, especially for more complicated problems. Dispatching rules have been successfully utilized for solving complicated scheduling problems, but haven’t been considered extensively for the IPPS problem. This approach incorporates dispatching rules with the concept of prioritizing jobs, in an algorithm called priority-based heuristic algorithm (PBHA. PBHA tries to establish job and machine priority for selecting operations. Priority assignment and a set of dispatching rules are simultaneously used to generate both the process plans and schedules for all jobs and machines. The algorithm was tested for a series of benchmark problems. The proposed algorithm was able to achieve superior results for most complex problems presented in recent literature while utilizing lesser computational resources.
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Vatutin Eduard
2017-12-01
Full Text Available The article deals with the problem of analysis of effectiveness of the heuristic methods with limited depth-first search techniques of decision obtaining in the test problem of getting the shortest path in graph. The article briefly describes the group of methods based on the limit of branches number of the combinatorial search tree and limit of analyzed subtree depth used to solve the problem. The methodology of comparing experimental data for the estimation of the quality of solutions based on the performing of computational experiments with samples of graphs with pseudo-random structure and selected vertices and arcs number using the BOINC platform is considered. It also shows description of obtained experimental results which allow to identify the areas of the preferable usage of selected subset of heuristic methods depending on the size of the problem and power of constraints. It is shown that the considered pair of methods is ineffective in the selected problem and significantly inferior to the quality of solutions that are provided by ant colony optimization method and its modification with combinatorial returns.
Vatutin, Eduard
2017-12-01
The article deals with the problem of analysis of effectiveness of the heuristic methods with limited depth-first search techniques of decision obtaining in the test problem of getting the shortest path in graph. The article briefly describes the group of methods based on the limit of branches number of the combinatorial search tree and limit of analyzed subtree depth used to solve the problem. The methodology of comparing experimental data for the estimation of the quality of solutions based on the performing of computational experiments with samples of graphs with pseudo-random structure and selected vertices and arcs number using the BOINC platform is considered. It also shows description of obtained experimental results which allow to identify the areas of the preferable usage of selected subset of heuristic methods depending on the size of the problem and power of constraints. It is shown that the considered pair of methods is ineffective in the selected problem and significantly inferior to the quality of solutions that are provided by ant colony optimization method and its modification with combinatorial returns.
Mechanical Design Optimization Using Advanced Optimization Techniques
Rao, R Venkata
2012-01-01
Mechanical design includes an optimization process in which designers always consider objectives such as strength, deflection, weight, wear, corrosion, etc. depending on the requirements. However, design optimization for a complete mechanical assembly leads to a complicated objective function with a large number of design variables. It is a good practice to apply optimization techniques for individual components or intermediate assemblies than a complete assembly. Analytical or numerical methods for calculating the extreme values of a function may perform well in many practical cases, but may fail in more complex design situations. In real design problems, the number of design parameters can be very large and their influence on the value to be optimized (the goal function) can be very complicated, having nonlinear character. In these complex cases, advanced optimization algorithms offer solutions to the problems, because they find a solution near to the global optimum within reasonable time and computational ...
Schiller, Stefan
2013-01-01
The purpose of this paper is to further the development of initial accounting for internally generated intangible assets, relevant to both academics and practitioners, examining what happens when accountants are given principles-based discretion. This paper draws on existing insights into heuristics or experience-based techniques for making accounting judgments. Knowledge about judgment under uncertainty, and the general framework offered by the heuristics and biases program in particular, fo...
Structure optimization by heuristic algorithm in a coarse-grained off-lattice model
International Nuclear Information System (INIS)
Jing-Fa, Liu
2009-01-01
A heuristic algorithm is presented for a three-dimensional off-lattice AB model consisting of hydrophobic (A) and hydrophilic (B) residues in Fibonacci sequences. By incorporating extra energy contributions into the original potential function, we convert the constrained optimization problem of AB model into an unconstrained optimization problem which can be solved by the gradient method. After the gradient minimization leads to the basins of the local energy minima, the heuristic off-trap strategy and subsequent neighborhood search mechanism are then proposed to get out of local minima and search for the lower-energy configurations. Furthermore, in order to improve the efficiency of the proposed algorithm, we apply the improved version called the new PERM with importance sampling (nPERMis) of the chain-growth algorithm, pruned-enriched-Rosenbluth method (PERM), to face-centered-cubic (FCC)-lattice to produce the initial configurations. The numerical results show that the proposed methods are very promising for finding the ground states of proteins. In several cases, we found the ground state energies are lower than the best values reported in the present literature
Parameter estimation in stochastic mammogram model by heuristic optimization techniques.
Selvan, S.E.; Xavier, C.C.; Karssemeijer, N.; Sequeira, J.; Cherian, R.A.; Dhala, B.Y.
2006-01-01
The appearance of disproportionately large amounts of high-density breast parenchyma in mammograms has been found to be a strong indicator of the risk of developing breast cancer. Hence, the breast density model is popular for risk estimation or for monitoring breast density change in prevention or
Energy Technology Data Exchange (ETDEWEB)
Fesanghary, M. [Department of Mechanical Engineering, Louisiana State University, 2508 Patrick Taylor Hall, Baton Rouge, LA 70808 (United States); Ardehali, M.M. [Energy Research Center, Department of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), 424-Hafez Avenue, 15875-4413 Tehran (Iran)
2009-06-15
The increasing costs of fuel and operation of thermal power generating units warrant development of optimization methodologies for economic dispatch (ED) problems. Optimization methodologies that are based on meta-heuristic procedures could assist power generation policy analysts to achieve the goal of minimizing the generation costs. In this context, the objective of this study is to present a novel approach based on harmony search (HS) algorithm for solving ED problems, aiming to provide a practical alternative for conventional methods. To demonstrate the efficiency and applicability of the proposed method and for the purposes of comparison, various types of ED problems are examined. The results of this study show that the new proposed approach is able to find more economical loads than those determined by other methods. (author)
On the complexity of decision trees, the quasi-optimizer, and the power of heuristic rules
Findler, N.V.; Leeuwen, J. van
The power of certain heuristic rules is indicated by the relative reduction in the complexity of computations carried out, due to the use of the heuristics. A concept of complexity is needed to evaluate the performance of programs as they operate with a varying set of heuristic rules in use. We
Optimized LTE cell planning for multiple user density subareas using meta-heuristic algorithms
Ghazzai, Hakim
2014-09-01
Base station deployment in cellular networks is one of the most fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation 4G-LTE cellular networks using meta heuristic algorithms. In this approach, we aim to satisfy both coverage and cell capacity constraints simultaneously by formulating a practical optimization problem. We start by performing a typical coverage and capacity dimensioning to identify the initial required number of base stations. Afterwards, we implement a Particle Swarm Optimization algorithm or a recently-proposed Grey Wolf Optimizer to find the optimal base station locations that satisfy both problem constraints in the area of interest which can be divided into several subareas with different user densities. Subsequently, an iterative approach is executed to eliminate eventual redundant base stations. We have also performed Monte Carlo simulations to study the performance of the proposed scheme and computed the average number of users in outage. Results show that our proposed approach respects in all cases the desired network quality of services even for large-scale dimension problems.
Directory of Open Access Journals (Sweden)
DOGAN, A.
2018-02-01
Full Text Available Image thresholding is the most crucial step in microscopic image analysis to distinguish bacilli objects causing of tuberculosis disease. Therefore, several bi-level thresholding algorithms are widely used to increase the bacilli segmentation accuracy. However, bi-level microscopic image thresholding problem has not been solved using optimization algorithms. This paper introduces a novel approach for the segmentation problem using heuristic algorithms and presents visual and quantitative comparisons of heuristic and state-of-art thresholding algorithms. In this study, well-known heuristic algorithms such as Firefly Algorithm, Particle Swarm Optimization, Cuckoo Search, Flower Pollination are used to solve bi-level microscopic image thresholding problem, and the results are compared with the state-of-art thresholding algorithms such as K-Means, Fuzzy C-Means, Fast Marching. Kapur's entropy is chosen as the entropy measure to be maximized. Experiments are performed to make comparisons in terms of evaluation metrics and execution time. The quantitative results are calculated based on ground truth segmentation. According to the visual results, heuristic algorithms have better performance and the quantitative results are in accord with the visual results. Furthermore, experimental time comparisons show the superiority and effectiveness of the heuristic algorithms over traditional thresholding algorithms.
Directory of Open Access Journals (Sweden)
Jeng-Fung Chen
2018-02-01
Full Text Available Electricity load forecasting plays a paramount role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate planning and prediction of electricity load are therefore vital. In this study, a novel approach for forecasting monthly electricity demands by wavelet transform and a neuro-fuzzy system is proposed. Firstly, the most appropriate inputs are selected and a dataset is constructed. Then, Haar wavelet transform is utilized to decompose the load data and eliminate noise. In the model, a hierarchical adaptive neuro-fuzzy inference system (HANFIS is suggested to solve the curse-of-dimensionality problem. Several heuristic algorithms including Gravitational Search Algorithm (GSA, Cuckoo Optimization Algorithm (COA, and Cuckoo Search (CS are utilized to optimize the clustering parameters which help form the rule base, and adaptive neuro-fuzzy inference system (ANFIS optimize the parameters in the antecedent and consequent parts of each sub-model. The proposed approach was applied to forecast the electricity load of Hanoi, Vietnam. The constructed models have shown high forecasting performances based on the performance indices calculated. The results demonstrate the validity of the approach. The obtained results were also compared with those of several other well-known methods including autoregressive integrated moving average (ARIMA and multiple linear regression (MLR. In our study, the wavelet CS-HANFIS model outperformed the others and provided more accurate forecasting.
Directory of Open Access Journals (Sweden)
Vinicius Amorim Sobreiro
2013-06-01
Full Text Available The definition of the product mix provides the allocation of the productive resources in the manufacture process and the optimization of productive system. However, the definition of the product mix is a problem of the NP-complete, in other words, of difficult solution. Taking this into account, with the aid of the Theory of Constraints - TOC, some constructive heuristics have been presented to help to solve this problem. Thus, the objective in this paper is to propose a new heuristics to provide better solutions when compared with the main heuristics presented in the literature, TOC-h of Fredendall and Lea. To accomplish this comparison, simulations were accomplished with the objective of identifying the production mix with the best throughput, considering CPU time and the characteristics of the productive ambient. The results show that the heuristics proposal was more satisfactory when compared to TOC-h and it shows good solution when compared with the optimum solution. This fact evidence the importance of the heuristics proposal in the definition of product mix.
Roozitalab, Ali; Asgharizadeh, Ezzatollah
2013-12-01
Warranty is now an integral part of each product. Since its length is directly related to the cost of production, it should be set in such a way that it would maximize revenue generation and customers' satisfaction. Furthermore, based on the behavior of customers, it is assumed that increasing the warranty period to earn the trust of more customers leads to more sales until the market is saturated. We should bear in mind that different groups of consumers have different consumption behaviors and that performance of the product has a direct impact on the failure rate over the life of the product. Therefore, the optimum duration for every group is different. In fact, we cannot present different warranty periods for various customer groups. In conclusion, using cuckoo meta-heuristic optimization algorithm, we try to find a common period for the entire population. Results with high convergence offer a term length that will maximize the aforementioned goals simultaneously. The study was tested using real data from Appliance Company. The results indicate a significant increase in sales when the optimization approach was applied; it provides a longer warranty through increased revenue from selling, not only reducing profit margins but also increasing it.
Energy Technology Data Exchange (ETDEWEB)
Pholdee, Nantiwat; Bureerat, Su Jin [Khon Kaen University, Khon Kaen (Thailand); Baek, Hyun Moo [DTaQ, Changwon (Korea, Republic of); Im, Yong Taek [KAIST, Daejeon (Korea, Republic of)
2015-08-15
Process optimization of a Non-circular drawing (NCD) sequence of a pearlitic steel wire was performed to improve the mechanical properties of a drawn wire based on surrogate assisted meta-heuristic algorithms. The objective function was introduced to minimize inhomogeneity of effective strain distribution at the cross-section of the drawn wire, which could deteriorate delamination characteristics of the drawn wires. The design variables introduced were die geometry and reduction of area of the NCD sequence. Several surrogate models and their combinations with the weighted sum technique were utilized. In the process optimization of the NCD sequence, the surrogate models were used to predict effective strain distributions at the cross-section of the drawn wire. Optimization using Differential evolution (DE) algorithm was performed, while the objective function was calculated from the predicted effective strains. The accuracy of all surrogate models was investigated, while optimum results were compared with the previous study available in the literature. It was found that hybrid surrogate models can improve prediction accuracy compared to a single surrogate model. The best result was obtained from the combination of Kriging (KG) and Support vector regression (SVR) models, while the second best was obtained from the combination of four surrogate models: Polynomial response surface (PRS), Radial basic function (RBF), KG, and SVR. The optimum results found in this study showed better effective strain homogeneity at the cross-section of the drawn wire with the same total reduction of area of the previous work available in the literature for fewer number of passes. The multi-surrogate models with the weighted sum technique were found to be powerful in improving the delamination characteristics of the drawn wire and reducing the production cost.
A heuristic technique to determine corrective control actions for reactive power flows
Energy Technology Data Exchange (ETDEWEB)
Trigo, Angel L.; Martinez, Jose L.; Riquelme, Jesus; Romero, Esther [Department of Electrical Engineering, University of Sevilla (Spain)
2011-01-15
This paper presents a sensitivity-based heuristic tool designed to help the system operator in the reactive power flow control problem. The objective of the proposed technique is to determine control actions to ensure that reactive power flows in transmission-subtransmission boundary transformers remain within specified limits, satisfying the new regulatory constraints imposed in most of deregulated markets. With this new constraint the utilities want to guarantee that the utility is able to satisfy its own reactive power requirements, avoiding reactive power flows through long distances in order to reduce the well known disadvantages that reactive power circulation has in the system. A 5-bus tutorial system is used to present the proposed algorithm. The results of the application of the proposed technique to the IEEE 118 buses system and to a regional subtransmission network of the South of Spain are reported and analyzed. In this last actual case, the aim is to maintain reactive power flows in transmission/distribution transformers between those limits set by the Spanish Regulation. A comparison between the proposed tool and a conventional OPF is discussed. (author)
Aldeen Yousra, S.; Mazleena, Salleh
2018-05-01
Recent advancement in Information and Communication Technologies (ICT) demanded much of cloud services to sharing users’ private data. Data from various organizations are the vital information source for analysis and research. Generally, this sensitive or private data information involves medical, census, voter registration, social network, and customer services. Primary concern of cloud service providers in data publishing is to hide the sensitive information of individuals. One of the cloud services that fulfill the confidentiality concerns is Privacy Preserving Data Mining (PPDM). The PPDM service in Cloud Computing (CC) enables data publishing with minimized distortion and absolute privacy. In this method, datasets are anonymized via generalization to accomplish the privacy requirements. However, the well-known privacy preserving data mining technique called K-anonymity suffers from several limitations. To surmount those shortcomings, I propose a new heuristic anonymization framework for preserving the privacy of sensitive datasets when publishing on cloud. The advantages of K-anonymity, L-diversity and (α, k)-anonymity methods for efficient information utilization and privacy protection are emphasized. Experimental results revealed the superiority and outperformance of the developed technique than K-anonymity, L-diversity, and (α, k)-anonymity measure.
Heuristic rules embedded genetic algorithm for in-core fuel management optimization
Alim, Fatih
The objective of this study was to develop a unique methodology and a practical tool for designing loading pattern (LP) and burnable poison (BP) pattern for a given Pressurized Water Reactor (PWR) core. Because of the large number of possible combinations for the fuel assembly (FA) loading in the core, the design of the core configuration is a complex optimization problem. It requires finding an optimal FA arrangement and BP placement in order to achieve maximum cycle length while satisfying the safety constraints. Genetic Algorithms (GA) have been already used to solve this problem for LP optimization for both PWR and Boiling Water Reactor (BWR). The GA, which is a stochastic method works with a group of solutions and uses random variables to make decisions. Based on the theories of evaluation, the GA involves natural selection and reproduction of the individuals in the population for the next generation. The GA works by creating an initial population, evaluating it, and then improving the population by using the evaluation operators. To solve this optimization problem, a LP optimization package, GARCO (Genetic Algorithm Reactor Code Optimization) code is developed in the framework of this thesis. This code is applicable for all types of PWR cores having different geometries and structures with an unlimited number of FA types in the inventory. To reach this goal, an innovative GA is developed by modifying the classical representation of the genotype. To obtain the best result in a shorter time, not only the representation is changed but also the algorithm is changed to use in-core fuel management heuristics rules. The improved GA code was tested to demonstrate and verify the advantages of the new enhancements. The developed methodology is explained in this thesis and preliminary results are shown for the VVER-1000 reactor hexagonal geometry core and the TMI-1 PWR. The improved GA code was tested to verify the advantages of new enhancements. The core physics code
International Nuclear Information System (INIS)
Tahani, Mojtaba; Babayan, Narek; Astaraei, Fatemeh Razi; Moghadam, Ali
2015-01-01
Highlights: • The performance of four different Meta heuristic optimization algorithms was studied. • Power coefficient and produced torque on stationary blade were selected as objective functions. • Chord and twist distributions were selected as decision variables. • All optimization algorithms were combined with blade element momentum theory. • The best Pareto front was obtained by multi objective flower pollination algorithm for HATCTs. - Abstract: The performance of horizontal axis tidal current turbines (HATCT) strongly depends on their geometry. According to this fact, the optimum performance will be achieved by optimized geometry. In this research study, the multi objective optimization of the HATCT is carried out by using four different multi objective optimization algorithms and their performance is evaluated in combination with blade element momentum theory (BEM). The second version of non-dominated sorting genetic algorithm (NSGA-II), multi objective particle swarm optimization algorithm (MOPSO), multi objective cuckoo search algorithm (MOCS) and multi objective flower pollination algorithm (MOFPA) are the selected algorithms. The power coefficient and the produced torque on stationary blade are selected as objective functions and chord and twist distributions along the blade span are selected as decision variables. These algorithms are combined with the blade element momentum (BEM) theory for the purpose of achieving the best Pareto front. The obtained Pareto fronts are compared with each other. Different sets of experiments are carried out by considering different numbers of iterations, population size and tip speed ratios. The Pareto fronts which are achieved by MOFPA and NSGA-II have better quality in comparison to MOCS and MOPSO, but on the other hand a detail comparison between the first fronts of MOFPA and NSGA-II indicated that MOFPA algorithm can obtain the best Pareto front and can maximize the power coefficient up to 4.3% and the
Du, Qishi; Mezey, Paul G.
1998-09-01
In this research we test and compare three possible atom-basedscreening functions used in the heuristic molecular lipophilicity potential(HMLP). Screening function 1 is a power distance-dependent function, b_{{i}} /| {R_{{i}}- r} |^γ, screening function 2is an exponential distance-dependent function, biexp(-| {R_i- r} |/d_0 , and screening function 3 is aweighted distance-dependent function, {{sign}}( {b_i } ){{exp}}ξ ( {| {R_i- r} |/| {b_i } |} )For every screening function, the parameters (γ ,d0, and ξ are optimized using 41 common organic molecules of 4 types of compounds:aliphatic alcohols, aliphatic carboxylic acids, aliphatic amines, andaliphatic alkanes. The results of calculations show that screening function3 cannot give chemically reasonable results, however, both the powerscreening function and the exponential screening function give chemicallysatisfactory results. There are two notable differences between screeningfunctions 1 and 2. First, the exponential screening function has largervalues in the short distance than the power screening function, thereforemore influence from the nearest neighbors is involved using screeningfunction 2 than screening function 1. Second, the power screening functionhas larger values in the long distance than the exponential screeningfunction, therefore screening function 1 is effected by atoms at longdistance more than screening function 2. For screening function 1, thesuitable range of parameter d0 is 1.5 < d0 < 3.0, and d0 = 2.0 is recommended. HMLP developed in this researchprovides a potential tool for computer-aided three-dimensional drugdesign.
Ortiz-Matos, L.; Aguila-Tellez, A.; Hincapié-Reyes, R. C.; González-Sanchez, J. W.
2017-07-01
In order to design electrification systems, recent mathematical models solve the problem of location, type of electrification components, and the design of possible distribution microgrids. However, due to the amount of points to be electrified increases, the solution to these models require high computational times, thereby becoming unviable practice models. This study posed a new heuristic method for the electrification of rural areas in order to solve the problem. This heuristic algorithm presents the deployment of rural electrification microgrids in the world, by finding routes for optimal placement lines and transformers in transmission and distribution microgrids. The challenge is to obtain a display with equity in losses, considering the capacity constraints of the devices and topology of the land at minimal economic cost. An optimal scenario ensures the electrification of all neighbourhoods to a minimum investment cost in terms of the distance between electric conductors and the amount of transformation devices.
A Heuristic Design Information Sharing Framework for Hard Discrete Optimization Problems
National Research Council Canada - National Science Library
Jacobson, Sheldon H
2007-01-01
.... This framework has been used to gain new insights into neighborhood structure designs that allow different neighborhood functions to share information when using the same heuristic applied to the same problem...
Optimized LTE cell planning for multiple user density subareas using meta-heuristic algorithms
Ghazzai, Hakim; Yaacoub, Elias E.; Alouini, Mohamed-Slim
2014-01-01
Base station deployment in cellular networks is one of the most fundamental problems in network design. This paper proposes a novel method for the cell planning problem for the fourth generation 4G-LTE cellular networks using meta heuristic
RELAXATION HEURISTICS FOR THE SET COVERING PROBLEM
Umetani, Shunji; Yagiura, Mutsunori; 柳浦, 睦憲
2007-01-01
The set covering problem (SCP) is one of representative combinatorial optimization problems, which has many practical applications. The continuous development of mathematical programming has derived a number of impressive heuristic algorithms as well as exact branch-and-bound algorithms, which can solve huge SCP instances of bus, railway and airline crew scheduling problems. We survey heuristic algorithms for SCP focusing mainly on contributions of mathematical programming techniques to heuri...
FocusHeuristics - expression-data-driven network optimization and disease gene prediction.
Ernst, Mathias; Du, Yang; Warsow, Gregor; Hamed, Mohamed; Endlich, Nicole; Endlich, Karlhans; Murua Escobar, Hugo; Sklarz, Lisa-Madeleine; Sender, Sina; Junghanß, Christian; Möller, Steffen; Fuellen, Georg; Struckmann, Stephan
2017-02-16
To identify genes contributing to disease phenotypes remains a challenge for bioinformatics. Static knowledge on biological networks is often combined with the dynamics observed in gene expression levels over disease development, to find markers for diagnostics and therapy, and also putative disease-modulatory drug targets and drugs. The basis of current methods ranges from a focus on expression-levels (Limma) to concentrating on network characteristics (PageRank, HITS/Authority Score), and both (DeMAND, Local Radiality). We present an integrative approach (the FocusHeuristics) that is thoroughly evaluated based on public expression data and molecular disease characteristics provided by DisGeNet. The FocusHeuristics combines three scores, i.e. the log fold change and another two, based on the sum and difference of log fold changes of genes/proteins linked in a network. A gene is kept when one of the scores to which it contributes is above a threshold. Our FocusHeuristics is both, a predictor for gene-disease-association and a bioinformatics method to reduce biological networks to their disease-relevant parts, by highlighting the dynamics observed in expression data. The FocusHeuristics is slightly, but significantly better than other methods by its more successful identification of disease-associated genes measured by AUC, and it delivers mechanistic explanations for its choice of genes.
Directory of Open Access Journals (Sweden)
S. López-Ruiz
2016-01-01
Full Text Available This paper presents the design of a tree sections corrugated horn antenna with a modified linear profile, using NURBS, suitable for radio-astronomy applications. The operating band ranges from 4.5 to 8.8 GHz. The aperture efficiency is higher than 84% and the return losses are greater than 20 dB in the whole bandwidth. The antenna optimization has been carried out with multiobjective versions of an evolutionary algorithm (EA and a particle swarm optimization (PSO algorithm. We show that both techniques provide good antenna design, but the experience carried out shows that the results of the evolutionary algorithm outperform the particle swarm results.
Simulation-based optimization parametric optimization techniques and reinforcement learning
Gosavi, Abhijit
2003-01-01
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduces the evolving area of simulation-based optimization. The book's objective is two-fold: (1) It examines the mathematical governing principles of simulation-based optimization, thereby providing the reader with the ability to model relevant real-life problems using these techniques. (2) It outlines the computational technology underlying these methods. Taken together these two aspects demonstrate that the mathematical and computational methods discussed in this book do work. Broadly speaking, the book has two parts: (1) parametric (static) optimization and (2) control (dynamic) optimization. Some of the book's special features are: *An accessible introduction to reinforcement learning and parametric-optimization techniques. *A step-by-step description of several algorithms of simulation-based optimization. *A clear and simple introduction to the methodology of neural networks. *A gentle introduction to converg...
Fazlollahtabar, Hamed
2010-12-01
Consumer expectations for automobile seat comfort continue to rise. With this said, it is evident that the current automobile seat comfort development process, which is only sporadically successful, needs to change. In this context, there has been growing recognition of the need for establishing theoretical and methodological automobile seat comfort. On the other hand, seat producer need to know the costumer's required comfort to produce based on their interests. The current research methodologies apply qualitative approaches due to anthropometric specifications. The most significant weakness of these approaches is the inexact extracted inferences. Despite the qualitative nature of the consumer's preferences there are some methods to transform the qualitative parameters into numerical value which could help seat producer to improve or enhance their products. Nonetheless this approach would help the automobile manufacturer to provide their seats from the best producer regarding to the consumers idea. In this paper, a heuristic multi criteria decision making technique is applied to make consumers preferences in the numeric value. This Technique is combination of Analytical Hierarchy Procedure (AHP), Entropy method, and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). A case study is conducted to illustrate the applicability and the effectiveness of the proposed heuristic approach. Copyright © 2010 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Dawid Połap
2017-09-01
Full Text Available In the proposed article, we present a nature-inspired optimization algorithm, which we called Polar Bear Optimization Algorithm (PBO. The inspiration to develop the algorithm comes from the way polar bears hunt to survive in harsh arctic conditions. These carnivorous mammals are active all year round. Frosty climate, unfavorable to other animals, has made polar bears adapt to the specific mode of exploration and hunting in large areas, not only over ice but also water. The proposed novel mathematical model of the way polar bears move in the search for food and hunt can be a valuable method of optimization for various theoretical and practical problems. Optimization is very similar to nature, similarly to search for optimal solutions for mathematical models animals search for optimal conditions to develop in their natural environments. In this method. we have used a model of polar bear behaviors as a search engine for optimal solutions. Proposed simulated adaptation to harsh winter conditions is an advantage for local and global search, while birth and death mechanism controls the population. Proposed PBO was evaluated and compared to other meta-heuristic algorithms using sample test functions and some classical engineering problems. Experimental research results were compared to other algorithms and analyzed using various parameters. The analysis allowed us to identify the leading advantages which are rapid recognition of the area by the relevant population and efficient birth and death mechanism to improve global and local search within the solution space.
Physical optimization of afterloading techniques
International Nuclear Information System (INIS)
Anderson, L.L.
1985-01-01
Physical optimization in brachytherapy refers to the process of determining the radioactive-source configuration which yields a desired dose distribution. In manually afterloaded intracavitary therapy for cervix cancer, discrete source strengths are selected iteratively to minimize the sum of squares of differences between trial and target doses. For remote afterloading with a stepping-source device, optimized (continuously variable) dwell times are obtained, either iteratively or analytically, to give least squares approximations to dose at an arbitrary number of points; in vaginal irradiation for endometrial cancer, the objective has included dose uniformity at applicator surface points in addition to a tapered contour of target dose at depth. For template-guided interstitial implants, seed placement at rectangular-grid mesh points may be least squares optimized within target volumes defined by computerized tomography; effective optimization is possible only for (uniform) seed strength high enough that the desired average peripheral dose is achieved with a significant fraction of empty seed locations. (orig.) [de
International Nuclear Information System (INIS)
Dahal, Keshav Prasad
2000-01-01
The work contained in this thesis demonstrates that there is a significant requirement for the development and application of new optimisation techniques for solving industrial scheduling problems, in order to achieve a better schedule with significant economic and operational impact. An investigation of how modern heuristic approaches, such as genetic algorithm (GA), simulated annealing (SA), fuzzy logic and hybrids of these techniques, may be developed, designed and implemented appropriately for solving short term and long term NP-hard scheduling problems that exist in electric power utilities and process facilities. GA and SA based methods are developed for generator maintenance scheduling using a novel integer encoding and appropriate GA and SA operators. Three hybrid approaches (an inoculated GA, a GA/SA and a GA with fuzzy logic) are proposed in order to improve the solution performance, and to take advantage of any flexibilities inherent in the problem. Five different GA-based approaches are investigated for solving the generation scheduling problem. Of those, a knowledge-based hybrid GA approach achieves better solutions in a shorter computational time. This approach integrates problem specific knowledge, heuristic dispatch calculation and linear programming within the GA-framework. The application of a GA-based methodology is proposed for the scheduling of storage tanks of a water treatment facility. The proposed approach is an integration of a GA and a heuristic rule-base. The GA string considers the tank allocation problem, and the heuristic approach solves the rate determination problems within the framework of the GA. For optimising the schedule of operations of a bulk handling port facility, a generic modelling tool is developed characterising the operational and maintenance activities of the facility. A GA-based approach is integrated with the simulation software for optimising the scheduling of operations of the facility. Each of these approaches is
Simple heuristics: A bridge between manual core design and automated optimization methods
International Nuclear Information System (INIS)
White, J.R.; Delmolino, P.M.
1993-01-01
The primary function of RESCUE is to serve as an aid in the analysis and identification of feasible loading patterns for LWR reload cores. The unique feature of RESCUE is that its physics model is based on some recent advances in generalized perturbation theory (GPT) methods. The high order GPT techniques offer the accuracy, computational efficiency, and flexibility needed for the implementation of a full range of capabilities within a set of compatible interactive (manual and semi-automated) and automated design tools. The basic design philosophy and current features within RESCUE are reviewed, and the new semi-automated capability is highlighted. The online advisor facility appears quite promising and it provides a natural bridge between the traditional trial-and-error manual process and the recent progress towards fully automated optimization sequences. (orig.)
Bakar, Sumarni Abu; Ibrahim, Milbah
2017-08-01
The shortest path problem is a popular problem in graph theory. It is about finding a path with minimum length between a specified pair of vertices. In any network the weight of each edge is usually represented in a form of crisp real number and subsequently the weight is used in the calculation of shortest path problem using deterministic algorithms. However, due to failure, uncertainty is always encountered in practice whereby the weight of edge of the network is uncertain and imprecise. In this paper, a modified algorithm which utilized heuristic shortest path method and fuzzy approach is proposed for solving a network with imprecise arc length. Here, interval number and triangular fuzzy number in representing arc length of the network are considered. The modified algorithm is then applied to a specific example of the Travelling Salesman Problem (TSP). Total shortest distance obtained from this algorithm is then compared with the total distance obtained from traditional nearest neighbour heuristic algorithm. The result shows that the modified algorithm can provide not only on the sequence of visited cities which shown to be similar with traditional approach but it also provides a good measurement of total shortest distance which is lesser as compared to the total shortest distance calculated using traditional approach. Hence, this research could contribute to the enrichment of methods used in solving TSP.
Optimization of Mangala Hydropower Station, Pakistan, using Optimization Techniques
Directory of Open Access Journals (Sweden)
Zaman Muhammad
2017-01-01
Full Text Available Hydropower generation is one of the key element in the economy of a country. The present study focusses on the optimal electricity generation from the Mangla reservoir in Pakistan. A mathematical model has been developed for the Mangla hydropower station and particle swarm and genetic algorithm optimization techniques were applied at this model for optimal electricity generation. Results revealed that electricity production increases with the application of optimization techniques at the proposed mathematical model. Genetic Algorithm can produce maximum electricity than Particle swarm optimization but the time of execution of particle swarm optimization is much lesser than the Genetic algorithm. Mangla hydropower station can produce up to 59*109 kWh electricity by using the flows optimally than 47*108 kWh production from traditional methods.
Hyper-heuristic applied to nuclear reactor core design
International Nuclear Information System (INIS)
Domingos, R P; Platt, G M
2013-01-01
The design of nuclear reactors gives rises to a series of optimization problems because of the need for high efficiency, availability and maintenance of security levels. Gradient-based techniques and linear programming have been applied, as well as genetic algorithms and particle swarm optimization. The nonlinearity, multimodality and lack of knowledge about the problem domain makes de choice of suitable meta-heuristic models particularly challenging. In this work we solve the optimization problem of a nuclear reactor core design through the application of an optimal sequence of meta-heuritics created automatically. This combinatorial optimization model is known as hyper-heuristic.
Directory of Open Access Journals (Sweden)
Markowski Marcin
2017-09-01
Full Text Available In recent years elastic optical networks have been perceived as a prospective choice for future optical networks due to better adjustment and utilization of optical resources than is the case with traditional wavelength division multiplexing networks. In the paper we investigate the elastic architecture as the communication network for distributed data centers. We address the problems of optimization of routing and spectrum assignment for large-scale computing systems based on an elastic optical architecture; particularly, we concentrate on anycast user to data center traffic optimization. We assume that computational resources of data centers are limited. For this offline problems we formulate the integer linear programming model and propose a few heuristics, including a meta-heuristic algorithm based on a tabu search method. We report computational results, presenting the quality of approximate solutions and efficiency of the proposed heuristics, and we also analyze and compare some data center allocation scenarios.
International Nuclear Information System (INIS)
Purdie, Thomas G.; Dinniwell, Robert E.; Letourneau, Daniel; Hill, Christine; Sharpe, Michael B.
2011-01-01
Purpose: To present an automated technique for two-field tangential breast intensity-modulated radiotherapy (IMRT) treatment planning. Method and Materials: A total of 158 planned patients with Stage 0, I, and II breast cancer treated using whole-breast IMRT were retrospectively replanned using automated treatment planning tools. The tools developed are integrated into the existing clinical treatment planning system (Pinnacle 3 ) and are designed to perform the manual volume delineation, beam placement, and IMRT treatment planning steps carried out by the treatment planning radiation therapist. The automated algorithm, using only the radio-opaque markers placed at CT simulation as inputs, optimizes the tangential beam parameters to geometrically minimize the amount of lung and heart treated while covering the whole-breast volume. The IMRT parameters are optimized according to the automatically delineated whole-breast volume. Results: The mean time to generate a complete treatment plan was 6 min, 50 s ± 1 min 12 s. For the automated plans, 157 of 158 plans (99%) were deemed clinically acceptable, and 138 of 158 plans (87%) were deemed clinically improved or equal to the corresponding clinical plan when reviewed in a randomized, double-blinded study by one experienced breast radiation oncologist. In addition, overall the automated plans were dosimetrically equivalent to the clinical plans when scored for target coverage and lung and heart doses. Conclusion: We have developed robust and efficient automated tools for fully inversed planned tangential breast IMRT planning that can be readily integrated into clinical practice. The tools produce clinically acceptable plans using only the common anatomic landmarks from the CT simulation process as an input. We anticipate the tools will improve patient access to high-quality IMRT treatment by simplifying the planning process and will reduce the effort and cost of incorporating more advanced planning into clinical practice.
Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations
Directory of Open Access Journals (Sweden)
Nadeem Javaid
2017-08-01
Full Text Available Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM programs using the genetic algorithm (GA, teaching learning-based optimization (TLBO, the enhanced differential evolution (EDE algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs are also integrated into the microgrid (MG. The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS. The main objectives of this paper include: (1 integration of RESs; (2 minimization of the electricity bill (cost and discomfort; and (3 minimizing the peak to average ratio (PAR and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives
Directory of Open Access Journals (Sweden)
MUDASIR AHMED MEMON
2017-01-01
Full Text Available In this paper, PSO (Particle Swarm Optimization based technique is proposed to derive optimized switching angles that minimizes the THD (Total Harmonic Distortion and reduces the effect of selected low order non-triple harmonics from the output of the multilevel inverter. Conventional harmonic elimination techniques have plenty of limitations, and other heuristic techniques also not provide the satisfactory results. In this paper, single phase symmetrical cascaded H-Bridge 11-Level multilevel inverter is considered, and proposed algorithm is utilized to obtain the optimized switching angles that reduced the effect of 5th, 7th, 11th and 13th non-triplen harmonics from the output voltage of the multilevel inverter. A simulation result indicates that this technique outperforms other methods in terms of minimizing THD and provides high-quality output voltage waveform.
International Nuclear Information System (INIS)
Memon, M.A.; Memon, S.; Khan, S.
2017-01-01
In this paper, PSO (Particle Swarm Optimization) based technique is proposed to derive optimized switching angles that minimizes the THD (Total Harmonic Distortion) and reduces the effect of selected low order non-triple harmonics from the output of the multilevel inverter. Conventional harmonic elimination techniques have plenty of limitations, and other heuristic techniques also not provide the satisfactory results. In this paper, single phase symmetrical cascaded H-Bridge 11-Level multilevel inverter is considered, and proposed algorithm is utilized to obtain the optimized switching angles that reduced the effect of 5th, 7th, 11th and 13th non-triplen harmonics from the output voltage of the multilevel inverter. A simulation result indicates that this technique outperforms other methods in terms of minimizing THD and provides high-quality output voltage waveform. (author)
Scaling Up Optimal Heuristic Search in Dec-POMDPs via Incremental Expansion (extended abstract)
Spaan, M.T.J.; Oliehoek, F.A.; Amato, C.
2011-01-01
We advance the state of the art in optimal solving of decentralized partially observable Markov decision processes (Dec-POMDPs), which provide a formal model for multiagent planning under uncertainty.
A heuristic approach to optimization of structural topology including self-weight
Tajs-Zielińska, Katarzyna; Bochenek, Bogdan
2018-01-01
Topology optimization of structures under a design-dependent self-weight load is investigated in this paper. The problem deserves attention because of its significant importance in the engineering practice, especially nowadays as topology optimization is more often applied when designing large engineering structures, for example, bridges or carrying systems of tall buildings. It is worth noting that well-known approaches of topology optimization which have been successfully applied to structures under fixed loads cannot be directly adapted to the case of design-dependent loads, so that topology generation can be a challenge also for numerical algorithms. The paper presents the application of a simple but efficient non-gradient method to topology optimization of elastic structures under self-weight loading. The algorithm is based on the Cellular Automata concept, the application of which can produce effective solutions with low computational cost.
López-Ruiz, S.; Sánchez Montero, R.; Tercero-Martínez, F.; López-Espí, P. L.; López-Fernandez, J. A.
2016-01-01
This paper presents the design of a tree sections corrugated horn antenna with a modified linear profile, using NURBS, suitable for radio-astronomy applications. The operating band ranges from 4.5 to 8.8 GHz. The aperture efficiency is higher than 84% and the return losses are greater than 20 dB in the whole bandwidth. The antenna optimization has been carried out with multiobjective versions of an evolutionary algorithm (EA) and a particle swarm optimization (PSO) algorithm. We show that bot...
Solving non-standard packing problems by global optimization and heuristics
Fasano, Giorgio
2014-01-01
This book results from a long-term research effort aimed at tackling complex non-standard packing issues which arise in space engineering. The main research objective is to optimize cargo loading and arrangement, in compliance with a set of stringent rules. Complicated geometrical aspects are also taken into account, in addition to balancing conditions based on attitude control specifications. Chapter 1 introduces the class of non-standard packing problems studied. Chapter 2 gives a detailed explanation of a general model for the orthogonal packing of tetris-like items in a convex domain. A number of additional conditions are looked at in depth, including the prefixed orientation of subsets of items, the presence of unusable holes, separation planes and structural elements, relative distance bounds as well as static and dynamic balancing requirements. The relative feasibility sub-problem which is a special case that does not have an optimization criterion is discussed in Chapter 3. This setting can be exploit...
Efficient reanalysis techniques for robust topology optimization
DEFF Research Database (Denmark)
Amir, Oded; Sigmund, Ole; Lazarov, Boyan Stefanov
2012-01-01
efficient robust topology optimization procedures based on reanalysis techniques. The approach is demonstrated on two compliant mechanism design problems where robust design is achieved by employing either a worst case formulation or a stochastic formulation. It is shown that the time spent on finite...
Energy Technology Data Exchange (ETDEWEB)
Pramanick, A.K.; Das, P.K. [Indian Inst. of Technology, Kharagpur (India). Dept. of Mechanical Engineering
2005-04-01
This article reports an alternative treatment in lieu of the principle of variational calculus for a certain class of optimization problems. In particular, the optimum distribution of insulating material on one side of a flat plate for minimum heat transfer is sought when the other side is exposed to a laminar forced convection. Both conjugate and non-conjugate formulations of the problem are conceived and closed form solutions are presented. Interestingly, optimized insulation profile exhibits a category of equipartition principle in some macroscopic domain. Expression for minimum heat transfer is a function of Biot number in non-conjugate analysis of the model. Contrastingly, the non-dimensional group Jh{sub L} is the characteristic parameter for conjugate formulation. Finally, Bejan's method of intersecting asymptotes is employed to find an order of magnitude for a ceiling value of the wall material. With some scale factor, a range 0 < J{sub max} {<=} 1.506Pr {sup -} {sup 1/3} for the representative material volume can be ascertained, beyond which the optimization exercise reduces to a trivial one and traditional constant thickness profile becomes a recognized design. (author)
Meta-Heuristics in Short Scale Construction: Ant Colony Optimization and Genetic Algorithm.
Schroeders, Ulrich; Wilhelm, Oliver; Olaru, Gabriel
2016-01-01
The advent of large-scale assessment, but also the more frequent use of longitudinal and multivariate approaches to measurement in psychological, educational, and sociological research, caused an increased demand for psychometrically sound short scales. Shortening scales economizes on valuable administration time, but might result in inadequate measures because reducing an item set could: a) change the internal structure of the measure, b) result in poorer reliability and measurement precision, c) deliver measures that cannot effectively discriminate between persons on the intended ability spectrum, and d) reduce test-criterion relations. Different approaches to abbreviate measures fare differently with respect to the above-mentioned problems. Therefore, we compare the quality and efficiency of three item selection strategies to derive short scales from an existing long version: a Stepwise COnfirmatory Factor Analytical approach (SCOFA) that maximizes factor loadings and two metaheuristics, specifically an Ant Colony Optimization (ACO) with a tailored user-defined optimization function and a Genetic Algorithm (GA) with an unspecific cost-reduction function. SCOFA compiled short versions were highly reliable, but had poor validity. In contrast, both metaheuristics outperformed SCOFA and produced efficient and psychometrically sound short versions (unidimensional, reliable, sensitive, and valid). We discuss under which circumstances ACO and GA produce equivalent results and provide recommendations for conditions in which it is advisable to use a metaheuristic with an unspecific out-of-the-box optimization function.
Non-heuristic reduction of the graph in graph-cut optimization
International Nuclear Information System (INIS)
Malgouyres, François; Lermé, Nicolas
2012-01-01
During the last ten years, graph cuts had a growing impact in shape optimization. In particular, they are commonly used in applications of shape optimization such as image processing, computer vision and computer graphics. Their success is due to their ability to efficiently solve (apparently) difficult shape optimization problems which typically involve the perimeter of the shape. Nevertheless, solving problems with a large number of variables remains computationally expensive and requires a high memory usage since underlying graphs sometimes involve billion of nodes and even more edges. Several strategies have been proposed in the literature to improve graph-cuts in this regards. In this paper, we give a formal statement which expresses that a simple and local test performed on every node before its construction permits to avoid the construction of useless nodes for the graphs typically encountered in image processing and vision. A useless node is such that the value of the maximum flow in the graph does not change when removing the node from the graph. Such a test therefore permits to limit the construction of the graph to a band of useful nodes surrounding the final cut.
Heuristic techniques for the analysis of variability as a dynamic aspect of change
Van Dijk, M.W.G.; Van Geert, P.
Due to the influence of dynamic systems and microgenetic perspectives, variability is nowadays often seen as an important phenomenon that helps us understand the underlying mechanisms of development. This paper aims at demonstrating several simple techniques that can be used to analyse variability
Tanjung, WN; Nurhasanah, N.; Suri, QA; Jingga; Aribowo, B.; Mardhika, DA; Gayatri, AM; Safitri, R.; Supriyanto, A.
2017-12-01
The textile industry is one of the 10 commodities of industrial products which are still survives in Indonesia due to the crisis in the year 2009 until 2016. Drawback happened in 2017 by increased the number of demand by approximate 3% compares with previous year. In this case, the research conducted in Small Medium Enterprise (SME) called FBS. SME is a business group that is able to absorb a lot of labor and a source of income for society. SME FBS producing clothing boys and domiciled in Jakarta. To complete FBS product, the WIP products are sent to CMT or depot in Sukabumi. In this study, aims to do the shortest route in the determination of the distribution of WIP product to 10 CMT scattered in the area of Sukabumi. After optimization hapened, the route must be started from the Depot SME FBS Sukabumi-Shell Sand Village - village of Sukamaju Village - Margaluyu Village - Narogong Cicurug, - the village of Parakanlima, Cuguha - Padabeunghar Village - Sagaranten Village - village of Ciherang, Ciguyang, Sagaranten - the village of Bojong Waru, Pasirsalam Village, Purabaya - Students - return to Depot SME FBS Sukabumi with mileage in a single trip of 403.6 kilometers. It spents 10 hours 09 minutes and cost distribution issued amounting to IDR 296,928.52. The route length was optimized 47% from 759.1 become 403.6 kilometers.
Directory of Open Access Journals (Sweden)
Sheraz Aslam
2017-12-01
Full Text Available The smart grid plays a vital role in decreasing electricity cost through Demand Side Management (DSM. Smart homes, a part of the smart grid, contribute greatly to minimizing electricity consumption cost via scheduling home appliances. However, user waiting time increases due to the scheduling of home appliances. This scheduling problem is the motivation to find an optimal solution that could minimize the electricity cost and Peak to Average Ratio (PAR with minimum user waiting time. There are many studies on Home Energy Management (HEM for cost minimization and peak load reduction. However, none of the systems gave sufficient attention to tackle multiple parameters (i.e., electricity cost and peak load reduction at the same time as user waiting time was minimum for residential consumers with multiple homes. Hence, in this work, we propose an efficient HEM scheme using the well-known meta-heuristic Genetic Algorithm (GA, the recently developed Cuckoo Search Optimization Algorithm (CSOA and the Crow Search Algorithm (CSA, which can be used for electricity cost and peak load alleviation with minimum user waiting time. The integration of a smart Electricity Storage System (ESS is also taken into account for more efficient operation of the Home Energy Management System (HEMS. Furthermore, we took the real-time electricity consumption pattern for every residence, i.e., every home has its own living pattern. The proposed scheme is implemented in a smart building; comprised of thirty smart homes (apartments, Real-Time Pricing (RTP and Critical Peak Pricing (CPP signals are examined in terms of electricity cost estimation for both a single smart home and a smart building. In addition, feasible regions are presented for single and multiple smart homes, which show the relationship among the electricity cost, electricity consumption and user waiting time. Experimental results demonstrate the effectiveness of our proposed scheme for single and multiple smart
Software for the grouped optimal aggregation technique
Brown, P. M.; Shaw, G. W. (Principal Investigator)
1982-01-01
The grouped optimal aggregation technique produces minimum variance, unbiased estimates of acreage and production for countries, zones (states), or any designated collection of acreage strata. It uses yield predictions, historical acreage information, and direct acreage estimate from satellite data. The acreage strata are grouped in such a way that the ratio model over historical acreage provides a smaller variance than if the model were applied to each individual stratum. An optimal weighting matrix based on historical acreages, provides the link between incomplete direct acreage estimates and the total, current acreage estimate.
SP-100 shield design automation process using expert system and heuristic search techniques
International Nuclear Information System (INIS)
Marcille, T.F.; Protsik, R.; Deane, N.A.; Hoover, D.G.
1993-01-01
The SP-100 shield subsystem design process has been modified to utilize the GE Corporate Reserch and Development program, ENGINEOUS (Tong 1990). ENGINEOUS is a software system that automates the use of Computer Aided Engineering (CAE) analysis programs in the engineering design process. The shield subsystem design process incorporates a nuclear subsystems design and performance code, a two-dimensional neutral particle transport code, several input processors and two general purpose neutronic output processors. Coupling these programs within ENGINEOUS provides automatic transition paths between applications, with no source code modifications. ENGINEOUS captures human design knowledge, as well as information about the specific CAE applications and stores this information in knowledge base files. The knowledge base information is used by the ENGINEOUS expert system to drive knowledge directed and knowledge supplemented search modules to find an optimum shield design for a given reactor definition, ensuring that specified constraints are satisfied. Alternate designs, not accommodated in the optimization design rules, can readily be explored through the use of a parametric study capability
Fusion blanket design and optimization techniques
International Nuclear Information System (INIS)
Gohar, Y.
2005-01-01
In fusion reactors, the blanket design and its characteristics have a major impact on the reactor performance, size, and economics. The selection and arrangement of the blanket materials, dimensions of the different blanket zones, and different requirements of the selected materials for a satisfactory performance are the main parameters, which define the blanket performance. These parameters translate to a large number of variables and design constraints, which need to be simultaneously considered in the blanket design process. This represents a major design challenge because of the lack of a comprehensive design tool capable of considering all these variables to define the optimum blanket design and satisfying all the design constraints for the adopted figure of merit and the blanket design criteria. The blanket design techniques of the First Wall/Blanket/Shield Design and Optimization System (BSDOS) have been developed to overcome this difficulty and to provide the state-of-the-art techniques and tools for performing blanket design and analysis. This report describes some of the BSDOS techniques and demonstrates its use. In addition, the use of the optimization technique of the BSDOS can result in a significant blanket performance enhancement and cost saving for the reactor design under consideration. In this report, examples are presented, which utilize an earlier version of the ITER solid breeder blanket design and a high power density self-cooled lithium blanket design for demonstrating some of the BSDOS blanket design techniques
Computational optimization techniques applied to microgrids planning
DEFF Research Database (Denmark)
Gamarra, Carlos; Guerrero, Josep M.
2015-01-01
Microgrids are expected to become part of the next electric power system evolution, not only in rural and remote areas but also in urban communities. Since microgrids are expected to coexist with traditional power grids (such as district heating does with traditional heating systems......), their planning process must be addressed to economic feasibility, as a long-term stability guarantee. Planning a microgrid is a complex process due to existing alternatives, goals, constraints and uncertainties. Usually planning goals conflict each other and, as a consequence, different optimization problems...... appear along the planning process. In this context, technical literature about optimization techniques applied to microgrid planning have been reviewed and the guidelines for innovative planning methodologies focused on economic feasibility can be defined. Finally, some trending techniques and new...
Directory of Open Access Journals (Sweden)
Cenk Demirkır
2014-04-01
Full Text Available Plywood, which is one of the most important wood based panels, has many usage areas changing from traffic signs to building constructions in many countries. It is known that the high quality plywood panel manufacturing has been achieved with a good bonding under the optimum pressure conditions depending on adhesive type. This is a study of determining the using possibilities of modern meta-heuristic hybrid artificial intelligence techniques such as IKE and AANN methods for prediction of bonding strength of plywood panels. This study has composed of two main parts as experimental and analytical. Scots pine, maritime pine and European black pine logs were used as wood species. The pine veneers peeled at 32°C and 50°C were dried at 110°C, 140°C and 160°C temperatures. Phenol formaldehyde and melamine urea formaldehyde resins were used as adhesive types. EN 314-1 standard was used to determine the bonding shear strength values of plywood panels in experimental part of this study. Then the intuitive k-nearest neighbor estimator (IKE and adaptive artificial neural network (AANN were used to estimate bonding strength of plywood panels. The best estimation performance was obtained from MA metric for k-value=10. The most effective factor on bonding strength was determined as adhesive type. Error rates were determined less than 5% for both of the IKE and AANN. It may be recommended that proposed methods could be used in applying to estimation of bonding strength values of plywood panels.
Directory of Open Access Journals (Sweden)
Ho-Lung Hung
2008-08-01
Full Text Available A suboptimal partial transmit sequence (PTS based on particle swarm optimization (PSO algorithm is presented for the low computation complexity and the reduction of the peak-to-average power ratio (PAPR of an orthogonal frequency division multiplexing (OFDM system. In general, PTS technique can improve the PAPR statistics of an OFDM system. However, it will come with an exhaustive search over all combinations of allowed phase weighting factors and the search complexity increasing exponentially with the number of subblocks. In this paper, we work around potentially computational intractability; the proposed PSO scheme exploits heuristics to search the optimal combination of phase factors with low complexity. Simulation results show that the new technique can effectively reduce the computation complexity and PAPR reduction.
Directory of Open Access Journals (Sweden)
Lee Shu-Hong
2008-01-01
Full Text Available Abstract A suboptimal partial transmit sequence (PTS based on particle swarm optimization (PSO algorithm is presented for the low computation complexity and the reduction of the peak-to-average power ratio (PAPR of an orthogonal frequency division multiplexing (OFDM system. In general, PTS technique can improve the PAPR statistics of an OFDM system. However, it will come with an exhaustive search over all combinations of allowed phase weighting factors and the search complexity increasing exponentially with the number of subblocks. In this paper, we work around potentially computational intractability; the proposed PSO scheme exploits heuristics to search the optimal combination of phase factors with low complexity. Simulation results show that the new technique can effectively reduce the computation complexity and PAPR reduction.
Energy Technology Data Exchange (ETDEWEB)
Yoo, Sua [Department of Radiation Oncology, Duke University Medical Center, Box 3295, Durham, NC 27710 (United States); Kowalok, Michael E [Department of Radiation Oncology, Virginia Commonwealth University Health System, 401 College St., PO Box 980058, Richmond, VA 23298-0058 (United States); Thomadsen, Bruce R [Department of Medical Physics, University of Wisconsin-Madison, 1530 MSC, 1300 University Ave., Madison, WI 53706 (United States); Henderson, Douglass L [Department of Engineering Physics, University of Wisconsin-Madison, 153 Engineering Research Bldg., 1500 Engineering Dr., Madison, WI 53706 (United States)
2007-02-07
We continue our work on the development of an efficient treatment-planning algorithm for prostate seed implants by incorporation of an automated seed and needle configuration routine. The treatment-planning algorithm is based on region of interest (ROI) adjoint functions and a greedy heuristic. As defined in this work, the adjoint function of an ROI is the sensitivity of the average dose in the ROI to a unit-strength brachytherapy source at any seed position. The greedy heuristic uses a ratio of target and critical structure adjoint functions to rank seed positions according to their ability to irradiate the target ROI while sparing critical structure ROIs. Because seed positions are ranked in advance and because the greedy heuristic does not modify previously selected seed positions, the greedy heuristic constructs a complete seed configuration quickly. Isodose surface constraints determine the search space and the needle constraint limits the number of needles. This study additionally includes a methodology that scans possible combinations of these constraint values automatically. This automated selection scheme saves the user the effort of manually searching constraint values. With this method, clinically acceptable treatment plans are obtained in less than 2 min. For comparison, the branch-and-bound method used to solve a mixed integer-programming model took close to 2.5 h to arrive at a feasible solution. Both methods achieved good treatment plans, but the speedup provided by the greedy heuristic was a factor of approximately 100. This attribute makes this algorithm suitable for intra-operative real-time treatment planning.
International Nuclear Information System (INIS)
Yoo, Sua; Kowalok, Michael E; Thomadsen, Bruce R; Henderson, Douglass L
2007-01-01
We continue our work on the development of an efficient treatment-planning algorithm for prostate seed implants by incorporation of an automated seed and needle configuration routine. The treatment-planning algorithm is based on region of interest (ROI) adjoint functions and a greedy heuristic. As defined in this work, the adjoint function of an ROI is the sensitivity of the average dose in the ROI to a unit-strength brachytherapy source at any seed position. The greedy heuristic uses a ratio of target and critical structure adjoint functions to rank seed positions according to their ability to irradiate the target ROI while sparing critical structure ROIs. Because seed positions are ranked in advance and because the greedy heuristic does not modify previously selected seed positions, the greedy heuristic constructs a complete seed configuration quickly. Isodose surface constraints determine the search space and the needle constraint limits the number of needles. This study additionally includes a methodology that scans possible combinations of these constraint values automatically. This automated selection scheme saves the user the effort of manually searching constraint values. With this method, clinically acceptable treatment plans are obtained in less than 2 min. For comparison, the branch-and-bound method used to solve a mixed integer-programming model took close to 2.5 h to arrive at a feasible solution. Both methods achieved good treatment plans, but the speedup provided by the greedy heuristic was a factor of approximately 100. This attribute makes this algorithm suitable for intra-operative real-time treatment planning
Machine Learning Techniques in Optimal Design
Cerbone, Giuseppe
1992-01-01
Many important applications can be formalized as constrained optimization tasks. For example, we are studying the engineering domain of two-dimensional (2-D) structural design. In this task, the goal is to design a structure of minimum weight that bears a set of loads. A solution to a design problem in which there is a single load (L) and two stationary support points (S1 and S2) consists of four members, E1, E2, E3, and E4 that connect the load to the support points is discussed. In principle, optimal solutions to problems of this kind can be found by numerical optimization techniques. However, in practice [Vanderplaats, 1984] these methods are slow and they can produce different local solutions whose quality (ratio to the global optimum) varies with the choice of starting points. Hence, their applicability to real-world problems is severely restricted. To overcome these limitations, we propose to augment numerical optimization by first performing a symbolic compilation stage to produce: (a) objective functions that are faster to evaluate and that depend less on the choice of the starting point and (b) selection rules that associate problem instances to a set of recommended solutions. These goals are accomplished by successive specializations of the problem class and of the associated objective functions. In the end, this process reduces the problem to a collection of independent functions that are fast to evaluate, that can be differentiated symbolically, and that represent smaller regions of the overall search space. However, the specialization process can produce a large number of sub-problems. This is overcome by deriving inductively selection rules which associate problems to small sets of specialized independent sub-problems. Each set of candidate solutions is chosen to minimize a cost function which expresses the tradeoff between the quality of the solution that can be obtained from the sub-problem and the time it takes to produce it. The overall solution
Evolutionary optimization technique for site layout planning
El Ansary, Ayman M.
2014-02-01
Solving the site layout planning problem is a challenging task. It requires an iterative approach to satisfy design requirements (e.g. energy efficiency, skyview, daylight, roads network, visual privacy, and clear access to favorite views). These design requirements vary from one project to another based on location and client preferences. In the Gulf region, the most important socio-cultural factor is the visual privacy in indoor space. Hence, most of the residential houses in this region are surrounded by high fences to provide privacy, which has a direct impact on other requirements (e.g. daylight and direction to a favorite view). This paper introduces a novel technique to optimally locate and orient residential buildings to satisfy a set of design requirements. The developed technique is based on genetic algorithm which explores the search space for possible solutions. This study considers two dimensional site planning problems. However, it can be extended to solve three dimensional cases. A case study is presented to demonstrate the efficiency of this technique in solving the site layout planning of simple residential dwellings. © 2013 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Wei Tu
2015-10-01
Full Text Available Vehicle routing optimization (VRO designs the best routes to reduce travel cost, energy consumption, and carbon emission. Due to non-deterministic polynomial-time hard (NP-hard complexity, many VROs involved in real-world applications require too much computing effort. Shortening computing time for VRO is a great challenge for state-of-the-art spatial optimization algorithms. From a spatial-temporal perspective, this paper presents a spatial-temporal Voronoi diagram-based heuristic approach for large-scale vehicle routing problems with time windows (VRPTW. Considering time constraints, a spatial-temporal Voronoi distance is derived from the spatial-temporal Voronoi diagram to find near neighbors in the space-time searching context. A Voronoi distance decay strategy that integrates a time warp operation is proposed to accelerate local search procedures. A spatial-temporal feature-guided search is developed to improve unpromising micro route structures. Experiments on VRPTW benchmarks and real-world instances are conducted to verify performance. The results demonstrate that the proposed approach is competitive with state-of-the-art heuristics and achieves high-quality solutions for large-scale instances of VRPTWs in a short time. This novel approach will contribute to spatial decision support community by developing an effective vehicle routing optimization method for large transportation applications in both public and private sectors.
Heuristics in Conflict Resolution
Drescher, Christian; Gebser, Martin; Kaufmann, Benjamin; Schaub, Torsten
2010-01-01
Modern solvers for Boolean Satisfiability (SAT) and Answer Set Programming (ASP) are based on sophisticated Boolean constraint solving techniques. In both areas, conflict-driven learning and related techniques constitute key features whose application is enabled by conflict analysis. Although various conflict analysis schemes have been proposed, implemented, and studied both theoretically and practically in the SAT area, the heuristic aspects involved in conflict analysis have not yet receive...
Parallel halftoning technique using dot diffusion optimization
Molina-Garcia, Javier; Ponomaryov, Volodymyr I.; Reyes-Reyes, Rogelio; Cruz-Ramos, Clara
2017-05-01
In this paper, a novel approach for halftone images is proposed and implemented for images that are obtained by the Dot Diffusion (DD) method. Designed technique is based on an optimization of the so-called class matrix used in DD algorithm and it consists of generation new versions of class matrix, which has no baron and near-baron in order to minimize inconsistencies during the distribution of the error. Proposed class matrix has different properties and each is designed for two different applications: applications where the inverse-halftoning is necessary, and applications where this method is not required. The proposed method has been implemented in GPU (NVIDIA GeForce GTX 750 Ti), multicore processors (AMD FX(tm)-6300 Six-Core Processor and in Intel core i5-4200U), using CUDA and OpenCV over a PC with linux. Experimental results have shown that novel framework generates a good quality of the halftone images and the inverse halftone images obtained. The simulation results using parallel architectures have demonstrated the efficiency of the novel technique when it is implemented in real-time processing.
Techniques for optimizing inerting in electron processors
International Nuclear Information System (INIS)
Rangwalla, I.J.; Korn, D.J.; Nablo, S.V.
1993-01-01
The design of an ''inert gas'' distribution system in an electron processor must satisfy a number of requirements. The first of these is the elimination or control of beam produced ozone and NO x which can be transported from the process zone by the product into the work area. Since the tolerable levels for O 3 in occupied areas around the processor are 3 in the beam heated process zone, or exhausting and dilution of the gas at the processor exit. The second requirement of the inerting system is to provide a suitable environment for completing efficient, free radical initiated addition polymerization. The competition between radical loss through de-excitation and that from O 2 quenching must be understood. This group has used gas chromatographic analysis of electron cured coatings to study the trade-offs of delivered dose, dose rate and O 2 concentrations in the process zone to determine the tolerable ranges of parameter excursions for production quality control purposes. These techniques are described for an ink coating system on paperboard, where a broad range of process parameters have been studied (D, D radical, O 2 ). It is then shown how the technique is used to optimize the use of higher purity (10-100 ppm O 2 ) nitrogen gas for inerting, in combination with lower purity (2-20,000 ppm O 2 ) non-cryogenically produced gas, as from a membrane or pressure swing adsorption generators. (author)
Optimal fuel loading pattern design using artificial intelligence techniques
International Nuclear Information System (INIS)
Kim, Han Gon; Chang, Soon Heung; Lee, Byung Ho
1993-01-01
The Optimal Fuel Shuffling System (OFSS) is developed for optimal design of PWR fuel loading pattern. OFSS is a hybrid system that a rule based system, a fuzzy logic, and an artificial neural network are connected each other. The rule based system classifies loading patterns into two classes using several heuristic rules and a fuzzy rule. A fuzzy rule is introduced to achieve more effective and fast searching. Its membership function is automatically updated in accordance with the prediction results. The artificial neural network predicts core parameters for the patterns generated from the rule based system. The back-propagation network is used for fast prediction of core parameters. The artificial neural network and the fuzzy logic can be used as the tool for improvement of existing algorithm's capabilities. OFSS was demonstrated and validated for cycle 1 of Kori unit 1 PWR. (Author)
Directory of Open Access Journals (Sweden)
Eneko Osaba
2016-12-01
Full Text Available This paper aims to give a presentation of the PhD defended by Eneko Osaba on November 16th, 2015, at the University of Deusto. The thesis can be placed in the field of artificial intelligence. Specifically, it is related with multi- population meta-heuristics for solving vehicle routing problems. The dissertation was held in the main auditorium of the University, in a publicly open presentation. After the presentation, Eneko was awarded with the highest grade (cum laude. Additionally, Eneko obtained the PhD obtaining award granted by the Basque Government through.
Application of Nontraditional Optimization Techniques for Airfoil Shape Optimization
Directory of Open Access Journals (Sweden)
R. Mukesh
2012-01-01
Full Text Available The method of optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays, various optimization methods, such as genetic algorithm (GA, simulated annealing (SA, and particle swarm optimization (PSO, are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterization becomes an important factor to be considered during the aerodynamic shape optimization process. The objective of this work is to introduce the knowledge of describing general airfoil geometry using twelve parameters by representing its shape as a polynomial function and coupling this approach with flow solution and optimization algorithms. An aerodynamic shape optimization problem is formulated for NACA 0012 airfoil and solved using the methods of simulated annealing and genetic algorithm for 5.0 deg angle of attack. The results show that the simulated annealing optimization scheme is more effective in finding the optimum solution among the various possible solutions. It is also found that the SA shows more exploitation characteristics as compared to the GA which is considered to be more effective explorer.
2015-01-01
How can we advance knowledge? Which methods do we need in order to make new discoveries? How can we rationally evaluate, reconstruct and offer discoveries as a means of improving the ‘method’ of discovery itself? And how can we use findings about scientific discovery to boost funding policies, thus fostering a deeper impact of scientific discovery itself? The respective chapters in this book provide readers with answers to these questions. They focus on a set of issues that are essential to the development of types of reasoning for advancing knowledge, such as models for both revolutionary findings and paradigm shifts; ways of rationally addressing scientific disagreement, e.g. when a revolutionary discovery sparks considerable disagreement inside the scientific community; frameworks for both discovery and inference methods; and heuristics for economics and the social sciences.
Directory of Open Access Journals (Sweden)
Khurram Hammed
2016-01-01
Full Text Available This paper presents a stochastic global optimization technique known as Particle Swarm Optimization (PSO for joint estimation of amplitude and direction of arrival of the targets in RADAR communication system. The proposed scheme is an excellent optimization methodology and a promising approach for solving the DOA problems in communication systems. Moreover, PSO is quite suitable for real time scenario and easy to implement in hardware. In this study, uniform linear array is used and targets are supposed to be in far field of the arrays. Formulation of the fitness function is based on mean square error and this function requires a single snapshot to obtain the best possible solution. To check the accuracy of the algorithm, all of the results are taken by varying the number of antenna elements and targets. Finally, these results are compared with existing heuristic techniques to show the accuracy of PSO.
Generation of Articulated Mechanisms by Optimization Techniques
DEFF Research Database (Denmark)
Kawamoto, Atsushi
2004-01-01
optimization [Paper 2] 3. Branch and bound global optimization [Paper 3] 4. Path-generation problems [Paper 4] In terms of the objective of the articulated mechanism design problems, the first to third papers deal with maximization of output displacement, while the fourth paper solves prescribed path...... generation problems. From a mathematical programming point of view, the methods proposed in the first and third papers are categorized as deterministic global optimization, while those of the second and fourth papers are categorized as gradient-based local optimization. With respect to design variables, only...... directly affects the result of the associated sensitivity analysis. Another critical issue for mechanism design is the concept of mechanical degrees of freedom and this should be also considered for obtaining a proper articulated mechanism. The thesis treats this inherently discrete criterion in some...
OPTIMAL DATA REPLACEMENT TECHNIQUE FOR COOPERATIVE CACHING IN MANET
Directory of Open Access Journals (Sweden)
P. Kuppusamy
2014-09-01
Full Text Available A cooperative caching approach improves data accessibility and reduces query latency in Mobile Ad hoc Network (MANET. Maintaining the cache is challenging issue in large MANET due to mobility, cache size and power. The previous research works on caching primarily have dealt with LRU, LFU and LRU-MIN cache replacement algorithms that offered low query latency and greater data accessibility in sparse MANET. This paper proposes Memetic Algorithm (MA to locate the better replaceable data based on neighbours interest and fitness value of cached data to store the newly arrived data. This work also elects ideal CH using Meta heuristic search Ant Colony Optimization algorithm. The simulation results shown that proposed algorithm reduces the latency, control overhead and increases the packet delivery rate than existing approach by increasing nodes and speed respectively.
Optimal placement of FACTS devices using optimization techniques: A review
Gaur, Dipesh; Mathew, Lini
2018-03-01
Modern power system is dealt with overloading problem especially transmission network which works on their maximum limit. Today’s power system network tends to become unstable and prone to collapse due to disturbances. Flexible AC Transmission system (FACTS) provides solution to problems like line overloading, voltage stability, losses, power flow etc. FACTS can play important role in improving static and dynamic performance of power system. FACTS devices need high initial investment. Therefore, FACTS location, type and their rating are vital and should be optimized to place in the network for maximum benefit. In this paper, different optimization methods like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) etc. are discussed and compared for optimal location, type and rating of devices. FACTS devices such as Thyristor Controlled Series Compensator (TCSC), Static Var Compensator (SVC) and Static Synchronous Compensator (STATCOM) are considered here. Mentioned FACTS controllers effects on different IEEE bus network parameters like generation cost, active power loss, voltage stability etc. have been analyzed and compared among the devices.
Optimal Technique in Cardiac Anesthesia Recovery
Svircevic, V.
2014-01-01
The aim of this thesis is to evaluate fast-track cardiac anesthesia techniques and investigate their impact on postoperative mortality, morbidity and quality of life. The following topics will be discussed in the thesis. (1.) Is fast track cardiac anesthesia a safe technique for cardiac surgery?
9th International Conference on Optimization : Techniques and Applications
Wang, Song; Wu, Soon-Yi
2015-01-01
This book presents the latest research findings and state-of-the-art solutions on optimization techniques and provides new research direction and developments. Both the theoretical and practical aspects of the book will be much beneficial to experts and students in optimization and operation research community. It selects high quality papers from The International Conference on Optimization: Techniques and Applications (ICOTA2013). The conference is an official conference series of POP (The Pacific Optimization Research Activity Group; there are over 500 active members). These state-of-the-art works in this book authored by recognized experts will make contributions to the development of optimization with its applications.
Hybrid Techniques for Optimizing Complex Systems
2009-12-01
relay placement problem, we modeled the network as a mechanical system with springs and a viscous damper ⎯a widely used approach for solving optimization...fundamental mathematical tools in many branches of physics such as fluid and solid mechanics, and general relativity [108]. More recently, several
Evolutionary optimization technique for site layout planning
El Ansary, Ayman M.; Shalaby, Mohamed
2014-01-01
of design requirements. The developed technique is based on genetic algorithm which explores the search space for possible solutions. This study considers two dimensional site planning problems. However, it can be extended to solve three dimensional cases. A
Optimal Technique in Cardiac Anesthesia Recovery
Svircevic, V.
2014-01-01
The aim of this thesis is to evaluate fast-track cardiac anesthesia techniques and investigate their impact on postoperative mortality, morbidity and quality of life. The following topics will be discussed in the thesis. (1.) Is fast track cardiac anesthesia a safe technique for cardiac surgery? (2.) Does thoracic epidural anesthesia have an effect on mortality and morbidity after cardiac surgery? (3.) Does thoracic epidural anesthesia have an effect on quality of life after cardiac surgery? ...
Core design optimization by integration of a fast 3-D nodal code in a heuristic search procedure
Energy Technology Data Exchange (ETDEWEB)
Geemert, R. van; Leege, P.F.A. de; Hoogenboom, J.E.; Quist, A.J. [Delft University of Technology, NL-2629 JB Delft (Netherlands)
1998-07-01
An automated design tool is being developed for the Hoger Onderwijs Reactor (HOR) in Delft, the Netherlands, which is a 2 MWth swimming-pool type research reactor. As a black box evaluator, the 3-D nodal code SILWER, which up to now has been used only for evaluation of predetermined core designs, is integrated in the core optimization procedure. SILWER is a part of PSl's ELCOS package and features optional additional thermal-hydraulic, control rods and xenon poisoning calculations. This allows for fast and accurate evaluation of different core designs during the optimization search. Special attention is paid to handling the in- and output files for SILWER such that no adjustment of the code itself is required for its integration in the optimization programme. The optimization objective, the safety and operation constraints, as well as the optimization procedure, are discussed. (author)
Core design optimization by integration of a fast 3-D nodal code in a heuristic search procedure
International Nuclear Information System (INIS)
Geemert, R. van; Leege, P.F.A. de; Hoogenboom, J.E.; Quist, A.J.
1998-01-01
An automated design tool is being developed for the Hoger Onderwijs Reactor (HOR) in Delft, the Netherlands, which is a 2 MWth swimming-pool type research reactor. As a black box evaluator, the 3-D nodal code SILWER, which up to now has been used only for evaluation of predetermined core designs, is integrated in the core optimization procedure. SILWER is a part of PSl's ELCOS package and features optional additional thermal-hydraulic, control rods and xenon poisoning calculations. This allows for fast and accurate evaluation of different core designs during the optimization search. Special attention is paid to handling the in- and output files for SILWER such that no adjustment of the code itself is required for its integration in the optimization programme. The optimization objective, the safety and operation constraints, as well as the optimization procedure, are discussed. (author)
A novel technique for active vibration control, based on optimal
Indian Academy of Sciences (India)
In the last few decades, researchers have proposed many control techniques to suppress unwanted vibrations in a structure. In this work, a novel and simple technique is proposed for the active vibration control. In this technique, an optimal tracking control is employed to suppress vibrations in a structure by simultaneously ...
Complex energy system management using optimization techniques
Energy Technology Data Exchange (ETDEWEB)
Bridgeman, Stuart; Hurdowar-Castro, Diana; Allen, Rick; Olason, Tryggvi; Welt, Francois
2010-09-15
Modern energy systems are often very complex with respect to the mix of generation sources, energy storage, transmission, and avenues to market. Historically, power was provided by government organizations to load centers, and pricing was provided in a regulatory manner. In recent years, this process has been displaced by the independent system operator (ISO). This complexity makes the operation of these systems very difficult, since the components of the system are interdependent. Consequently, computer-based large-scale simulation and optimization methods like Decision Support Systems are now being used. This paper discusses the application of a DSS to operations and planning systems.
Li, Xuejun; Xu, Jia; Yang, Yun
2015-01-01
Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.
Directory of Open Access Journals (Sweden)
Xuejun Li
2015-01-01
Full Text Available Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO and Particle Swarm Optimization (PSO have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts.
A Direct Heuristic Algorithm for Linear Programming
Indian Academy of Sciences (India)
Abstract. An (3) mathematically non-iterative heuristic procedure that needs no artificial variable is presented for solving linear programming problems. An optimality test is included. Numerical experiments depict the utility/scope of such a procedure.
Bayer Digester Optimization Studies using Computer Techniques
Kotte, Jan J.; Schleider, Victor H.
Theoretically required heat transfer performance by the multistaged flash heat reclaim system of a high pressure Bayer digester unit is determined for various conditions of discharge temperature, excess flash vapor and indirect steam addition. Solution of simultaneous heat balances around the digester vessels and the heat reclaim system yields the magnitude of available heat for representation of each case on a temperature-enthalpy diagram, where graphical fit of the number of flash stages fixes the heater requirements. Both the heat balances and the trial-and-error graphical solution are adapted to solution by digital computer techniques.
Diagnosis of scaphoid fracture: optimal imaging techniques
Directory of Open Access Journals (Sweden)
Geijer M
2013-07-01
Full Text Available Mats Geijer Center for Medical Imaging and Physiology, Skåne University Hospital and Lund University, Lund, Sweden Abstract: This review aims to provide an overview of modern imaging techniques for evaluation of scaphoid fracture, with emphasis on occult fractures and an outlook on the possible evolution of imaging; it also gives an overview of the pathologic and anatomic basis for selection of techniques. Displaced scaphoid fractures detected by wrist radiography, with or without special scaphoid views, pose no diagnostic problems. After wrist trauma with clinically suspected scaphoid fracture and normal scaphoid radiography, most patients will have no clinically important fracture. Between 5% and 19% of patients (on average 16% in meta-analyses will, however, have an occult scaphoid fracture which, untreated, may lead to later, potentially devastating, complications. Follow-up imaging may be done with repeat radiography, tomosynthesis, computed tomography, magnetic resonance imaging (MRI, or bone scintigraphy. However, no method is perfect, and choice of imaging may be based on availability, cost, perceived accuracy, or personal preference. Generally, MRI and bone scintigraphy are regarded as the most sensitive modalities, but both are flawed by false positive results at various rates. Keywords: occult fracture, wrist, radiography, computed tomography, magnetic resonance imaging, radionuclide imaging
DEFF Research Database (Denmark)
Sousa, Tiago; Morais, Hugo; Castro, Rui
2016-01-01
vehicles. The proposed algorithms proved to present results very close to the optimal with a small difference between 0.1%. A deterministic technique is used as comparison and it took around 26 h to obtain the optimal one. On the other hand, the simulated annealing was able of obtaining results around 1...
Acceleration techniques in the univariate Lipschitz global optimization
Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.; De Franco, Angela
2016-10-01
Univariate box-constrained Lipschitz global optimization problems are considered in this contribution. Geometric and information statistical approaches are presented. The novel powerful local tuning and local improvement techniques are described in the contribution as well as the traditional ways to estimate the Lipschitz constant. The advantages of the presented local tuning and local improvement techniques are demonstrated using the operational characteristics approach for comparing deterministic global optimization algorithms on the class of 100 widely used test functions.
Knowledge discovery in hyper-heuristic using case-based reasoning on course timetabling
Burke, Edmund; MacCarthy, Bart L.; Petrovic, Sanja; Qu, Rong
2002-01-01
This paper presents a new hyper-heuristic method using Case-Based Reasoning (CBR) for solving course timetabling problems. The term Hyper-heuristics has recently been employed to refer to 'heuristics that choose heuristics' rather than heuristics that operate directly on given problems. One of the overriding motivations of hyper-heuristic methods is the attempt to develop techniques that can operate with greater generality than is currently possible. The basic idea behind this is that we main...
Directory of Open Access Journals (Sweden)
Fanrong Kong
2017-09-01
Full Text Available To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving.
Heuristic Search Theory and Applications
Edelkamp, Stefan
2011-01-01
Search has been vital to artificial intelligence from the very beginning as a core technique in problem solving. The authors present a thorough overview of heuristic search with a balance of discussion between theoretical analysis and efficient implementation and application to real-world problems. Current developments in search such as pattern databases and search with efficient use of external memory and parallel processing units on main boards and graphics cards are detailed. Heuristic search as a problem solving tool is demonstrated in applications for puzzle solving, game playing, constra
de Jong, Menno D.T.; van der Geest, Thea
2000-01-01
This article is intended to make Web designers more aware of the qualities of heuristics by presenting a framework for analyzing the characteristics of heuristics. The framework is meant to support Web designers in choosing among alternative heuristics. We hope that better knowledge of the
Assessing Use of Cognitive Heuristic Representativeness in Clinical Reasoning
Payne, Velma L.; Crowley, Rebecca S.
2008-01-01
We performed a pilot study to investigate use of the cognitive heuristic Representativeness in clinical reasoning. We tested a set of tasks and assessments to determine whether subjects used the heuristics in reasoning, to obtain initial frequencies of heuristic use and related cognitive errors, and to collect cognitive process data using think-aloud techniques. The study investigates two aspects of the Representativeness heuristic - judging by perceived frequency and representativeness as ca...
International Nuclear Information System (INIS)
Trovão, João P.; Antunes, Carlos Henggeler
2015-01-01
Highlights: • Two meta-heuristic approaches are evaluated for multi-ESS management in electric vehicles. • An online global energy management strategy with two different layers is studied. • Meta-heuristic techniques are used to define optimized energy sharing mechanisms. • A comparative analysis for ARTEMIS driving cycle is addressed. • The effectiveness of the double-layer management with meta-heuristic is presented. - Abstract: This work is focused on the performance evaluation of two meta-heuristic approaches, simulated annealing and particle swarm optimization, to deal with power management of a dual energy storage system for electric vehicles. The proposed strategy is based on a global energy management system with two layers: long-term (energy) and short-term (power) management. A rule-based system deals with the long-term (strategic) layer and for the short-term (action) layer meta-heuristic techniques are developed to define optimized online energy sharing mechanisms. Simulations have been made for several driving cycles to validate the proposed strategy. A comparative analysis for ARTEMIS driving cycle is presented evaluating three performance indicators (computation time, final value of battery state of charge, and minimum value of supercapacitors state of charge) as a function of input parameters. The results show the effectiveness of an implementation based on a double-layer management system using meta-heuristic methods for online power management supported by a rule set that restricts the search space
Tabu search, a versatile technique for the functions optimization
International Nuclear Information System (INIS)
Castillo M, J.A.
2003-01-01
The basic elements of the Tabu search technique are presented, putting emphasis in the qualities that it has in comparison with the traditional methods of optimization known as in descending pass. Later on some modifications are sketched that have been implemented in the technique along the time, so that this it is but robust. Finally they are given to know some areas where this technique has been applied, obtaining successful results. (Author)
Modeling reproductive decisions with simple heuristics
Directory of Open Access Journals (Sweden)
Peter Todd
2013-10-01
Full Text Available BACKGROUND Many of the reproductive decisions that humans make happen without much planning or forethought, arising instead through the use of simple choice rules or heuristics that involve relatively little information and processing. Nonetheless, these heuristic-guided decisions are typically beneficial, owing to humans' ecological rationality - the evolved fit between our constrained decision mechanisms and the adaptive problems we face. OBJECTIVE This paper reviews research on the ecological rationality of human decision making in the domain of reproduction, showing how fertility-related decisions are commonly made using various simple heuristics matched to the structure of the environment in which they are applied, rather than being made with information-hungry mechanisms based on optimization or rational economic choice. METHODS First, heuristics for sequential mate search are covered; these heuristics determine when to stop the process of mate search by deciding that a good-enough mate who is also mutually interested has been found, using a process of aspiration-level setting and assessing. These models are tested via computer simulation and comparison to demographic age-at-first-marriage data. Next, a heuristic process of feature-based mate comparison and choice is discussed, in which mate choices are determined by a simple process of feature-matching with relaxing standards over time. Parental investment heuristics used to divide resources among offspring are summarized. Finally, methods for testing the use of such mate choice heuristics in a specific population over time are then described.
Heuristic attacks against graphical password generators
CSIR Research Space (South Africa)
Peach, S
2010-05-01
Full Text Available In this paper the authors explore heuristic attacks against graphical password generators. A new trend is emerging to use user clickable pictures to generate passwords. This technique of authentication can be successfully used for - for example...
Operation optimization of distributed generation using artificial intelligent techniques
Directory of Open Access Journals (Sweden)
Mahmoud H. Elkazaz
2016-06-01
Full Text Available Future smart grids will require an observable, controllable and flexible network architecture for reliable and efficient energy delivery. The use of artificial intelligence and advanced communication technologies is essential in building a fully automated system. This paper introduces a new technique for online optimal operation of distributed generation (DG resources, i.e. a hybrid fuel cell (FC and photovoltaic (PV system for residential applications. The proposed technique aims to minimize the total daily operating cost of a group of residential homes by managing the operation of embedded DG units remotely from a control centre. The target is formed as an objective function that is solved using genetic algorithm (GA optimization technique. The optimal settings of the DG units obtained from the optimization process are sent to each DG unit through a fully automated system. The results show that the proposed technique succeeded in defining the optimal operating points of the DGs that affect directly the total operating cost of the entire system.
Gigerenzer, Gerd; Gaissmaier, Wolfgang
2011-01-01
As reflected in the amount of controversy, few areas in psychology have undergone such dramatic conceptual changes in the past decade as the emerging science of heuristics. Heuristics are efficient cognitive processes, conscious or unconscious, that ignore part of the information. Because using heuristics saves effort, the classical view has been that heuristic decisions imply greater errors than do "rational" decisions as defined by logic or statistical models. However, for many decisions, the assumptions of rational models are not met, and it is an empirical rather than an a priori issue how well cognitive heuristics function in an uncertain world. To answer both the descriptive question ("Which heuristics do people use in which situations?") and the prescriptive question ("When should people rely on a given heuristic rather than a complex strategy to make better judgments?"), formal models are indispensable. We review research that tests formal models of heuristic inference, including in business organizations, health care, and legal institutions. This research indicates that (a) individuals and organizations often rely on simple heuristics in an adaptive way, and (b) ignoring part of the information can lead to more accurate judgments than weighting and adding all information, for instance for low predictability and small samples. The big future challenge is to develop a systematic theory of the building blocks of heuristics as well as the core capacities and environmental structures these exploit.
An optimization planning technique for Suez Canal Network in Egypt
Energy Technology Data Exchange (ETDEWEB)
Abou El-Ela, A.A.; El-Zeftawy, A.A.; Allam, S.M.; Atta, Gasir M. [Electrical Engineering Dept., Faculty of Eng., Shebin El-Kom (Egypt)
2010-02-15
This paper introduces a proposed optimization technique POT for predicting the peak load demand and planning of transmission line systems. Many of traditional methods have been presented for long-term load forecasting of electrical power systems. But, the results of these methods are approximated. Therefore, the artificial neural network (ANN) technique for long-term peak load forecasting is modified and discussed as a modern technique in long-term load forecasting. The modified technique is applied on the Egyptian electrical network dependent on its historical data to predict the electrical peak load demand forecasting up to year 2017. This technique is compared with extrapolation of trend curves as a traditional method. The POT is applied also to obtain the optimal planning of transmission lines for the 220 kV of Suez Canal Network (SCN) using the ANN technique. The minimization of the transmission network costs are considered as an objective function, while the transmission lines (TL) planning constraints are satisfied. Zafarana site on the Red Sea coast is considered as an optimal site for installing big wind farm (WF) units in Egypt. So, the POT is applied to plan both the peak load and the electrical transmission of SCN with and without considering WF to develop the impact of WF units on the electrical transmission system of Egypt, considering the reliability constraints which were taken as a separate model in the previous techniques. The application on SCN shows the capability and the efficiently of the proposed techniques to obtain the predicting peak load demand and the optimal planning of transmission lines of SCN up to year 2017. (author)
An ordering heuristic to develop the binary decision diagram based on structural importance
International Nuclear Information System (INIS)
Bartlett, L.M.; Andrews, J.D.
2001-01-01
Fault tree analysis is often used to assess risks within industrial systems. The technique is commonly used although there are associated limitations in terms of accuracy and efficiency when dealing with large fault tree structures. The most recent approach to aid the analysis of the fault tree diagram is the Binary Decision Diagram (BDD) methodology. To utilise the technique the fault tree structure needs to be converted into the BDD format. Converting the fault tree requires the basic events of the tree to be placed in an ordering. The ordering of the basic events is critical to the resulting size of the BDD, and ultimately affects the performance and benefits of this technique. A number of heuristic approaches have been developed to produce an optimal ordering permutation for a specific tree. These heuristic approaches do not always yield a minimal BDD structure for all trees. This paper looks at a heuristic that is based on the structural importance measure of each basic event. Comparing the resulting size of the BDD with the smallest generated from a set of six alternative ordering heuristics, this new structural heuristic produced a BDD of smaller or equal dimension on 77% of trials
An improved technique for the prediction of optimal image resolution ...
African Journals Online (AJOL)
user
2010-10-04
Oct 4, 2010 ... Available online at http://www.academicjournals.org/AJEST ... robust technique for predicting optimal image resolution for the mapping of savannah ecosystems was developed. .... whether to purchase multi-spectral imagery acquired by GeoEye-2 ..... Analysis of the spectral behaviour of the pasture class in.
Energy Technology Data Exchange (ETDEWEB)
Gonzalez C, J.; Martin del Campo M, C.; Francois L, J.L. [Facultad de Ingenieria, UNAM, Paseo Cuauhnahuac 8532, 62550 Jiutepec, Morelos (Mexico)
2004-07-01
The objective of this work is to verify the validity of the heuristic rules that have been applied in the processes of radial optimization of fuel cells. It was examined the rule with respect to the accommodation of fuel in the corners of the cell and it became special attention on the influence of the position and concentration of those pellets with gadolinium in the reactivity of the cell and the safety parameters. The evaluation behaved on designed cells violating the heuristic rules. For both cases the cells were analyzed between infinite using the HELIOS code. Additionally, for the second case, it was behaved a stage more exhaustive where it was used one of the studied cells that it completed those safety parameters and of reactivity to generate the design of an assemble that was used to calculate with CM-PRESTO the behavior of the nucleus during three operation cycles. (Author)
Automatic Generation of Heuristics for Scheduling
Morris, Robert A.; Bresina, John L.; Rodgers, Stuart M.
1997-01-01
This paper presents a technique, called GenH, that automatically generates search heuristics for scheduling problems. The impetus for developing this technique is the growing consensus that heuristics encode advice that is, at best, useful in solving most, or typical, problem instances, and, at worst, useful in solving only a narrowly defined set of instances. In either case, heuristic problem solvers, to be broadly applicable, should have a means of automatically adjusting to the idiosyncrasies of each problem instance. GenH generates a search heuristic for a given problem instance by hill-climbing in the space of possible multi-attribute heuristics, where the evaluation of a candidate heuristic is based on the quality of the solution found under its guidance. We present empirical results obtained by applying GenH to the real world problem of telescope observation scheduling. These results demonstrate that GenH is a simple and effective way of improving the performance of an heuristic scheduler.
Directory of Open Access Journals (Sweden)
I PUTU SUDANA
2011-01-01
Full Text Available Prinsip heuristics tidak dapat dikatakan sebagai sebuah pendekatanpengambilan keputusan yang non-rasional, karena penerapan atau penggunaanyang unconscious atau subtle mind tidak dapat dianggap sebagai tindakanyang irrational. Dengan alasan tersebut, terdapat cukup alasan untukmenyatakan bahwa pengklasifikasian pendekatan-pendekatan keputusansemestinya menggunakan terminologi analytical dan experiential, dan bukanmemakai istilah rational dan non-rational seperti yang umumnya diikuti.Penerapan pendekatan heuristics dapat ditemukan pada berbagai disiplin,termasuk bisnis dan akuntansi. Topik heuristics semestinya mendapatperhatian yang cukup luas dari para periset di bidang akuntansi. Bidangbehavioral research in accounting menawarkan banyak kemungkinan untukdikaji, karena prinsip heuristics bertautan erat dengan aspek manusia sebagaipelaku dalam pengambilan keputusan.
Optimization and Optimal Control in Automotive Systems
Waschl, H.; Kolmanovsky, I.V.; Steinbuch, M.; Re, del L.
2014-01-01
This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier approaches, based on some degree of heuristics, to the use of more and
An Image Morphing Technique Based on Optimal Mass Preserving Mapping
Zhu, Lei; Yang, Yan; Haker, Steven; Tannenbaum, Allen
2013-01-01
Image morphing, or image interpolation in the time domain, deals with the metamorphosis of one image into another. In this paper, a new class of image morphing algorithms is proposed based on the theory of optimal mass transport. The L2 mass moving energy functional is modified by adding an intensity penalizing term, in order to reduce the undesired double exposure effect. It is an intensity-based approach and, thus, is parameter free. The optimal warping function is computed using an iterative gradient descent approach. This proposed morphing method is also extended to doubly connected domains using a harmonic parameterization technique, along with finite-element methods. PMID:17547128
TECHNIQUE OF OPTIMAL AUDIT PLANNING FOR INFORMATION SECURITY MANAGEMENT SYSTEM
Directory of Open Access Journals (Sweden)
F. N. Shago
2014-03-01
Full Text Available Complication of information security management systems leads to the necessity of improving the scientific and methodological apparatus for these systems auditing. Planning is an important and determining part of information security management systems auditing. Efficiency of audit will be defined by the relation of the reached quality indicators to the spent resources. Thus, there is an important and urgent task of developing methods and techniques for optimization of the audit planning, making it possible to increase its effectiveness. The proposed technique gives the possibility to implement optimal distribution for planning time and material resources on audit stages on the basis of dynamics model for the ISMS quality. Special feature of the proposed approach is the usage of a priori data as well as a posteriori data for the initial audit planning, and also the plan adjustment after each audit event. This gives the possibility to optimize the usage of audit resources in accordance with the selected criteria. Application examples of the technique are given while planning audit information security management system of the organization. The result of computational experiment based on the proposed technique showed that the time (cost audit costs can be reduced by 10-15% and, consequently, quality assessments obtained through audit resources allocation can be improved with respect to well-known methods of audit planning.
Optimization of Hydraulic Machinery Bladings by Multilevel CFD Techniques
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Thum Susanne
2005-01-01
Full Text Available The numerical design optimization for complex hydraulic machinery bladings requires a high number of design parameters and the use of a precise CFD solver yielding high computational costs. To reduce the CPU time needed, a multilevel CFD method has been developed. First of all, the 3D blade geometry is parametrized by means of a geometric design tool to reduce the number of design parameters. To keep geometric accuracy, a special B-spline modification technique has been developed. On the first optimization level, a quasi-3D Euler code (EQ3D is applied. To guarantee a sufficiently accurate result, the code is calibrated by a Navier-Stokes recalculation of the initial design and can be recalibrated after a number of optimization steps by another Navier-Stokes computation. After having got a convergent solution, the optimization process is repeated on the second level using a full 3D Euler code yielding a more accurate flow prediction. Finally, a 3D Navier-Stokes code is applied on the third level to search for the optimum optimorum by means of a fine-tuning of the geometrical parameters. To show the potential of the developed optimization system, the runner blading of a water turbine having a specific speed n q = 41 1 / min was optimized applying the multilevel approach.
Assessing the use of cognitive heuristic representativeness in clinical reasoning.
Payne, Velma L; Crowley, Rebecca S; Crowley, Rebecca
2008-11-06
We performed a pilot study to investigate use of the cognitive heuristic Representativeness in clinical reasoning. We tested a set of tasks and assessments to determine whether subjects used the heuristics in reasoning, to obtain initial frequencies of heuristic use and related cognitive errors, and to collect cognitive process data using think-aloud techniques. The study investigates two aspects of the Representativeness heuristic - judging by perceived frequency and representativeness as causal beliefs. Results show that subjects apply both aspects of the heuristic during reasoning, and make errors related to misapplication of these heuristics. Subjects in this study rarely used base rates, showed significant variability in their recall of base rates, demonstrated limited ability to use provided base rates, and favored causal data in diagnosis. We conclude that the tasks and assessments we have developed provide a suitable test-bed to study the cognitive processes underlying heuristic errors.
Assessing Use of Cognitive Heuristic Representativeness in Clinical Reasoning
Payne, Velma L.; Crowley, Rebecca S.
2008-01-01
We performed a pilot study to investigate use of the cognitive heuristic Representativeness in clinical reasoning. We tested a set of tasks and assessments to determine whether subjects used the heuristics in reasoning, to obtain initial frequencies of heuristic use and related cognitive errors, and to collect cognitive process data using think-aloud techniques. The study investigates two aspects of the Representativeness heuristic - judging by perceived frequency and representativeness as causal beliefs. Results show that subjects apply both aspects of the heuristic during reasoning, and make errors related to misapplication of these heuristics. Subjects in this study rarely used base rates, showed significant variability in their recall of base rates, demonstrated limited ability to use provided base rates, and favored causal data in diagnosis. We conclude that the tasks and assessments we have developed provide a suitable test-bed to study the cognitive processes underlying heuristic errors. PMID:18999140
A decoupled power flow algorithm using particle swarm optimization technique
International Nuclear Information System (INIS)
Acharjee, P.; Goswami, S.K.
2009-01-01
A robust, nondivergent power flow method has been developed using the particle swarm optimization (PSO) technique. The decoupling properties between the power system quantities have been exploited in developing the power flow algorithm. The speed of the power flow algorithm has been improved using a simple perturbation technique. The basic power flow algorithm and the improvement scheme have been designed to retain the simplicity of the evolutionary approach. The power flow is rugged, can determine the critical loading conditions and also can handle the flexible alternating current transmission system (FACTS) devices efficiently. Test results on standard test systems show that the proposed method can find the solution when the standard power flows fail.
Quantitative Portfolio Optimization Techniques Applied to the Brazilian Stock Market
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André Alves Portela Santos
2012-09-01
Full Text Available In this paper we assess the out-of-sample performance of two alternative quantitative portfolio optimization techniques - mean-variance and minimum variance optimization – and compare their performance with respect to a naive 1/N (or equally-weighted portfolio and also to the market portfolio given by the Ibovespa. We focus on short selling-constrained portfolios and consider alternative estimators for the covariance matrices: sample covariance matrix, RiskMetrics, and three covariance estimators proposed by Ledoit and Wolf (2003, Ledoit and Wolf (2004a and Ledoit and Wolf (2004b. Taking into account alternative portfolio re-balancing frequencies, we compute out-of-sample performance statistics which indicate that the quantitative approaches delivered improved results in terms of lower portfolio volatility and better risk-adjusted returns. Moreover, the use of more sophisticated estimators for the covariance matrix generated optimal portfolios with lower turnover over time.
Material saving by means of CWR technology using optimization techniques
Pérez, Iñaki; Ambrosio, Cristina
2017-10-01
Material saving is currently a must for the forging companies, as material costs sum up to 50% for parts made of steel and up to 90% in other materials like titanium. For long products, cross wedge rolling (CWR) technology can be used to obtain forging preforms with a suitable distribution of the material along its own axis. However, defining the correct preform dimensions is not an easy task and it could need an intensive trial-and-error campaign. To speed up the preform definition, it is necessary to apply optimization techniques on Finite Element Models (FEM) able to reproduce the material behaviour when being rolled. Meta-models Assisted Evolution Strategies (MAES), that combine evolutionary algorithms with Kriging meta-models, are implemented in FORGE® software and they allow reducing optimization computation costs in a relevant way. The paper shows the application of these optimization techniques to the definition of the right preform for a shaft from a vehicle of the agricultural sector. First, the current forging process, based on obtaining the forging preform by means of an open die forging operation, is showed. Then, the CWR preform optimization is developed by using the above mentioned optimization techniques. The objective is to reduce, as much as possible, the initial billet weight, so that a calculation of flash weight reduction due to the use of the proposed preform is stated. Finally, a simulation of CWR process for the defined preform is carried out to check that most common failures (necking, spirals,..) in CWR do not appear in this case.
Novel optimization technique of isolated microgrid with hydrogen energy storage.
Beshr, Eman Hassan; Abdelghany, Hazem; Eteiba, Mahmoud
2018-01-01
This paper presents a novel optimization technique for energy management studies of an isolated microgrid. The system is supplied by various Distributed Energy Resources (DERs), Diesel Generator (DG), a Wind Turbine Generator (WTG), Photovoltaic (PV) arrays and supported by fuel cell/electrolyzer Hydrogen storage system for short term storage. Multi-objective optimization is used through non-dominated sorting genetic algorithm to suit the load requirements under the given constraints. A novel multi-objective flower pollination algorithm is utilized to check the results. The Pros and cons of the two optimization techniques are compared and evaluated. An isolated microgrid is modelled using MATLAB software package, dispatch of active/reactive power, optimal load flow analysis with slack bus selection are carried out to be able to minimize fuel cost and line losses under realistic constraints. The performance of the system is studied and analyzed during both summer and winter conditions and three case studies are presented for each condition. The modified IEEE 15 bus system is used to validate the proposed algorithm.
Electrostatic afocal-zoom lens design using computer optimization technique
Energy Technology Data Exchange (ETDEWEB)
Sise, Omer, E-mail: omersise@gmail.com
2014-12-15
Highlights: • We describe the detailed design of a five-element electrostatic afocal-zoom lens. • The simplex optimization is used to optimize lens voltages. • The method can be applied to multi-element electrostatic lenses. - Abstract: Electron optics is the key to the successful operation of electron collision experiments where well designed electrostatic lenses are needed to drive electron beam before and after the collision. In this work, the imaging properties and aberration analysis of an electrostatic afocal-zoom lens design were investigated using a computer optimization technique. We have found a whole new range of voltage combinations that has gone unnoticed until now. A full range of voltage ratios and spherical and chromatic aberration coefficients were systematically analyzed with a range of magnifications between 0.3 and 3.2. The grid-shadow evaluation was also employed to show the effect of spherical aberration. The technique is found to be useful for searching the optimal configuration in a multi-element lens system.
Novel optimization technique of isolated microgrid with hydrogen energy storage.
Directory of Open Access Journals (Sweden)
Eman Hassan Beshr
Full Text Available This paper presents a novel optimization technique for energy management studies of an isolated microgrid. The system is supplied by various Distributed Energy Resources (DERs, Diesel Generator (DG, a Wind Turbine Generator (WTG, Photovoltaic (PV arrays and supported by fuel cell/electrolyzer Hydrogen storage system for short term storage. Multi-objective optimization is used through non-dominated sorting genetic algorithm to suit the load requirements under the given constraints. A novel multi-objective flower pollination algorithm is utilized to check the results. The Pros and cons of the two optimization techniques are compared and evaluated. An isolated microgrid is modelled using MATLAB software package, dispatch of active/reactive power, optimal load flow analysis with slack bus selection are carried out to be able to minimize fuel cost and line losses under realistic constraints. The performance of the system is studied and analyzed during both summer and winter conditions and three case studies are presented for each condition. The modified IEEE 15 bus system is used to validate the proposed algorithm.
Structural Sustainability - Heuristic Approach
Rostański, Krzysztof
2017-10-01
Nowadays, we are faced with a challenge of having to join building structures with elements of nature, which seems to be the paradigm of modern planning and design. The questions arise, however, with reference to the following categories: the leading idea, the relation between elements of nature and buildings, the features of a structure combining such elements and, finally, our perception of this structure. If we consider both the overwhelming globalization and our attempts to preserve local values, the only reasonable solution is to develop naturalistic greenery. It can add its uniqueness to any building and to any developed area. Our holistic model, presented in this paper, contains the above mentioned categories within the scope of naturalism. The model is divided into principles, actions related, and possible effects to be obtained. It provides a useful tool for determining the ways and priorities of our design. Although it is not possible to consider all possible actions and solutions in order to support sustainability in any particular design, we can choose, however, a proper mode for our design according to the local conditions by turning to the heuristic method, which helps to choose priorities and targets. Our approach is an attempt to follow the ways of nature as in the natural environment it is optimal solutions that appear and survive, idealism being the domain of mankind only. We try to describe various natural processes in a manner comprehensible to us, which is always a generalization. Such definitions, however, called artificial by naturalists, are presented as art or the current state of knowledge by artists and engineers. Reality, in fact, is always more complicated than its definitions. The heuristic method demonstrates the way how to optimize our design. It requires that all possible information about the local environment should be gathered, as the more is known, the fewer mistakes are made. Following the unquestionable principles, we can
Further heuristics for $k$-means: The merge-and-split heuristic and the $(k,l)$-means
Nielsen, Frank; Nock, Richard
2014-01-01
Finding the optimal $k$-means clustering is NP-hard in general and many heuristics have been designed for minimizing monotonically the $k$-means objective. We first show how to extend Lloyd's batched relocation heuristic and Hartigan's single-point relocation heuristic to take into account empty-cluster and single-point cluster events, respectively. Those events tend to increasingly occur when $k$ or $d$ increases, or when performing several restarts. First, we show that those special events ...
Age Effects and Heuristics in Decision Making.
Besedeš, Tibor; Deck, Cary; Sarangi, Sudipta; Shor, Mikhael
2012-05-01
Using controlled experiments, we examine how individuals make choices when faced with multiple options. Choice tasks are designed to mimic the selection of health insurance, prescription drug, or retirement savings plans. In our experiment, available options can be objectively ranked allowing us to examine optimal decision making. First, the probability of a person selecting the optimal option declines as the number of options increases, with the decline being more pronounced for older subjects. Second, heuristics differ by age with older subjects relying more on suboptimal decision rules. In a heuristics validation experiment, older subjects make worse decisions than younger subjects.
Energy Technology Data Exchange (ETDEWEB)
Souza Lima, Carlos A. [Instituto de Engenharia Nuclear - Divisao de Reatores/PPGIEN, Rua Helio de Almeida 75, Cidade Universitaria - Ilha do Fundao, P.O. Box: 68550 - Zip Code: 21941-972, Rio de Janeiro (Brazil); Instituto Politecnico, Universidade do Estado do Rio de Janeiro, Pos-Graduacao em Modelagem Computacional, Rua Alberto Rangel - s/n, Vila Nova, Nova Friburgo, Zip Code: 28630-050, Nova Friburgo (Brazil); Lapa, Celso Marcelo F.; Pereira, Claudio Marcio do N.A. [Instituto de Engenharia Nuclear - Divisao de Reatores/PPGIEN, Rua Helio de Almeida 75, Cidade Universitaria - Ilha do Fundao, P.O. Box: 68550 - Zip Code: 21941-972, Rio de Janeiro (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores (INCT) (Brazil); Cunha, Joao J. da [Eletronuclear Eletrobras Termonuclear - Gerencia de Analise de Seguranca Nuclear, Rua da Candelaria, 65, 7 andar. Centro, Zip Code: 20091-906, Rio de Janeiro (Brazil); Alvim, Antonio Carlos M. [Universidade Federal do Rio de Janeiro, COPPE/Nuclear, Cidade Universitaria - Ilha do Fundao s/n, P.O.Box 68509 - Zip Code: 21945-970, Rio de Janeiro (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores (INCT) (Brazil)
2011-06-15
Research highlights: > Performance of PSO and GA techniques applied to similar system design. > This work uses ANGRA1 (two loop PWR) core as a prototype. > Results indicate that PSO technique is more adequate than GA to solve this kind of problem. - Abstract: This paper compares the performance of two optimization techniques, particle swarm optimization (PSO) and genetic algorithm (GA) applied to the design a typical reduced scale two loop Pressurized Water Reactor (PWR) core, at full power in single phase forced circulation flow. This comparison aims at analyzing the performance in reaching the global optimum, considering that both heuristics are based on population search methods, that is, methods whose population (candidate solution set) evolve from one generation to the next using a combination of deterministic and probabilistic rules. The simulated PWR, similar to ANGRA 1 power plant, was used as a case example to compare the performance of PSO and GA. Results from simulations indicated that PSO is more adequate to solve this kind of problem.
International Nuclear Information System (INIS)
Souza Lima, Carlos A.; Lapa, Celso Marcelo F.; Pereira, Claudio Marcio do N.A.; Cunha, Joao J. da; Alvim, Antonio Carlos M.
2011-01-01
Research highlights: → Performance of PSO and GA techniques applied to similar system design. → This work uses ANGRA1 (two loop PWR) core as a prototype. → Results indicate that PSO technique is more adequate than GA to solve this kind of problem. - Abstract: This paper compares the performance of two optimization techniques, particle swarm optimization (PSO) and genetic algorithm (GA) applied to the design a typical reduced scale two loop Pressurized Water Reactor (PWR) core, at full power in single phase forced circulation flow. This comparison aims at analyzing the performance in reaching the global optimum, considering that both heuristics are based on population search methods, that is, methods whose population (candidate solution set) evolve from one generation to the next using a combination of deterministic and probabilistic rules. The simulated PWR, similar to ANGRA 1 power plant, was used as a case example to compare the performance of PSO and GA. Results from simulations indicated that PSO is more adequate to solve this kind of problem.
"The Gaze Heuristic:" Biography of an Adaptively Rational Decision Process.
Hamlin, Robert P
2017-04-01
This article is a case study that describes the natural and human history of the gaze heuristic. The gaze heuristic is an interception heuristic that utilizes a single input (deviation from a constant angle of approach) repeatedly as a task is performed. Its architecture, advantages, and limitations are described in detail. A history of the gaze heuristic is then presented. In natural history, the gaze heuristic is the only known technique used by predators to intercept prey. In human history the gaze heuristic was discovered accidentally by Royal Air Force (RAF) fighter command just prior to World War II. As it was never discovered by the Luftwaffe, the technique conferred a decisive advantage upon the RAF throughout the war. After the end of the war in America, German technology was combined with the British heuristic to create the Sidewinder AIM9 missile, the most successful autonomous weapon ever built. There are no plans to withdraw it or replace its guiding gaze heuristic. The case study demonstrates that the gaze heuristic is a specific heuristic type that takes a single best input at the best time (take the best 2 ). Its use is an adaptively rational response to specific, rapidly evolving decision environments that has allowed those animals/humans/machines who use it to survive, prosper, and multiply relative to those who do not. Copyright © 2017 Cognitive Science Society, Inc.
Heuristics as Bayesian inference under extreme priors.
Parpart, Paula; Jones, Matt; Love, Bradley C
2018-05-01
Simple heuristics are often regarded as tractable decision strategies because they ignore a great deal of information in the input data. One puzzle is why heuristics can outperform full-information models, such as linear regression, which make full use of the available information. These "less-is-more" effects, in which a relatively simpler model outperforms a more complex model, are prevalent throughout cognitive science, and are frequently argued to demonstrate an inherent advantage of simplifying computation or ignoring information. In contrast, we show at the computational level (where algorithmic restrictions are set aside) that it is never optimal to discard information. Through a formal Bayesian analysis, we prove that popular heuristics, such as tallying and take-the-best, are formally equivalent to Bayesian inference under the limit of infinitely strong priors. Varying the strength of the prior yields a continuum of Bayesian models with the heuristics at one end and ordinary regression at the other. Critically, intermediate models perform better across all our simulations, suggesting that down-weighting information with the appropriate prior is preferable to entirely ignoring it. Rather than because of their simplicity, our analyses suggest heuristics perform well because they implement strong priors that approximate the actual structure of the environment. We end by considering how new heuristics could be derived by infinitely strengthening the priors of other Bayesian models. These formal results have implications for work in psychology, machine learning and economics. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
Methodology and Implementation on DSP of Heuristic Multiuser DS/CDMA Detectors
Directory of Open Access Journals (Sweden)
Alex Miyamoto Mussi
2010-12-01
Full Text Available The growing number of users of mobile communications networks and the scarcity of the electromagnetic spectrum make the use of diversity techniques and detection/decoding efficient, such as the use of multiple antennas at the transmitter and/or receiver, multiuser detection (MuD – Multiuser Detection, among others, have an increasingly prominent role in the telecommunications landscape. This paper presents a design methodology based on digital signal processors (DSP – Digital Signal Processor with a view to the implementation of multiuser heuristics detectors in systems DS/CDMA (Direct Sequence Code Division Multiple Access. Heuristics detection techniques result in near-optimal performance in order to approach the performance of maximum-likelihood (ML. In this work, was employed the DSP development platform called the C6713 DSK, which is based in Texas TMS320C6713 processor. The heuristics techniques proposed are based on well established algorithms in the literature. The efficiency of the algorithms implemented in DSP has been evaluated numerically by computing the measure of bit error rate (BER. Finally, the feasibility of implementation in DSP could then be verified by comparing results from multiple Monte-Carlo simulation in Matlab, with those obtained from implementation on DSP. It also demonstrates the effective increase in performance and system capacity of DS/CDMA with the use of heuristic multiuser detection techniques, implemented directly in the DSP.
Photon attenuation correction technique in SPECT based on nonlinear optimization
International Nuclear Information System (INIS)
Suzuki, Shigehito; Wakabayashi, Misato; Okuyama, Keiichi; Kuwamura, Susumu
1998-01-01
Photon attenuation correction in SPECT was made using a nonlinear optimization theory, in which an optimum image is searched so that the sum of square errors between observed and reprojected projection data is minimized. This correction technique consists of optimization and step-width algorithms, which determine at each iteration a pixel-by-pixel directional value of search and its step-width, respectively. We used the conjugate gradient and quasi-Newton methods as the optimization algorithm, and Curry rule and the quadratic function method as the step-width algorithm. Statistical fluctuations in the corrected image due to statistical noise in the emission projection data grew as the iteration increased, depending on the combination of optimization and step-width algorithms. To suppress them, smoothing for directional values was introduced. Computer experiments and clinical applications showed a pronounced reduction in statistical fluctuations of the corrected image for all combinations. Combinations using the conjugate gradient method were superior in noise characteristic and computation time. The use of that method with the quadratic function method was optimum if noise property was regarded as important. (author)
Simple heuristics and rules of thumb: where psychologists and behavioural biologists might meet.
Hutchinson, John M C; Gigerenzer, Gerd
2005-05-31
The Centre for Adaptive Behaviour and Cognition (ABC) has hypothesised that much human decision-making can be described by simple algorithmic process models (heuristics). This paper explains this approach and relates it to research in biology on rules of thumb, which we also review. As an example of a simple heuristic, consider the lexicographic strategy of Take The Best for choosing between two alternatives: cues are searched in turn until one discriminates, then search stops and all other cues are ignored. Heuristics consist of building blocks, and building blocks exploit evolved or learned abilities such as recognition memory; it is the complexity of these abilities that allows the heuristics to be simple. Simple heuristics have an advantage in making decisions fast and with little information, and in avoiding overfitting. Furthermore, humans are observed to use simple heuristics. Simulations show that the statistical structures of different environments affect which heuristics perform better, a relationship referred to as ecological rationality. We contrast ecological rationality with the stronger claim of adaptation. Rules of thumb from biology provide clearer examples of adaptation because animals can be studied in the environments in which they evolved. The range of examples is also much more diverse. To investigate them, biologists have sometimes used similar simulation techniques to ABC, but many examples depend on empirically driven approaches. ABC's theoretical framework can be useful in connecting some of these examples, particularly the scattered literature on how information from different cues is integrated. Optimality modelling is usually used to explain less detailed aspects of behaviour but might more often be redirected to investigate rules of thumb.
Reliability analysis of large scaled structures by optimization technique
International Nuclear Information System (INIS)
Ishikawa, N.; Mihara, T.; Iizuka, M.
1987-01-01
This paper presents a reliability analysis based on the optimization technique using PNET (Probabilistic Network Evaluation Technique) method for the highly redundant structures having a large number of collapse modes. This approach makes the best use of the merit of the optimization technique in which the idea of PNET method is used. The analytical process involves the minimization of safety index of the representative mode, subjected to satisfaction of the mechanism condition and of the positive external work. The procedure entails the sequential performance of a series of the NLP (Nonlinear Programming) problems, where the correlation condition as the idea of PNET method pertaining to the representative mode is taken as an additional constraint to the next analysis. Upon succeeding iterations, the final analysis is achieved when a collapse probability at the subsequent mode is extremely less than the value at the 1st mode. The approximate collapse probability of the structure is defined as the sum of the collapse probabilities of the representative modes classified by the extent of correlation. Then, in order to confirm the validity of the proposed method, the conventional Monte Carlo simulation is also revised by using the collapse load analysis. Finally, two fairly large structures were analyzed to illustrate the scope and application of the approach. (orig./HP)
Optimization Techniques for 3D Graphics Deployment on Mobile Devices
Koskela, Timo; Vatjus-Anttila, Jarkko
2015-03-01
3D Internet technologies are becoming essential enablers in many application areas including games, education, collaboration, navigation and social networking. The use of 3D Internet applications with mobile devices provides location-independent access and richer use context, but also performance issues. Therefore, one of the important challenges facing 3D Internet applications is the deployment of 3D graphics on mobile devices. In this article, we present an extensive survey on optimization techniques for 3D graphics deployment on mobile devices and qualitatively analyze the applicability of each technique from the standpoints of visual quality, performance and energy consumption. The analysis focuses on optimization techniques related to data-driven 3D graphics deployment, because it supports off-line use, multi-user interaction, user-created 3D graphics and creation of arbitrary 3D graphics. The outcome of the analysis facilitates the development and deployment of 3D Internet applications on mobile devices and provides guidelines for future research.
Villar-Rodriguez, Esther
2015-01-01
According to the report published by the online protection firm Iovation in 2012, cyber fraud ranged from 1 percent of the Internet transactions in North America Africa to a 7 percent in Africa, most of them involving credit card fraud, identity theft, and account takeover or h¼acking attempts. This kind of crime is still growing due to the advantages offered by a non face-to-face channel where a increasing number of unsuspecting victims divulges sensitive information. Interpol...
Greenhouse Environmental Control Using Optimized MIMO PID Technique
Directory of Open Access Journals (Sweden)
Fateh BOUNAAMA
2011-10-01
Full Text Available Climate control for protected crops brings the added dimension of a biological system into a physical system control situation. The thermally dynamic nature of a greenhouse suggests that disturbance attenuation (load control of external temperature, humidity, and sunlight is far more important than is the case for controlling other types of buildings. This paper investigates the application of multi-inputs multi-outputs (MIMO PID controller to a MIMO greenhouse environmental model with actuation constraints. This method is based on decoupling the system at low frequency point. The optimal tuning values are determined using genetic algorithms optimization (GA. The inside outsides climate model of the environmental greenhouse, and the automatically collected data sets of Avignon, France are used to simulate and test this technique. The control objective is to maintain a highly coupled inside air temperature and relative humidity of strongly perturbed greenhouse, at specified set-points, by the ventilation/cooling and moisturizing operations.
Efficient Heuristics for Simulating Population Overflow in Parallel Networks
Zaburnenko, T.S.; Nicola, V.F.
2006-01-01
In this paper we propose a state-dependent importance sampling heuristic to estimate the probability of population overflow in networks of parallel queues. This heuristic approximates the “optimal��? state-dependent change of measure without the need for costly optimization involved in other
Pitfalls in Teaching Judgment Heuristics
Shepperd, James A.; Koch, Erika J.
2005-01-01
Demonstrations of judgment heuristics typically focus on how heuristics can lead to poor judgments. However, exclusive focus on the negative consequences of heuristics can prove problematic. We illustrate the problem with the representativeness heuristic and present a study (N = 45) that examined how examples influence understanding of the…
Can we trust module-respect heuristics?
International Nuclear Information System (INIS)
Mo, Yuchang
2013-01-01
BDD (Binary Decision Diagrams) have proven to be a very efficient tool to assess Fault Trees. However, the size of BDD, and therefore the efficiency of the whole methodology, depends dramatically on the choice of variable ordering. The determination of the best variable ordering is intractable. Therefore, heuristics have been designed to select reasonably good variable orderings. One very important common feature for good static heuristics is to respect modules. In this paper, the notion of module-respect is studied in a systematic way. It is proved that under certain condition there always exists an optimal ordering that respects modules. This condition is that for each module there is always a smallest module BDD and each included module variable appears only once. On the other hand, it is shown that for the trees not satisfying the above sufficient condition the optimal orderings may not be able to be directly generated using module-respect heuristics, even when the shuffling strategy is used.
National Research Council Canada - National Science Library
Keedwell, Edward
2005-01-01
... Intelligence and Computer Science 3.1 Introduction to search 3.2 Search algorithms 3.3 Heuristic search methods 3.4 Optimal search strategies 3.5 Problems with search techniques 3.6 Complexity of...
International Nuclear Information System (INIS)
Kauffman, L.H.
1990-01-01
This paper gives a heuristic derivation of the skein relation for the Homfly polynomial in an integral formalism. The derivation is formally correct but highly simplified. In the light of Witten's proposal for invariants of links via functional integrals, it is useful to have a formal pattern to compare with the complexities of the full approach. The formalism is a heuristic. However, it is closely related to the actual structure of the Witten functional integral
Multivariate Analysis Techniques for Optimal Vision System Design
DEFF Research Database (Denmark)
Sharifzadeh, Sara
The present thesis considers optimization of the spectral vision systems used for quality inspection of food items. The relationship between food quality, vision based techniques and spectral signature are described. The vision instruments for food analysis as well as datasets of the food items...... used in this thesis are described. The methodological strategies are outlined including sparse regression and pre-processing based on feature selection and extraction methods, supervised versus unsupervised analysis and linear versus non-linear approaches. One supervised feature selection algorithm...... (SSPCA) and DCT based characterization of the spectral diffused reflectance images for wavelength selection and discrimination. These methods together with some other state-of-the-art statistical and mathematical analysis techniques are applied on datasets of different food items; meat, diaries, fruits...
Optimization of AFP-radioimmunoassay using Antibody Capture Technique
International Nuclear Information System (INIS)
Moustafa, K.A.
2003-01-01
Alpha-fetoprotein (AFP) is a substance produced by the unborn baby. When the neural tube is not properly formed large amounts of AFP pass into the amniotic fluid and reach the mother's blood. By measuring AFP in the mother's blood and amniotic fluid, it is possible to tell whether or not there is a chance that the unborn baby has a neural tube defect. AFP also used as a tumor marker for hepatocellular carcinoma. There are many different techniques for measuring AFP in blood, but the most accurate one is the immunoassay technique. The immunoassays can be classified on the basis of methodology into three classes; (1) the antibody capture assays, (2) the antigen capture assay, (3)the two-antibody sandwich assays. In this present study, the antibody capture assay in which the antigen is attached to a solid support, and labeled antibody is allowed to bind, will be optimized
Optimization of analytical techniques to characterize antibiotics in aquatic systems
International Nuclear Information System (INIS)
Al Mokh, S.
2013-01-01
Antibiotics are considered as pollutants when they are present in aquatic ecosystems, ultimate receptacles of anthropogenic substances. These compounds are studied as their persistence in the environment or their effects on natural organisms. Numerous efforts have been made worldwide to assess the environmental quality of different water resources for the survival of aquatic species, but also for human consumption and health risk related. Towards goal, the optimization of analytical techniques for these compounds in aquatic systems remains a necessity. Our objective is to develop extraction and detection methods for 12 molecules of aminoglycosides and colistin in sewage treatment plants and hospitals waters. The lack of analytical methods for analysis of these compounds and the deficiency of studies for their detection in water is the reason for their study. Solid Phase Extraction (SPE) in classic mode (offline) or online followed by Liquid Chromatography analysis coupled with Mass Spectrometry (LC/MS/MS) is the most method commonly used for this type of analysis. The parameters are optimized and validated to ensure the best conditions for the environmental analysis. This technique was applied to real samples of wastewater treatment plants in Bordeaux and Lebanon. (author)
An entropy flow optimization technique for helium liquefaction cycles
International Nuclear Information System (INIS)
Minta, M.; Smith, J.L.
1984-01-01
This chapter proposes a new method of analyzing thermodynamic cycles based on a continuous distribution of precooling over the temperature range of the cycle. The method gives the optimum distribution of precooling over the temperature range of the cycle by specifying the mass flow to be expanded at each temperature. The result is used to select a cycle configuration with discrete expansions and to initialize the independent variables for final optimization. Topics considered include the continuous precooling model, the results for ideal gas, the results for real gas, and the application to the design of a saturated vapor compression (SVC) cycle. The optimization technique for helium liquefaction cycles starts with the minimization of the generated entropy in a cycle model with continuous precooling. The pressure ratio, the pressure level and the distribution of the heat exchange are selected based on the results of the continuous precooling analysis. It is concluded that the technique incorporates the non-ideal behavior of helium in the procedure and allows the trade-off between heat exchange area and losses to be determined
Using simulation-optimization techniques to improve multiphase aquifer remediation
Energy Technology Data Exchange (ETDEWEB)
Finsterle, S.; Pruess, K. [Lawrence Berkeley Laboratory, Berkeley, CA (United States)
1995-03-01
The T2VOC computer model for simulating the transport of organic chemical contaminants in non-isothermal multiphase systems has been coupled to the ITOUGH2 code which solves parameter optimization problems. This allows one to use linear programming and simulated annealing techniques to solve groundwater management problems, i.e. the optimization of operations for multiphase aquifer remediation. A cost function has to be defined, containing the actual and hypothetical expenses of a cleanup operation which depend - directly or indirectly - on the state variables calculated by T2VOC. Subsequently, the code iteratively determines a remediation strategy (e.g. pumping schedule) which minimizes, for instance, pumping and energy costs, the time for cleanup, and residual contamination. We discuss an illustrative sample problem to discuss potential applications of the code. The study shows that the techniques developed for estimating model parameters can be successfully applied to the solution of remediation management problems. The resulting optimum pumping scheme depends, however, on the formulation of the remediation goals and the relative weighting between individual terms of the cost function.
Optimized evaporation technique for leachate treatment: Small scale implementation.
Benyoucef, Fatima; Makan, Abdelhadi; El Ghmari, Abderrahman; Ouatmane, Aziz
2016-04-01
This paper introduces an optimized evaporation technique for leachate treatment. For this purpose and in order to study the feasibility and measure the effectiveness of the forced evaporation, three cuboidal steel tubs were designed and implemented. The first control-tub was installed at the ground level to monitor natural evaporation. Similarly, the second and the third tub, models under investigation, were installed respectively at the ground level (equipped-tub 1) and out of the ground level (equipped-tub 2), and provided with special equipment to accelerate the evaporation process. The obtained results showed that the evaporation rate at the equipped-tubs was much accelerated with respect to the control-tub. It was accelerated five times in the winter period, where the evaporation rate was increased from a value of 0.37 mm/day to reach a value of 1.50 mm/day. In the summer period, the evaporation rate was accelerated more than three times and it increased from a value of 3.06 mm/day to reach a value of 10.25 mm/day. Overall, the optimized evaporation technique can be applied effectively either under electric or solar energy supply, and will accelerate the evaporation rate from three to five times whatever the season temperature. Copyright © 2016. Published by Elsevier Ltd.
A comparison of Heuristic method and Llewellyn’s rules for identification of redundant constraints
Estiningsih, Y.; Farikhin; Tjahjana, R. H.
2018-03-01
Important techniques in linear programming is modelling and solving practical optimization. Redundant constraints are consider for their effects on general linear programming problems. Identification and reduce redundant constraints are for avoidance of all the calculations associated when solving an associated linear programming problems. Many researchers have been proposed for identification redundant constraints. This paper a compararison of Heuristic method and Llewellyn’s rules for identification of redundant constraints.
Directory of Open Access Journals (Sweden)
I. Nayak
2017-06-01
Full Text Available In the present research work, four different multi response optimization techniques, viz. multiple response signal-to-noise (MRSN ratio, weighted signal-to-noise (WSN ratio, Grey relational analysis (GRA and VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian methods have been used to optimize the electro-discharge machining (EDM performance characteristics such as material removal rate (MRR, tool wear rate (TWR and surface roughness (SR simultaneously. Experiments have been planned on a D2 steel specimen based on L9 orthogonal array. Experimental results are analyzed using the standard procedure. The optimum level combinations of input process parameters such as voltage, current, pulse-on-time and pulse-off-time, and percentage contributions of each process parameter using ANOVA technique have been determined. Different correlations have been developed between the various input process parameters and output performance characteristics. Finally, the optimum performances of these four methods are compared and the results show that WSN ratio method is the best multiresponse optimization technique for this process. From the analysis, it is also found that the current has the maximum effect on the overall performance of EDM operation as compared to other process parameters.
Using tree diversity to compare phylogenetic heuristics.
Sul, Seung-Jin; Matthews, Suzanne; Williams, Tiffani L
2009-04-29
Evolutionary trees are family trees that represent the relationships between a group of organisms. Phylogenetic heuristics are used to search stochastically for the best-scoring trees in tree space. Given that better tree scores are believed to be better approximations of the true phylogeny, traditional evaluation techniques have used tree scores to determine the heuristics that find the best scores in the fastest time. We develop new techniques to evaluate phylogenetic heuristics based on both tree scores and topologies to compare Pauprat and Rec-I-DCM3, two popular Maximum Parsimony search algorithms. Our results show that although Pauprat and Rec-I-DCM3 find the trees with the same best scores, topologically these trees are quite different. Furthermore, the Rec-I-DCM3 trees cluster distinctly from the Pauprat trees. In addition to our heatmap visualizations of using parsimony scores and the Robinson-Foulds distance to compare best-scoring trees found by the two heuristics, we also develop entropy-based methods to show the diversity of the trees found. Overall, Pauprat identifies more diverse trees than Rec-I-DCM3. Overall, our work shows that there is value to comparing heuristics beyond the parsimony scores that they find. Pauprat is a slower heuristic than Rec-I-DCM3. However, our work shows that there is tremendous value in using Pauprat to reconstruct trees-especially since it finds identical scoring but topologically distinct trees. Hence, instead of discounting Pauprat, effort should go in improving its implementation. Ultimately, improved performance measures lead to better phylogenetic heuristics and will result in better approximations of the true evolutionary history of the organisms of interest.
Teo, Jing Chun; Foin, Nicolas; Otsuka, Fumiyuki; Bulluck, Heerajnarain; Fam, Jiang Ming; Wong, Philip; Low, Fatt Hoe; Leo, Hwa Liang; Mari, Jean-Martial; Joner, Michael; Girard, Michael J A; Virmani, Renu; Bezerra, HG.; Costa, MA.; Guagliumi, G.; Rollins, AM.; Simon, D.; Gutiérrez-Chico, JL.; Alegría-Barrero, E.; Teijeiro-Mestre, R.; Chan, PH.; Tsujioka, H.; de Silva, R.; Otsuka, F.; Joner, M.; Prati, F.; Virmani, R.; Narula, J.; Members, WC.; Levine, GN.; Bates, ER.; Blankenship, JC.; Bailey, SR.; Bittl, JA.; Prati, F.; Guagliumi, G.; Mintz, G.S.; Costa, Marco; Regar, E.; Akasaka, T.; Roleder, T.; Jąkała, J.; Kałuża, GL.; Partyka, Ł.; Proniewska, K.; Pociask, E.; Girard, MJA.; Strouthidis, NG.; Ethier, CR.; Mari, JM.; Mari, JM.; Strouthidis, NG.; Park, SC.; Girard, MJA.; van der Lee, R.; Foin, N.; Otsuka, F.; Wong, P.K.; Mari, J-M.; Joner, M.; Nakano, M.; Vorpahl, M.; Otsuka, F.; Taniwaki, M.; Yazdani, SK.; Finn, AV.; Nakano, M.; Yahagi, K.; Yamamoto, H.; Taniwaki, M.; Otsuka, F.; Ladich, ER.; Girard, MJ.; Ang, M.; Chung, CW.; Farook, M.; Strouthidis, N.; Mehta, JS.; Foin, N.; Mari, JM.; Nijjer, S.; Sen, S.; Petraco, R.; Ghione, M.; Liu, X.; Kang, JU.; Virmani, R.; Kolodgie, F.D.; Burke, AP.; Farb, A.; Schwartz, S.M.; Yahagi, K.; Kolodgie, F.D.; Otsuka, F.; Finn, AV.; Davis, HR.; Joner, M.; Kume, T.; Akasaka, T.; Kawamoto, T.; Watanabe, N.; Toyota, E.; Neishi, Y.; Rieber, J.; Meissner, O.; Babaryka, G.; Reim, S.; Oswald, M.E.; Koenig, A.S.; Tearney, G. J.; Regar, E.; Akasaka, T.; Adriaenssens, T.; Barlis, P.; Bezerra, HG.; Yabushita, H.; Bouma, BE.; Houser, S. L.; Aretz, HT.; Jang, I-K.; Schlendorf, KH.; Guo, J.; Sun, L.; Chen, Y.D.; Tian, F.; Liu, HB.; Chen, L.; Kawasaki, M.; Bouma, BE.; Bressner, J. E.; Houser, S. L.; Nadkarni, S. K.; MacNeill, BD.; Jansen, CHP.; Onthank, DC.; Cuello, F.; Botnar, RM.; Wiethoff, AJ.; Warley, A.; von Birgelen, C.; Hartmann, A. M.; Kubo, T.; Akasaka, T.; Shite, J.; Suzuki, T.; Uemura, S.; Yu, B.; Habara, M.; Nasu, K.; Terashima, M.; Kaneda, H.; Yokota, D.; Ko, E.; Virmani, R.; Burke, AP.; Kolodgie, F.D.; Farb, A.; Takarada, S.; Imanishi, T.; Kubo, T.; Tanimoto, T.; Kitabata, H.; Nakamura, N.; Hattori, K.; Ozaki, Y.; Ismail, TF.; Okumura, M.; Naruse, H.; Kan, S.; Nishio, R.; Shinke, T.; Otake, H.; Nakagawa, M.; Nagoshi, R.; Inoue, T.; Sinclair, H.D.; Bourantas, C.; Bagnall, A.; Mintz, G.S.; Kunadian, V.; Tearney, G. J.; Yabushita, H.; Houser, S. L.; Aretz, HT.; Jang, I-K.; Schlendorf, KH.; van Soest, G.; Goderie, T.; Regar, E.; Koljenović, S.; Leenders, GL. van; Gonzalo, N.; Xu, C.; Schmitt, JM.; Carlier, SG.; Virmani, R.; van der Meer, FJ; Faber, D.J.; Sassoon, DMB.; Aalders, M.C.; Pasterkamp, G.; Leeuwen, TG. van; Schmitt, JM.; Knuttel, A.; Yadlowsky, M.; Eckhaus, MA.; Karamata, B.; Laubscher, M.; Leutenegger, M.; Bourquin, S.; Lasser, T.; Lambelet, P.; Vermeer, K.A.; Mo, J.; Weda, J.J.A.; Lemij, H.G.; Boer, JF. de
2016-01-01
PURPOSE To optimize conventional coronary optical coherence tomography (OCT) images using the attenuation-compensated technique to improve identification of plaques and the external elastic lamina (EEL) contour. METHOD The attenuation-compensated technique was optimized via manipulating contrast
International Nuclear Information System (INIS)
Sugny, D.; Bomble, L.; Ribeyre, T.; Dulieu, O.; Desouter-Lecomte, M.
2009-01-01
Implementation of quantum controlled-NOT (CNOT) gates in realistic molecular systems is studied using stimulated Raman adiabatic passage (STIRAP) techniques optimized in the time domain by genetic algorithms or coupled with optimal control theory. In the first case, with an adiabatic solution (a series of STIRAP processes) as starting point, we optimize in the time domain different parameters of the pulses to obtain a high fidelity in two realistic cases under consideration. A two-qubit CNOT gate constructed from different assignments in rovibrational states is considered in diatomic (NaCs) or polyatomic (SCCl 2 ) molecules. The difficulty of encoding logical states in pure rotational states with STIRAP processes is illustrated. In such circumstances, the gate can be implemented by optimal control theory and the STIRAP sequence can then be used as an interesting trial field. We discuss the relative merits of the two methods for rovibrational computing (structure of the control field, duration of the control, and efficiency of the optimization).
Airfoil shape optimization using non-traditional optimization technique and its validation
Directory of Open Access Journals (Sweden)
R. Mukesh
2014-07-01
Full Text Available Computational fluid dynamics (CFD is one of the computer-based solution methods which is more widely employed in aerospace engineering. The computational power and time required to carry out the analysis increase as the fidelity of the analysis increases. Aerodynamic shape optimization has become a vital part of aircraft design in the recent years. Generally if we want to optimize an airfoil we have to describe the airfoil and for that, we need to have at least hundred points of x and y co-ordinates. It is really difficult to optimize airfoils with this large number of co-ordinates. Nowadays many different schemes of parameter sets are used to describe general airfoil such as B-spline, and PARSEC. The main goal of these parameterization schemes is to reduce the number of needed parameters as few as possible while controlling the important aerodynamic features effectively. Here the work has been done on the PARSEC geometry representation method. The objective of this work is to introduce the knowledge of describing general airfoil using twelve parameters by representing its shape as a polynomial function. And also we have introduced the concept of Genetic Algorithm to optimize the aerodynamic characteristics of a general airfoil for specific conditions. A MATLAB program has been developed to implement PARSEC, Panel Technique, and Genetic Algorithm. This program has been tested for a standard NACA 2411 airfoil and optimized to improve its coefficient of lift. Pressure distribution and co-efficient of lift for airfoil geometries have been calculated using the Panel method. The optimized airfoil has improved co-efficient of lift compared to the original one. The optimized airfoil is validated using wind tunnel data.
Essays on variational approximation techniques for stochastic optimization problems
Deride Silva, Julio A.
This dissertation presents five essays on approximation and modeling techniques, based on variational analysis, applied to stochastic optimization problems. It is divided into two parts, where the first is devoted to equilibrium problems and maxinf optimization, and the second corresponds to two essays in statistics and uncertainty modeling. Stochastic optimization lies at the core of this research as we were interested in relevant equilibrium applications that contain an uncertain component, and the design of a solution strategy. In addition, every stochastic optimization problem relies heavily on the underlying probability distribution that models the uncertainty. We studied these distributions, in particular, their design process and theoretical properties such as their convergence. Finally, the last aspect of stochastic optimization that we covered is the scenario creation problem, in which we described a procedure based on a probabilistic model to create scenarios for the applied problem of power estimation of renewable energies. In the first part, Equilibrium problems and maxinf optimization, we considered three Walrasian equilibrium problems: from economics, we studied a stochastic general equilibrium problem in a pure exchange economy, described in Chapter 3, and a stochastic general equilibrium with financial contracts, in Chapter 4; finally from engineering, we studied an infrastructure planning problem in Chapter 5. We stated these problems as belonging to the maxinf optimization class and, in each instance, we provided an approximation scheme based on the notion of lopsided convergence and non-concave duality. This strategy is the foundation of the augmented Walrasian algorithm, whose convergence is guaranteed by lopsided convergence, that was implemented computationally, obtaining numerical results for relevant examples. The second part, Essays about statistics and uncertainty modeling, contains two essays covering a convergence problem for a sequence
Babaveisi, Vahid; Paydar, Mohammad Mahdi; Safaei, Abdul Sattar
2017-07-01
This study aims to discuss the solution methodology for a closed-loop supply chain (CLSC) network that includes the collection of used products as well as distribution of the new products. This supply chain is presented on behalf of the problems that can be solved by the proposed meta-heuristic algorithms. A mathematical model is designed for a CLSC that involves three objective functions of maximizing the profit, minimizing the total risk and shortages of products. Since three objective functions are considered, a multi-objective solution methodology can be advantageous. Therefore, several approaches have been studied and an NSGA-II algorithm is first utilized, and then the results are validated using an MOSA and MOPSO algorithms. Priority-based encoding, which is used in all the algorithms, is the core of the solution computations. To compare the performance of the meta-heuristics, random numerical instances are evaluated by four criteria involving mean ideal distance, spread of non-dominance solution, the number of Pareto solutions, and CPU time. In order to enhance the performance of the algorithms, Taguchi method is used for parameter tuning. Finally, sensitivity analyses are performed and the computational results are presented based on the sensitivity analyses in parameter tuning.
Methods and techniques of nuclear in-core fuel management
International Nuclear Information System (INIS)
Jong, A.J. de.
1992-04-01
Review of methods of nuclear in-core fuel management (the minimal critical mass problem, minimal power peaking) and calculational techniques: reactorphysical calculations (point reactivity models, continuous refueling, empirical methods, depletion perturbation theory, nodal computer programs); optimization techniques (stochastic search, linear programming, heuristic parameter optimization). (orig./HP)
Techniques for optimizing nanotips derived from frozen taylor cones
Hirsch, Gregory
2017-12-05
Optimization techniques are disclosed for producing sharp and stable tips/nanotips relying on liquid Taylor cones created from electrically conductive materials with high melting points. A wire substrate of such a material with a preform end in the shape of a regular or concave cone, is first melted with a focused laser beam. Under the influence of a high positive potential, a Taylor cone in a liquid/molten state is formed at that end. The cone is then quenched upon cessation of the laser power, thus freezing the Taylor cone. The tip of the frozen Taylor cone is reheated by the laser to allow its precise localized melting and shaping. Tips thus obtained yield desirable end-forms suitable as electron field emission sources for a variety of applications. In-situ regeneration of the tip is readily accomplished. These tips can also be employed as regenerable bright ion sources using field ionization/desorption of introduced chemical species.
Neoliberal Optimism: Applying Market Techniques to Global Health.
Mei, Yuyang
2017-01-01
Global health and neoliberalism are becoming increasingly intertwined as organizations utilize markets and profit motives to solve the traditional problems of poverty and population health. I use field work conducted over 14 months in a global health technology company to explore how the promise of neoliberalism re-envisions humanitarian efforts. In this company's vaccine refrigerator project, staff members expect their investors and their market to allow them to achieve scale and develop accountability to their users in developing countries. However, the translation of neoliberal techniques to the global health sphere falls short of the ideal, as profits are meager and purchasing power remains with donor organizations. The continued optimism in market principles amidst such a non-ideal market reveals the tenacious ideological commitment to neoliberalism in these global health projects.
Multi-Material Design Optimization of Composite Structures
DEFF Research Database (Denmark)
Hvejsel, Christian Frier
properties. The modeling encompasses discrete orientationing of orthotropic materials, selection between different distinct materials as well as removal of material representing holes in the structure within a unified parametrization. The direct generalization of two-phase topology optimization to any number...... of a relaxation-based search heuristic that accelerates a Generalized Benders' Decomposition technique for global optimization and enables the solution of medium-scale problems to global optimality. Improvements in the ability to solve larger problems to global optimality are found and potentially further...... improvements may be obtained with this technique in combination with cheaper heuristics....
Optimization Techniques for Dimensionally Truncated Sparse Grids on Heterogeneous Systems
Deftu, A.
2013-02-01
Given the existing heterogeneous processor landscape dominated by CPUs and GPUs, topics such as programming productivity and performance portability have become increasingly important. In this context, an important question refers to how can we develop optimization strategies that cover both CPUs and GPUs. We answer this for fastsg, a library that provides functionality for handling efficiently high-dimensional functions. As it can be employed for compressing and decompressing large-scale simulation data, it finds itself at the core of a computational steering application which serves us as test case. We describe our experience with implementing fastsg\\'s time critical routines for Intel CPUs and Nvidia Fermi GPUs. We show the differences and especially the similarities between our optimization strategies for the two architectures. With regard to our test case for which achieving high speedups is a "must" for real-time visualization, we report a speedup of up to 6.2x times compared to the state-of-the-art implementation of the sparse grid technique for GPUs. © 2013 IEEE.
Heuristics for no-wait flow shop scheduling problem
Directory of Open Access Journals (Sweden)
Kewal Krishan Nailwal
2016-09-01
Full Text Available No-wait flow shop scheduling refers to continuous flow of jobs through different machines. The job once started should have the continuous processing through the machines without wait. This situation occurs when there is a lack of an intermediate storage between the processing of jobs on two consecutive machines. The problem of no-wait with the objective of minimizing makespan in flow shop scheduling is NP-hard; therefore the heuristic algorithms are the key to solve the problem with optimal solution or to approach nearer to optimal solution in simple manner. The paper describes two heuristics, one constructive and an improvement heuristic algorithm obtained by modifying the constructive one for sequencing n-jobs through m-machines in a flow shop under no-wait constraint with the objective of minimizing makespan. The efficiency of the proposed heuristic algorithms is tested on 120 Taillard’s benchmark problems found in the literature against the NEH under no-wait and the MNEH heuristic for no-wait flow shop problem. The improvement heuristic outperforms all heuristics on the Taillard’s instances by improving the results of NEH by 27.85%, MNEH by 22.56% and that of the proposed constructive heuristic algorithm by 24.68%. To explain the computational process of the proposed algorithm, numerical illustrations are also given in the paper. Statistical tests of significance are done in order to draw the conclusions.
Directory of Open Access Journals (Sweden)
Po-Chen Cheng
2015-06-01
Full Text Available In this paper, an asymmetrical fuzzy-logic-control (FLC-based maximum power point tracking (MPPT algorithm for photovoltaic (PV systems is presented. Two membership function (MF design methodologies that can improve the effectiveness of the proposed asymmetrical FLC-based MPPT methods are then proposed. The first method can quickly determine the input MF setting values via the power–voltage (P–V curve of solar cells under standard test conditions (STC. The second method uses the particle swarm optimization (PSO technique to optimize the input MF setting values. Because the PSO approach must target and optimize a cost function, a cost function design methodology that meets the performance requirements of practical photovoltaic generation systems (PGSs is also proposed. According to the simulated and experimental results, the proposed asymmetrical FLC-based MPPT method has the highest fitness value, therefore, it can successfully address the tracking speed/tracking accuracy dilemma compared with the traditional perturb and observe (P&O and symmetrical FLC-based MPPT algorithms. Compared to the conventional FLC-based MPPT method, the obtained optimal asymmetrical FLC-based MPPT can improve the transient time and the MPPT tracking accuracy by 25.8% and 0.98% under STC, respectively.
Directory of Open Access Journals (Sweden)
Hediyeh Karimi
2013-01-01
Full Text Available It has been predicted that the nanomaterials of graphene will be among the candidate materials for postsilicon electronics due to their astonishing properties such as high carrier mobility, thermal conductivity, and biocompatibility. Graphene is a semimetal zero gap nanomaterial with demonstrated ability to be employed as an excellent candidate for DNA sensing. Graphene-based DNA sensors have been used to detect the DNA adsorption to examine a DNA concentration in an analyte solution. In particular, there is an essential need for developing the cost-effective DNA sensors holding the fact that it is suitable for the diagnosis of genetic or pathogenic diseases. In this paper, particle swarm optimization technique is employed to optimize the analytical model of a graphene-based DNA sensor which is used for electrical detection of DNA molecules. The results are reported for 5 different concentrations, covering a range from 0.01 nM to 500 nM. The comparison of the optimized model with the experimental data shows an accuracy of more than 95% which verifies that the optimized model is reliable for being used in any application of the graphene-based DNA sensor.
Optimal technique for deep breathing exercises after cardiac surgery.
Westerdahl, E
2015-06-01
Cardiac surgery patients often develop a restrictive pulmonary impairment and gas exchange abnormalities in the early postoperative period. Chest physiotherapy is routinely prescribed in order to reduce or prevent these complications. Besides early mobilization, positioning and shoulder girdle exercises, various breathing exercises have been implemented as a major component of postoperative care. A variety of deep breathing maneuvres are recommended to the spontaneously breathing patient to reduce atelectasis and to improve lung function in the early postoperative period. Different breathing exercises are recommended in different parts of the world, and there is no consensus about the most effective breathing technique after cardiac surgery. Arbitrary instructions are given, and recommendations on performance and duration vary between hospitals. Deep breathing exercises are a major part of this therapy, but scientific evidence for the efficacy has been lacking until recently, and there is a lack of trials describing how postoperative breathing exercises actually should be performed. The purpose of this review is to provide a brief overview of postoperative breathing exercises for patients undergoing cardiac surgery via sternotomy, and to discuss and suggest an optimal technique for the performance of deep breathing exercises.
A Monte Carlo simulation technique to determine the optimal portfolio
Directory of Open Access Journals (Sweden)
Hassan Ghodrati
2014-03-01
Full Text Available During the past few years, there have been several studies for portfolio management. One of the primary concerns on any stock market is to detect the risk associated with various assets. One of the recognized methods in order to measure, to forecast, and to manage the existing risk is associated with Value at Risk (VaR, which draws much attention by financial institutions in recent years. VaR is a method for recognizing and evaluating of risk, which uses the standard statistical techniques and the method has been used in other fields, increasingly. The present study has measured the value at risk of 26 companies from chemical industry in Tehran Stock Exchange over the period 2009-2011 using the simulation technique of Monte Carlo with 95% confidence level. The used variability in the present study has been the daily return resulted from the stock daily price change. Moreover, the weight of optimal investment has been determined using a hybrid model called Markowitz and Winker model in each determined stocks. The results showed that the maximum loss would not exceed from 1259432 Rials at 95% confidence level in future day.
Local search heuristics for the probabilistic dial-a-ride problem
DEFF Research Database (Denmark)
Ho, Sin C.; Haugland, Dag
2011-01-01
evaluation procedure in a pure local search heuristic and in a tabu search heuristic. The quality of the solutions obtained by the two heuristics have been compared experimentally. Computational results confirm that our neighborhood evaluation technique is much faster than the straightforward one...
Solution quality improvement in chiller loading optimization
International Nuclear Information System (INIS)
Geem, Zong Woo
2011-01-01
In order to reduce greenhouse gas emission, we can energy-efficiently operate a multiple chiller system using optimization techniques. So far, various optimization techniques have been proposed to the optimal chiller loading problem. Most of those techniques are meta-heuristic algorithms such as genetic algorithm, simulated annealing, and particle swarm optimization. However, this study applied a gradient-based method, named generalized reduced gradient, and then obtains better results when compared with other approaches. When two additional approaches (hybridization between meta-heuristic algorithm and gradient-based algorithm; and reformulation of optimization structure by adding a binary variable which denotes chiller's operating status) were introduced, generalized reduced gradient found even better solutions. - Highlights: → Chiller loading problem is optimized by generalized reduced gradient (GRG) method. → Results are compared with meta-heuristic algorithms such as genetic algorithm. → Results are even enhanced by hybridizing meta-heuristic and gradient techniques. → Results are even enhanced by modifying the optimization formulation.
Conspicuous Waste and Representativeness Heuristic
Directory of Open Access Journals (Sweden)
Tatiana M. Shishkina
2017-12-01
Full Text Available The article deals with the similarities between conspicuous waste and representativeness heuristic. The conspicuous waste is analyzed according to the classic Veblen’ interpretation as a strategy to increase social status through conspicuous consumption and conspicuous leisure. In “The Theory of the Leisure Class” Veblen introduced two different types of utility – conspicuous and functional. The article focuses on the possible benefits of the analysis of conspicuous utility not only in terms of institutional economic theory, but also in terms of behavioral economics. To this end, the representativeness heuristics is considered, on the one hand, as a way to optimize the decision-making process, which allows to examine it in comparison with procedural rationality by Simon. On the other hand, it is also analyzed as cognitive bias within the Kahneman and Twersky’ approach. The article provides the analysis of the patterns in the deviations from the rational behavior strategy that could be observed in case of conspicuous waste both in modern market economies in the form of conspicuous consumption and in archaic economies in the form of gift-exchange. The article also focuses on the marketing strategies for luxury consumption’ advertisement. It highlights the impact of the symbolic capital (in Bourdieu’ interpretation on the social and symbolic payments that actors get from the act of conspicuous waste. This allows to perform a analysis of conspicuous consumption both as a rational way to get the particular kind of payments, and, at the same time, as a form of institutionalized cognitive bias.
Efficient heuristics for maximum common substructure search.
Englert, Péter; Kovács, Péter
2015-05-26
Maximum common substructure search is a computationally hard optimization problem with diverse applications in the field of cheminformatics, including similarity search, lead optimization, molecule alignment, and clustering. Most of these applications have strict constraints on running time, so heuristic methods are often preferred. However, the development of an algorithm that is both fast enough and accurate enough for most practical purposes is still a challenge. Moreover, in some applications, the quality of a common substructure depends not only on its size but also on various topological features of the one-to-one atom correspondence it defines. Two state-of-the-art heuristic algorithms for finding maximum common substructures have been implemented at ChemAxon Ltd., and effective heuristics have been developed to improve both their efficiency and the relevance of the atom mappings they provide. The implementations have been thoroughly evaluated and compared with existing solutions (KCOMBU and Indigo). The heuristics have been found to greatly improve the performance and applicability of the algorithms. The purpose of this paper is to introduce the applied methods and present the experimental results.
Use of advanced modeling techniques to optimize thermal packaging designs.
Formato, Richard M; Potami, Raffaele; Ahmed, Iftekhar
2010-01-01
Through a detailed case study the authors demonstrate, for the first time, the capability of using advanced modeling techniques to correctly simulate the transient temperature response of a convective flow-based thermal shipper design. The objective of this case study was to demonstrate that simulation could be utilized to design a 2-inch-wall polyurethane (PUR) shipper to hold its product box temperature between 2 and 8 °C over the prescribed 96-h summer profile (product box is the portion of the shipper that is occupied by the payload). Results obtained from numerical simulation are in excellent agreement with empirical chamber data (within ±1 °C at all times), and geometrical locations of simulation maximum and minimum temperature match well with the corresponding chamber temperature measurements. Furthermore, a control simulation test case was run (results taken from identical product box locations) to compare the coupled conduction-convection model with a conduction-only model, which to date has been the state-of-the-art method. For the conduction-only simulation, all fluid elements were replaced with "solid" elements of identical size and assigned thermal properties of air. While results from the coupled thermal/fluid model closely correlated with the empirical data (±1 °C), the conduction-only model was unable to correctly capture the payload temperature trends, showing a sizeable error compared to empirical values (ΔT > 6 °C). A modeling technique capable of correctly capturing the thermal behavior of passively refrigerated shippers can be used to quickly evaluate and optimize new packaging designs. Such a capability provides a means to reduce the cost and required design time of shippers while simultaneously improving their performance. Another advantage comes from using thermal modeling (assuming a validated model is available) to predict the temperature distribution in a shipper that is exposed to ambient temperatures which were not bracketed
Directory of Open Access Journals (Sweden)
A. Baskar
2016-04-01
Full Text Available Permutation flow shop scheduling problems have been an interesting area of research for over six decades. Out of the several parameters, minimization of makespan has been studied much over the years. The problems are widely regarded as NP-Complete if the number of machines is more than three. As the computation time grows exponentially with respect to the problem size, heuristics and meta-heuristics have been proposed by many authors that give reasonably accurate and acceptable results. The NEH algorithm proposed in 1983 is still considered as one of the best simple, constructive heuristics for the minimization of makespan. This paper analyses the powerful job insertion technique used by NEH algorithm and proposes seven new variants, the complexity level remains same. 120 numbers of problem instances proposed by Taillard have been used for the purpose of validating the algorithms. Out of the seven, three produce better results than the original NEH algorithm.
Optimized inspection techniques and structural analysis in lifetime management
International Nuclear Information System (INIS)
Aguado, M.T.; Marcelles, I.
1993-01-01
Preservation of the option of extending the service lifetime of a nuclear power plant beyond its normal design lifetime requires correct remaining lifetime management from the very beginning of plant operation. The methodology used in plant remaining lifetime management is essentially based on the use of standard inspections, surveillance and monitoring programs and calculations, such as thermal-stress and fracture mechanics analysis. The inspection techniques should be continuously optimized, in order to be able to detect and dimension existing defects with the highest possible degree of accuracy. The information obtained during the inspection is combined with the historical data of the components: design, quality, operation, maintenance, and transients, and with the results of destructive testing, fracture mechanics and thermal fatigue analysis. These data are used to estimate the remaining lifetime of nuclear power plant components, systems and structures with the highest degree possible of accuracy. The use of this methodology allows component repairs and replacements to be reduced or avoided and increases the safety levels and availability of the nuclear power plant. Use of this strategy avoids the need for heavy investments at the end of the licensing period
Muscle optimization techniques impact the magnitude of calculated hip joint contact forces
Wesseling, M.; Derikx, L.C.; de Groote, F.; Bartels, W.; Meyer, C.; Verdonschot, Nicolaas Jacobus Joseph; Jonkers, I.
2015-01-01
In musculoskeletal modelling, several optimization techniques are used to calculate muscle forces, which strongly influence resultant hip contact forces (HCF). The goal of this study was to calculate muscle forces using four different optimization techniques, i.e., two different static optimization
Directory of Open Access Journals (Sweden)
Viktor Ivanovich Petrov
2017-01-01
Full Text Available The article considers the issues of civil aviation aircraft onboard computers data safety. Infor- mation security undeclared capabilities stand for technical equipment or software possibilities, which are not mentioned in the documentation. Documentation and tests content requirements are imposed during the software certification. Documentation requirements include documents composition and content of control (specification, description and program code, the source code. Test requirements include: static analysis of program codes (including the compliance of the sources with their loading modules monitoring; dynamic analysis of source code (including implementation of routes monitor- ing. Currently, there are no complex measures for checking onboard computer software. There are no rules and regulations that can allow controlling foreign production aircraft software, and the actual receiving of software is difficult. Consequently, the author suggests developing the basics of aviation rules and regulations, which allow to analyze the programs of CA aircraft onboard computers. If there are no software source codes the two approaches of code analysis are used: a structural static and dy- namic analysis of the source code; signature-heuristic analysis of potentially dangerous operations. Static analysis determines the behavior of the program by reading the program code (without running the program which is represented in the assembler language - disassembly listing. Program tracing is performed by the dynamic analysis. The analysis of aircraft software ability to detect undeclared capa- bilities using the interactive disassembler was considered in this article.
Heuristic decision making in medicine
Marewski, Julian N.; Gigerenzer, Gerd
2012-01-01
Can less information be more helpful when it comes to making medical decisions? Contrary to the common intuition that more information is always better, the use of heuristics can help both physicians and patients to make sound decisions. Heuristics are simple decision strategies that ignore part of the available information, basing decisions on only a few relevant predictors. We discuss: (i) how doctors and patients use heuristics; and (ii) when heuristics outperform information-greedy methods, such as regressions in medical diagnosis. Furthermore, we outline those features of heuristics that make them useful in health care settings. These features include their surprising accuracy, transparency, and wide accessibility, as well as the low costs and little time required to employ them. We close by explaining one of the statistical reasons why heuristics are accurate, and by pointing to psychiatry as one area for future research on heuristics in health care. PMID:22577307
Heuristic decision making in medicine.
Marewski, Julian N; Gigerenzer, Gerd
2012-03-01
Can less information be more helpful when it comes to making medical decisions? Contrary to the common intuition that more information is always better, the use of heuristics can help both physicians and patients to make sound decisions. Heuristics are simple decision strategies that ignore part of the available information, basing decisions on only a few relevant predictors. We discuss: (i) how doctors and patients use heuristics; and (ii) when heuristics outperform information-greedy methods, such as regressions in medical diagnosis. Furthermore, we outline those features of heuristics that make them useful in health care settings. These features include their surprising accuracy, transparency, and wide accessibility, as well as the low costs and little time required to employ them. We close by explaining one of the statistical reasons why heuristics are accurate, and by pointing to psychiatry as one area for future research on heuristics in health care.
DEFF Research Database (Denmark)
Larsen, Anders Astrup; Bendsøe, Martin P.; Schmidt, Henrik Nikolaj Blicher
2007-01-01
The aim of this paper is to optimize a thermal model of a friction stir welding process. The optimization is performed using a space mapping technique in which an analytical model is used along with the FEM model to be optimized. The results are compared to traditional gradient based optimization...
A Meta-Heuristic Regression-Based Feature Selection for Predictive Analytics
Directory of Open Access Journals (Sweden)
Bharat Singh
2014-11-01
Full Text Available A high-dimensional feature selection having a very large number of features with an optimal feature subset is an NP-complete problem. Because conventional optimization techniques are unable to tackle large-scale feature selection problems, meta-heuristic algorithms are widely used. In this paper, we propose a particle swarm optimization technique while utilizing regression techniques for feature selection. We then use the selected features to classify the data. Classification accuracy is used as a criterion to evaluate classifier performance, and classification is accomplished through the use of k-nearest neighbour (KNN and Bayesian techniques. Various high dimensional data sets are used to evaluate the usefulness of the proposed approach. Results show that our approach gives better results when compared with other conventional feature selection algorithms.
Heuristic Diagrams as a Tool to Teach History of Science
Chamizo, José A.
2012-05-01
The graphic organizer called here heuristic diagram as an improvement of Gowin's Vee heuristic is proposed as a tool to teach history of science. Heuristic diagrams have the purpose of helping students (or teachers, or researchers) to understand their own research considering that asks and problem-solving are central to scientific activity. The left side originally related in Gowin's Vee with philosophies, theories, models, laws or regularities now agrees with Toulmin's concepts (language, models as representation techniques and application procedures). Mexican science teachers without experience in science education research used the heuristic diagram to learn about the history of chemistry considering also in the left side two different historical times: past and present. Through a semantic differential scale teachers' attitude to the heuristic diagram was evaluated and its usefulness was demonstrated.
Directory of Open Access Journals (Sweden)
Mario C Vélez Gallego
2012-04-01
Full Text Available A discrete event simulation-optimization heuristic is presented for configuring a selective pallet rack system. To the rack system under study pallets arrive dynamically over time to be stored for a given period of time. The storage duration, arrival time and pallet height are assumed to be continuous random variables with known density functions. In such a system, if there is no available slot in the rack to store an arriving pallet, it is placed on the floor. The proposed heuristic aims at minimizing the number of racking banks needed so that the long term proportion of pallets that are placed in a rack slot reaches a minimum pre-specified value. The computational experiments conducted suggest that the proposed heuristic effectively solves the problem under study.En este artículo se presenta un heurístico de simulación-optimización para la configuración de un sistema de almacenamiento en estantería selectiva simple. Al sistema de almacenamiento objeto del estudio llegan pallets dinámicamente en el tiempo para ser almacenados por un periodo de tiempo. Se asume que la duración del almacenamiento, el instante de llegada y la altura de los pallets son variables aleatorias continuas con funciones de densidad conocidas. En este sistema, si al momento de llegada de un pallet no hay disponible una posición en la estantería en la que éste se pueda almacenar, el pallet se almacena en el suelo. El heurístico propuesto busca minimizar en el número de módulos de estantería necesarios de tal manera que en el largo plazo la proporción de pallets ubicados en la estantería alcance un valor mínimo preestablecido. Los experimentos computacionales llevados a cabo sugieren que el heurístico propuesto es efectivo para resolver el problema en estudio.
Intelligent System Design Using Hyper-Heuristics
Directory of Open Access Journals (Sweden)
Nelishia Pillay
2015-07-01
Full Text Available Determining the most appropriate search method or artificial intelligence technique to solve a problem is not always evident and usually requires implementation of the different approaches to ascertain this. In some instances a single approach may not be sufficient and hybridization of methods may be needed to find a solution. This process can be time consuming. The paper proposes the use of hyper-heuristics as a means of identifying which method or combination of approaches is needed to solve a problem. The research presented forms part of a larger initiative aimed at using hyper-heuristics to develop intelligent hybrid systems. As an initial step in this direction, this paper investigates this for classical artificial intelligence uninformed and informed search methods, namely depth first search, breadth first search, best first search, hill-climbing and the A* algorithm. The hyper-heuristic determines the search or combination of searches to use to solve the problem. An evolutionary algorithm hyper-heuristic is implemented for this purpose and its performance is evaluated in solving the 8-Puzzle, Towers of Hanoi and Blocks World problems. The hyper-heuristic employs a generational evolutionary algorithm which iteratively refines an initial population using tournament selection to select parents, which the mutation and crossover operators are applied to for regeneration. The hyper-heuristic was able to identify a search or combination of searches to produce solutions for the twenty 8-Puzzle, five Towers of Hanoi and five Blocks World problems. Furthermore, admissible solutions were produced for all problem instances.
Solving Large Clustering Problems with Meta-Heuristic Search
DEFF Research Database (Denmark)
Turkensteen, Marcel; Andersen, Kim Allan; Bang-Jensen, Jørgen
In Clustering Problems, groups of similar subjects are to be retrieved from data sets. In this paper, Clustering Problems with the frequently used Minimum Sum-of-Squares Criterion are solved using meta-heuristic search. Tabu search has proved to be a successful methodology for solving optimization...... problems, but applications to large clustering problems are rare. The simulated annealing heuristic has mainly been applied to relatively small instances. In this paper, we implement tabu search and simulated annealing approaches and compare them to the commonly used k-means approach. We find that the meta-heuristic...
The beauty of simple models: Themes in recognition heuristic research
Directory of Open Access Journals (Sweden)
Daniel G. Goldstein
2011-07-01
Full Text Available The advantage of models that do not use flexible parameters is that one can precisely show to what degree they predict behavior, and in what situations. In three issues of this journal, the recognition heuristic has been examined carefully from many points of view. We comment here on four themes, the use of optimization models to understand the rationality of heuristics, the generalization of the recognition input beyond a binary judgment, new conditions for less-is-more effects, and the importance of specifying boundary conditions for cognitive heuristics.
Optimal fringe angle selection for digital fringe projection technique.
Wang, Yajun; Zhang, Song
2013-10-10
Existing digital fringe projection (DFP) systems mainly use either horizontal or vertical fringe patterns for three-dimensional shape measurement. This paper reveals that these two fringe directions are usually not optimal where the phase change is the largest to a given depth variation. We propose a novel and efficient method to determine the optimal fringe angle by projecting a set of horizontal and vertical fringe patterns onto a step-height object and by further analyzing two resultant phase maps. Experiments demonstrate the existence of the optimal angle and the success of the proposed optimal angle determination method.
Load flow optimization and optimal power flow
Das, J C
2017-01-01
This book discusses the major aspects of load flow, optimization, optimal load flow, and culminates in modern heuristic optimization techniques and evolutionary programming. In the deregulated environment, the economic provision of electrical power to consumers requires knowledge of maintaining a certain power quality and load flow. Many case studies and practical examples are included to emphasize real-world applications. The problems at the end of each chapter can be solved by hand calculations without having to use computer software. The appendices are devoted to calculations of line and cable constants, and solutions to the problems are included throughout the book.
Heuristics for Multidimensional Packing Problems
DEFF Research Database (Denmark)
Egeblad, Jens
for a minimum height container required for the items. The main contributions of the thesis are three new heuristics for strip-packing and knapsack packing problems where items are both rectangular and irregular. In the two first papers we describe a heuristic for the multidimensional strip-packing problem...... that is based on a relaxed placement principle. The heuristic starts with a random overlapping placement of items and large container dimensions. From the overlapping placement overlap is reduced iteratively until a non-overlapping placement is found and a new problem is solved with a smaller container size...... of this heuristic are among the best published in the literature both for two- and three-dimensional strip-packing problems for irregular shapes. In the third paper, we introduce a heuristic for two- and three-dimensional rectangular knapsack packing problems. The two-dimensional heuristic uses the sequence pair...
Complicated problem solution techniques in optimal parameter searching
International Nuclear Information System (INIS)
Gergel', V.P.; Grishagin, V.A.; Rogatneva, E.A.; Strongin, R.G.; Vysotskaya, I.N.; Kukhtin, V.V.
1992-01-01
An algorithm is presented of a global search for numerical solution of multidimentional multiextremal multicriteria optimization problems with complicated constraints. A boundedness of object characteristic changes is assumed at restricted changes of its parameters (Lipschitz condition). The algorithm was realized as a computer code. The algorithm was realized as a computer code. The programme was used to solve in practice the different applied optimization problems. 10 refs.; 3 figs
Advanced Gradient Based Optimization Techniques Applied on Sheet Metal Forming
International Nuclear Information System (INIS)
Endelt, Benny; Nielsen, Karl Brian
2005-01-01
The computational-costs for finite element simulations of general sheet metal forming processes are considerable, especially measured in time. In combination with optimization, the performance of the optimization algorithm is crucial for the overall performance of the system, i.e. the optimization algorithm should gain as much information about the system in each iteration as possible. Least-square formulation of the object function is widely applied for solution of inverse problems, due to the superior performance of this formulation.In this work focus will be on small problems which are defined as problems with less than 1000 design parameters; as the majority of real life optimization and inverse problems, represented in literature, can be characterized as small problems, typically with less than 20 design parameters.We will show that the least square formulation is well suited for two classes of inverse problems; identification of constitutive parameters and process optimization.The scalability and robustness of the approach are illustrated through a number of process optimizations and inverse material characterization problems; tube hydro forming, two step hydro forming, flexible aluminum tubes, inverse identification of material parameters
Directory of Open Access Journals (Sweden)
Qi Xu
2016-01-01
Full Text Available This paper proposes an economic production quantity problem with the maximal production run time and minimal preventive maintenance time over a finite planning horizon. The objective is to find the efficient production and maintenance policy to minimize the total cost composed of production, maintenance, shortages, and holding costs under the restriction on the production run time and the preventive maintenance time. The production and maintenance decisions include the production and maintenance frequencies and the production run and the maintenance time. The variability and the boundedness of the production run and maintenance time make the problem difficult to solve. Two heuristic algorithms are developed using different techniques based on the optimal properties of the relaxed problem. The performance comparison between the two algorithms is illustrated by numerical examples. The numerical results show that, for the most part, there exists a heuristic algorithm which is more effective than the other.
A Tutorial on Heuristic Methods
DEFF Research Database (Denmark)
Vidal, Rene Victor Valqui; Werra, D. de; Silver, E.
1980-01-01
In this paper we define a heuristic method as a procedure for solving a well-defined mathematical problem by an intuitive approach in which the structure of the problem can be interpreted and exploited intelligently to obtain a reasonable solution. Issues discussed include: (i) the measurement...... of the quality of a heuristic method, (ii) different types of heuristic procedures, (iii) the interactive role of human beings and (iv) factors that may influence the choice or testing of heuristic methods. A large number of references are included....
Machine learning techniques for optical communication system optimization
DEFF Research Database (Denmark)
Zibar, Darko; Wass, Jesper; Thrane, Jakob
In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction.......In this paper, machine learning techniques relevant to optical communication are presented and discussed. The focus is on applying machine learning tools to optical performance monitoring and performance prediction....
Optimal Technique for Abdominal Fascial Closure in Liver Transplant Patients
Directory of Open Access Journals (Sweden)
Unal Aydin
2010-01-01
Conclusion: Our results indicate that the novel technique used in this study contributed to overcoming early and late postoperative complications associated with closure of the abdominal fascia in liver transplant patients. In addition, this new technique has proven to be easily applicable, faster, safer and efficient in these patients; it is also potentially useful for conventional surgery.
Directory of Open Access Journals (Sweden)
Elisabeth Rangosch-Schneck
2007-01-01
Full Text Available Studying subjective attitudes has to answer the question about the possibilities of expressing personal systems of meanings and about the possibilities of reconstructing the verbalized meanings by the researchers. Investigating teachers' perceptions of parents leads to two additional problems: teachers' rule of neutrality—which demands not making emotional and degrading statements—, and the normative orientation of "partnership" with parents—which is currently being increasingly discussed in the context of school-development and which makes teachers justify their working together with parents. The Repertory Grid Technique as a method of interview design supports the teachers who have been questioned in verbalizing individual perceptions of parents without using common phrases in their statements. But explicitly integrating the method in a qualitative design raises new questions: According to the currently prevailing orientation towards the typical quantitative Grid-data, qualitative designs are of little interest, so one cannot rely on proven procedures. The procedure described in this article is characterized by using the complete transcription of the interviews with teachers and the analysis of them as texts. The quantitative grid data are only collected for heuristic purposes. This article is a contribution to the discussion of the possibilities of realizing the qualitative potentials of Repertory Grid Technique. URN: urn:nbn:de:0114-fqs070197
Optimization Techniques for Dimensionally Truncated Sparse Grids on Heterogeneous Systems
Deftu, A.; Murarasu, A.
2013-01-01
and especially the similarities between our optimization strategies for the two architectures. With regard to our test case for which achieving high speedups is a "must" for real-time visualization, we report a speedup of up to 6.2x times compared to the state
An improved technique for the prediction of optimal image resolution ...
African Journals Online (AJOL)
Past studies to predict optimal image resolution required for generating spatial information for savannah ecosystems have yielded different outcomes, hence providing a knowledge gap that was investigated in the present study. The postulation, for the present study, was that by graphically solving two simultaneous ...
Optimal Component Lumping: problem formulation and solution techniques
DEFF Research Database (Denmark)
Lin, Bao; Leibovici, Claude F.; Jørgensen, Sten Bay
2008-01-01
This paper presents a systematic method for optimal lumping of a large number of components in order to minimize the loss of information. In principle, a rigorous composition-based model is preferable to describe a system accurately. However, computational intensity and numerical issues restrict ...
Optimization of an embedded rail structure using a numerical technique
Markine, V.L.; De Man, A.P.; Esveld, C.
2000-01-01
This paper presents several steps of a procedure for design of a railway track aiming at the development of optimal track structures under various predefined service and environmental conditions. The structural behavior of the track is analyzed using a finite element model in which the track and a
Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry.
Rappoport, Dmitrij; Galvin, Cooper J; Zubarev, Dmitry Yu; Aspuru-Guzik, Alán
2014-03-11
While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here, we present a heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in cell metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.
Analysis and Optimization of Heterogeneous Real-Time Embedded Systems
DEFF Research Database (Denmark)
Pop, Paul; Eles, Petru; Peng, Zebo
2005-01-01
. The success of such new design methods depends on the availability of analysis and optimization techniques. In this paper, we present analysis and optimization techniques for heterogeneous real-time embedded systems. We address in more detail a particular class of such systems called multi-clusters, composed...... to frames. Optimization heuristics for frame packing aiming at producing a schedulable system are presented. Extensive experiments and a real-life example show the efficiency of the frame-packing approach....
Heuristics Reasoning in Diagnostic Judgment.
O'Neill, Eileen S.
1995-01-01
Describes three heuristics--short-cut mental strategies that streamline information--relevant to diagnostic reasoning: accessibility, similarity, and anchoring and adjustment. Analyzes factors thought to influence heuristic reasoning and presents interventions to be tested for nursing practice and education. (JOW)
Reexamining Our Bias against Heuristics
McLaughlin, Kevin; Eva, Kevin W.; Norman, Geoff R.
2014-01-01
Using heuristics offers several cognitive advantages, such as increased speed and reduced effort when making decisions, in addition to allowing us to make decision in situations where missing data do not allow for formal reasoning. But the traditional view of heuristics is that they trade accuracy for efficiency. Here the authors discuss sources…
Nuclear-fuel-cycle optimization: methods and modelling techniques
International Nuclear Information System (INIS)
Silvennoinen, P.
1982-01-01
This book present methods applicable to analyzing fuel-cycle logistics and optimization as well as in evaluating the economics of different reactor strategies. After an introduction to the phases of a fuel cycle, uranium cost trends are assessed in a global perspective. Subsequent chapters deal with the fuel-cycle problems faced by a power utility. The fuel-cycle models cover the entire cycle from the supply of uranium to the disposition of spent fuel. The chapter headings are: Nuclear Fuel Cycle, Uranium Supply and Demand, Basic Model of the LWR (light water reactor) Fuel Cycle, Resolution of Uncertainties, Assessment of Proliferation Risks, Multigoal Optimization, Generalized Fuel-Cycle Models, Reactor Strategy Calculations, and Interface with Energy Strategies. 47 references, 34 figures, 25 tables
Purchasing and inventory management techniques for optimizing inventory investment
International Nuclear Information System (INIS)
McFarlane, I.; Gehshan, T.
1993-01-01
In an effort to reduce operations and maintenance costs among nuclear plants, many utilities are taking a closer look at their inventory investment. Various approaches for inventory reduction have been used and discussed, but these approaches are often limited to an inventory management perspective. Interaction with purchasing and planning personnel to reduce inventory investment is a necessity in utility efforts to become more cost competitive. This paper addresses the activities that purchasing and inventory management personnel should conduct in an effort to optimize inventory investment while maintaining service-level goals. Other functions within a materials management organization, such as the warehousing and investment recovery functions, can contribute to optimizing inventory investment. However, these are not addressed in this paper because their contributions often come after inventory management and purchasing decisions have been made
Directory of Open Access Journals (Sweden)
Jianhua Wang
2014-10-01
Full Text Available Purpose: The stable relationship of one-supplier-one-customer is replaced by a dynamic relationship of multi-supplier-multi-customer in current market gradually, and efficient scheduling techniques are important tools of the dynamic supply chain relationship establishing process. This paper studies the optimization of the integrated planning and scheduling problem of a two-stage supply chain with multiple manufacturers and multiple retailers to obtain a minimum supply chain operating cost, whose manufacturers have different production capacities, holding and producing cost rates, transportation costs to retailers.Design/methodology/approach: As a complex task allocation and scheduling problem, this paper sets up an INLP model for it and designs a Unit Cost Adjusting (UCA heuristic algorithm that adjust the suppliers’ supplying quantity according to their unit costs step by step to solve the model.Findings: Relying on the contrasting analysis between the UCA and the Lingo solvers for optimizing many numerical experiments, results show that the INLP model and the UCA algorithm can obtain its near optimal solution of the two-stage supply chain’s planning and scheduling problem within very short CPU time.Research limitations/implications: The proposed UCA heuristic can easily help managers to optimizing the two-stage supply chain scheduling problems which doesn’t include the delivery time and batch of orders. For two-stage supply chains are the most common form of actual commercial relationships, so to make some modification and study on the UCA heuristic should be able to optimize the integrated planning and scheduling problems of a supply chain with more reality constraints.Originality/value: This research proposes an innovative UCA heuristic for optimizing the integrated planning and scheduling problem of two-stage supply chains with the constraints of suppliers’ production capacity and the orders’ delivering time, and has a great
International Nuclear Information System (INIS)
Tavares, R S; Tsuzuki, M S G; Martins, T C
2012-01-01
Electrical Impedance Tomography (EIT) is an imaging technique that attempts to reconstruct the conductivity distribution inside an object from electrical currents and potentials applied and measured at its surface. The EIT reconstruction problem is approached as an optimization problem, where the difference between the simulated and measured distributions must be minimized. This optimization problem can be solved using Simulated Annealing (SA), but at a high computational cost. To reduce the computational load, it is possible to use an incomplete evaluation of the objective function. This algorithm showed to present an outside-in behavior, determining the impedance of the external elements first, similar to a layer striping algorithm. A new outside-in heuristic to make use of this property is proposed. It also presents the impact of using GPU for parallelizing matrix-vector multiplication and triangular solvers. Results with experimental data are presented. The outside-in heuristic showed to be faster when compared to the conventional SA algorithm.
Directory of Open Access Journals (Sweden)
Nader Ghaffari-Nasab
2010-07-01
Full Text Available During the past two decades, there have been increasing interests on permutation flow shop with different types of objective functions such as minimizing the makespan, the weighted mean flow-time etc. The permutation flow shop is formulated as a mixed integer programming and it is classified as NP-Hard problem. Therefore, a direct solution is not available and meta-heuristic approaches need to be used to find the near-optimal solutions. In this paper, we present a new discrete firefly meta-heuristic to minimize the makespan for the permutation flow shop scheduling problem. The results of implementation of the proposed method are compared with other existing ant colony optimization technique. The preliminary results indicate that the new proposed method performs better than the ant colony for some well known benchmark problems.
Optimization of connection techniques for multipoint satellite videoconference
Perrone, A.; Puccio, A.; Tirro, S.
1985-12-01
Videoconferencing is increasingly considered a convenient substitute for business travels, and satellites will remain for a long time the most convenient means for quick network implementation. The paper gives indications about the most promising connection and demand assignment techniques, and defines a possible protocol for information exchange among involved entities.
Optimizing Nuclear Reactor Operation Using Soft Computing Techniques
Entzinger, J.O.; Ruan, D.; Kahraman, Cengiz
2006-01-01
The strict safety regulations for nuclear reactor control make it di±cult to implement new control techniques such as fuzzy logic control (FLC). FLC however, can provide very desirable advantages over classical control, like robustness, adaptation and the capability to include human experience into
Decomposition based parallel processing technique for efficient collaborative optimization
International Nuclear Information System (INIS)
Park, Hyung Wook; Kim, Sung Chan; Kim, Min Soo; Choi, Dong Hoon
2000-01-01
In practical design studies, most of designers solve multidisciplinary problems with complex design structure. These multidisciplinary problems have hundreds of analysis and thousands of variables. The sequence of process to solve these problems affects the speed of total design cycle. Thus it is very important for designer to reorder original design processes to minimize total cost and time. This is accomplished by decomposing large multidisciplinary problem into several MultiDisciplinary Analysis SubSystem (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problem to raise design efficiency by using genetic algorithm and shows the relationship between decomposition and Multidisciplinary Design Optimization(MDO) methodology
Electric power systems advanced forecasting techniques and optimal generation scheduling
Catalão, João P S
2012-01-01
Overview of Electric Power Generation SystemsCláudio MonteiroUncertainty and Risk in Generation SchedulingRabih A. JabrShort-Term Load ForecastingAlexandre P. Alves da Silva and Vitor H. FerreiraShort-Term Electricity Price ForecastingNima AmjadyShort-Term Wind Power ForecastingGregor Giebel and Michael DenhardPrice-Based Scheduling for GencosGovinda B. Shrestha and Songbo QiaoOptimal Self-Schedule of a Hydro Producer under UncertaintyF. Javier Díaz and Javie
DEFF Research Database (Denmark)
Thummala, Prasanth; Schneider, Henrik; Zhang, Zhe
2015-01-01
.The energy efficiency is optimized using a proposed new automatic winding layout (AWL) technique and a comprehensive loss model.The AWL technique generates a large number of transformer winding layouts.The transformer parasitics such as dc resistance, leakage inductance and self-capacitance are calculated...... for each winding layout.An optimization technique is formulated to minimize the sum of energy losses during charge and discharge operations.The efficiency and energy loss distribution results from the optimization routine provide a deep insight into the high voltage transformer designand its impact...
Optimization of fast dissolving etoricoxib tablets prepared by sublimation technique
Patel D; Patel M
2008-01-01
The purpose of this investigation was to develop fast dissolving tablets of etoricoxib. Granules containing etoricoxib, menthol, crospovidone, aspartame and mannitol were prepared by wet granulation technique. Menthol was sublimed from the granules by exposing the granules to vacuum. The porous granules were then compressed in to tablets. Alternatively, tablets were first prepared and later exposed to vacuum. The tablets were evaluated for percentage friability and disintegration time. A 3 2 ...
Optimal deep neural networks for sparse recovery via Laplace techniques
Limmer, Steffen; Stanczak, Slawomir
2017-01-01
This paper introduces Laplace techniques for designing a neural network, with the goal of estimating simplex-constraint sparse vectors from compressed measurements. To this end, we recast the problem of MMSE estimation (w.r.t. a pre-defined uniform input distribution) as the problem of computing the centroid of some polytope that results from the intersection of the simplex and an affine subspace determined by the measurements. Owing to the specific structure, it is shown that the centroid ca...
Impact of heuristics in clustering large biological networks.
Shafin, Md Kishwar; Kabir, Kazi Lutful; Ridwan, Iffatur; Anannya, Tasmiah Tamzid; Karim, Rashid Saadman; Hoque, Mohammad Mozammel; Rahman, M Sohel
2015-12-01
Traditional clustering algorithms often exhibit poor performance for large networks. On the contrary, greedy algorithms are found to be relatively efficient while uncovering functional modules from large biological networks. The quality of the clusters produced by these greedy techniques largely depends on the underlying heuristics employed. Different heuristics based on different attributes and properties perform differently in terms of the quality of the clusters produced. This motivates us to design new heuristics for clustering large networks. In this paper, we have proposed two new heuristics and analyzed the performance thereof after incorporating those with three different combinations in a recently celebrated greedy clustering algorithm named SPICi. We have extensively analyzed the effectiveness of these new variants. The results are found to be promising. Copyright © 2015 Elsevier Ltd. All rights reserved.
Parallel processing based decomposition technique for efficient collaborative optimization
International Nuclear Information System (INIS)
Park, Hyung Wook; Kim, Sung Chan; Kim, Min Soo; Choi, Dong Hoon
2001-01-01
In practical design studies, most of designers solve multidisciplinary problems with large sized and complex design system. These multidisciplinary problems have hundreds of analysis and thousands of variables. The sequence of process to solve these problems affects the speed of total design cycle. Thus it is very important for designer to reorder the original design processes to minimize total computational cost. This is accomplished by decomposing large multidisciplinary problem into several MultiDisciplinary Analysis SubSystem (MDASS) and processing it in parallel. This paper proposes new strategy for parallel decomposition of multidisciplinary problem to raise design efficiency by using genetic algorithm and shows the relationship between decomposition and Multidisciplinary Design Optimization(MDO) methodology
Nuclear fuel cycle optimization - methods and modelling techniques
International Nuclear Information System (INIS)
Silvennoinen, P.
1982-01-01
This book is aimed at presenting methods applicable in the analysis of fuel cycle logistics and optimization as well as in evaluating the economics of different reactor strategies. After a succinct introduction to the phases of a fuel cycle, uranium cost trends are assessed in a global perspective and subsequent chapters deal with the fuel cycle problems faced by a power utility. A fundamental material flow model is introduced first in the context of light water reactor fuel cycles. Besides the minimum cost criterion, the text also deals with other objectives providing for a treatment of cost uncertainties and of the risk of proliferation of nuclear weapons. Methods to assess mixed reactor strategies, comprising also other reactor types than the light water reactor, are confined to cost minimization. In the final Chapter, the integration of nuclear capacity within a generating system is examined. (author)
THE METHOD OF FORMING THE PIGGYBACK TECHNOLOGIES USING THE AUTOMATED HEURISTIC SYSTEM
Directory of Open Access Journals (Sweden)
Ye. Nahornyi
2015-07-01
Full Text Available In order to choose a rational piggyback technology there was offered a method that envisages the automated system improvement by giving it a heuristic nature. The automated system is based on a set of methods, techniques and strategies aimed at creating optimal resource saving technologies, which makes it possible to take into account with maximum efficiency the interests of all the participants of the delivery process. When organizing the piggyback traffic there is presupposed the coordination of operations between the piggyback traffic participants to minimize the cargo travel time.
Social heuristics shape intuitive cooperation.
Rand, David G; Peysakhovich, Alexander; Kraft-Todd, Gordon T; Newman, George E; Wurzbacher, Owen; Nowak, Martin A; Greene, Joshua D
2014-04-22
Cooperation is central to human societies. Yet relatively little is known about the cognitive underpinnings of cooperative decision making. Does cooperation require deliberate self-restraint? Or is spontaneous prosociality reined in by calculating self-interest? Here we present a theory of why (and for whom) intuition favors cooperation: cooperation is typically advantageous in everyday life, leading to the formation of generalized cooperative intuitions. Deliberation, by contrast, adjusts behaviour towards the optimum for a given situation. Thus, in one-shot anonymous interactions where selfishness is optimal, intuitive responses tend to be more cooperative than deliberative responses. We test this 'social heuristics hypothesis' by aggregating across every cooperation experiment using time pressure that we conducted over a 2-year period (15 studies and 6,910 decisions), as well as performing a novel time pressure experiment. Doing so demonstrates a positive average effect of time pressure on cooperation. We also find substantial variation in this effect, and show that this variation is partly explained by previous experience with one-shot lab experiments.
A general heuristic for genome rearrangement problems.
Dias, Ulisses; Galvão, Gustavo Rodrigues; Lintzmayer, Carla Négri; Dias, Zanoni
2014-06-01
In this paper, we present a general heuristic for several problems in the genome rearrangement field. Our heuristic does not solve any problem directly, it is rather used to improve the solutions provided by any non-optimal algorithm that solve them. Therefore, we have implemented several algorithms described in the literature and several algorithms developed by ourselves. As a whole, we implemented 23 algorithms for 9 well known problems in the genome rearrangement field. A total of 13 algorithms were implemented for problems that use the notions of prefix and suffix operations. In addition, we worked on 5 algorithms for the classic problem of sorting by transposition and we conclude the experiments by presenting results for 3 approximation algorithms for the sorting by reversals and transpositions problem and 2 approximation algorithms for the sorting by reversals problem. Another algorithm with better approximation ratio can be found for the last genome rearrangement problem, but it is purely theoretical with no practical implementation. The algorithms we implemented in addition to our heuristic lead to the best practical results in each case. In particular, we were able to improve results on the sorting by transpositions problem, which is a very special case because many efforts have been made to generate algorithms with good results in practice and some of these algorithms provide results that equal the optimum solutions in many cases. Our source codes and benchmarks are freely available upon request from the authors so that it will be easier to compare new approaches against our results.
New insights into diversification of hyper-heuristics.
Ren, Zhilei; Jiang, He; Xuan, Jifeng; Hu, Yan; Luo, Zhongxuan
2014-10-01
There has been a growing research trend of applying hyper-heuristics for problem solving, due to their ability of balancing the intensification and the diversification with low level heuristics. Traditionally, the diversification mechanism is mostly realized by perturbing the incumbent solutions to escape from local optima. In this paper, we report our attempt toward providing a new diversification mechanism, which is based on the concept of instance perturbation. In contrast to existing approaches, the proposed mechanism achieves the diversification by perturbing the instance under solving, rather than the solutions. To tackle the challenge of incorporating instance perturbation into hyper-heuristics, we also design a new hyper-heuristic framework HIP-HOP (recursive acronym of HIP-HOP is an instance perturbation-based hyper-heuristic optimization procedure), which employs a grammar guided high level strategy to manipulate the low level heuristics. With the expressive power of the grammar, the constraints, such as the feasibility of the output solution could be easily satisfied. Numerical results and statistical tests over both the Ising spin glass problem and the p -median problem instances show that HIP-HOP is able to achieve promising performances. Furthermore, runtime distribution analysis reveals that, although being relatively slow at the beginning, HIP-HOP is able to achieve competitive solutions once given sufficient time.
Energy Technology Data Exchange (ETDEWEB)
Toelle, F.J.
1997-12-01
Process Simulation is routine in chemical engineering and process analysis. This article traces the early developments of process simulation of flowsheeting. Dramatically new expectations and visions are emerging for software tools used in chemical process modeling and simulation. Many companies anticipate a rapid migration of process modeling software to an open architecture. The software components exploit object-oriented pragmatics, including abstraction, encapsulation, inheritance and polymorphism. We discuss the software architecture of tools supporting process synthesis and operations optimization. (orig.) [Deutsch] Als Simulation bezeichnen wir ein experimentelles Vorgehen, bei dem wir bestimmte Eigenschaften eines tatsaechlichen oder auch gedachten technischen, wirtschaftlichen, biologischen Systems nicht am Original selbst, sondern ersatzweise an einem geeigneten Modell des Originals, dem sogenannten Simulator, untersuchen. Bezogen auf die Prozess- und Verfahrenstechnik sind dies primaer funktionelle und systemdynamische Eigenschaften, wie zum Beispiel ein Kraftwerksblock, den wir moeglichst genau dem Original nachbilden. Dabei werden nur jene Aspekte des realen Verhaltens nachgebildet, die vom Modellierer als notwendig erachtet werden. Ein guter Simulator liefert in der Regel eine bequeme und umfassende, zeit- und kostenguenstige, gelegentlich sogar einzige praktikable Moeglichkeit zum Studium aller Betriebszustaende und Eigenschaften des Originals. (orig.)
Paranoid thinking as a heuristic.
Preti, Antonio; Cella, Matteo
2010-08-01
Paranoid thinking can be viewed as a human heuristic used by individuals to deal with uncertainty during stressful situations. Under stress, individuals are likely to emphasize the threatening value of neutral stimuli and increase the reliance on paranoia-based heuristic to interpreter events and guide their decisions. Paranoid thinking can also be activated by stress arising from the possibility of losing a good opportunity; this may result in an abnormal allocation of attentional resources to social agents. A better understanding of the interplay between cognitive heuristics and emotional processes may help to detect situations in which paranoid thinking is likely to exacerbate and improve intervention for individuals with delusional disorders.
International Nuclear Information System (INIS)
Sadjadi, Seyed Jafar; Soltani, R.
2009-01-01
We present a heuristic approach to solve a general framework of serial-parallel redundancy problem where the reliability of the system is maximized subject to some general linear constraints. The complexity of the redundancy problem is generally considered to be NP-Hard and the optimal solution is not normally available. Therefore, to evaluate the performance of the proposed method, a hybrid genetic algorithm is also implemented whose parameters are calibrated via Taguchi's robust design method. Then, various test problems are solved and the computational results indicate that the proposed heuristic approach could provide us some promising reliabilities, which are fairly close to optimal solutions in a reasonable amount of time.
Pulmonary CT angiography: optimization of contrast enhancement technique
International Nuclear Information System (INIS)
Ma Lianju; Tang Guangjian; Fu Jiazhen
2012-01-01
Objective: To derive and evaluate the formula of exactly calculating the contrast dosage used during pulmonary CT angiography (CTPA). Methods: Time density curves in 27 patients who underwent CTPA were collected and analyzed,the formula for calculating contrast dosage during CTPA was derived. 68 patients suspected of pulmonary embolism (PE) clinically but no PE on CTPA were divided randomly into group A, with bolus tracing technique (n=26), and group B, with small dose injection contrast test (SDCT) (n=42). The CT values of the right main pulmonary artery (RMPA), right upper pulmonary vein (RUPV), right posterior basal PA, right lower PV (RLPV) and the aorta were calculated. The total contrast dosage and the hard beam artifact in the SVC were compared between the two groups.Student's t test, Chi-square test and Mann-Whitney U test were used. Results: The ratio of the time from starting injection to enhancement peak of caudal end of SVC and the time to enhancement peak of the main pulmonary trunk was 0.65 ±0.09 (about 2/3), the formula for contrast dosage calculation was derived as (DTs/3 + STs/2) FR ml/s. The CT values of RMPA and RLPA between the two groups [(301 ±117), (329 ± 122) and (283 ±95), (277 ±98) HU respectively] were not significantly different (t=1.060, P=0.292; t=2.056, P=0.044), but the differences of CT values in the paired PA and PV between the two groups (median were 22.5, 58.0 and 170.5, 166.5 HU respectively) were significant (U=292, P=0.001 and U=325, P=0.005), contrast artifact of the SVC (grade 1-3) in group B (n=34, 7, 1 respectively) was significantly less than in group A (n=11, 10, 5 respectively, χ 2 =10.714, P=0.002), the contrast dosage injected in group A was ( 87.6 ± 7.3) ml, and in group B was (40.0 ±5.4) ml (P<0.01). Conclusion: CTPA with SDCT technique is superior to that with conventional bolus tracing technique regarding contrast dosage and contrast artifact in the SVC. (authors)
Mixed Integer Programming and Heuristic Scheduling for Space Communication
Lee, Charles H.; Cheung, Kar-Ming
2013-01-01
Optimal planning and scheduling for a communication network was created where the nodes within the network are communicating at the highest possible rates while meeting the mission requirements and operational constraints. The planning and scheduling problem was formulated in the framework of Mixed Integer Programming (MIP) to introduce a special penalty function to convert the MIP problem into a continuous optimization problem, and to solve the constrained optimization problem using heuristic optimization. The communication network consists of space and ground assets with the link dynamics between any two assets varying with respect to time, distance, and telecom configurations. One asset could be communicating with another at very high data rates at one time, and at other times, communication is impossible, as the asset could be inaccessible from the network due to planetary occultation. Based on the network's geometric dynamics and link capabilities, the start time, end time, and link configuration of each view period are selected to maximize the communication efficiency within the network. Mathematical formulations for the constrained mixed integer optimization problem were derived, and efficient analytical and numerical techniques were developed to find the optimal solution. By setting up the problem using MIP, the search space for the optimization problem is reduced significantly, thereby speeding up the solution process. The ratio of the dimension of the traditional method over the proposed formulation is approximately an order N (single) to 2*N (arraying), where N is the number of receiving antennas of a node. By introducing a special penalty function, the MIP problem with non-differentiable cost function and nonlinear constraints can be converted into a continuous variable problem, whose solution is possible.
Optimal time-domain technique for pulse width modulation in power electronics
Directory of Open Access Journals (Sweden)
I. Mayergoyz
2018-05-01
Full Text Available Optimal time-domain technique for pulse width modulation is presented. It is based on exact and explicit analytical solutions for inverter circuits, obtained for any sequence of input voltage rectangular pulses. Two optimal criteria are discussed and illustrated by numerical examples.
Application of Advanced Particle Swarm Optimization Techniques to Wind-thermal Coordination
DEFF Research Database (Denmark)
Singh, Sri Niwas; Østergaard, Jacob; Yadagiri, J.
2009-01-01
wind-thermal coordination algorithm is necessary to determine the optimal proportion of wind and thermal generator capacity that can be integrated into the system. In this paper, four versions of Particle Swarm Optimization (PSO) techniques are proposed for solving wind-thermal coordination problem...
Usable guidelines for usable websites? an analysis of five e-government heuristics
Welle Donker-Kuijer, M.C.J.; de Jong, Menno D.T.; Lentz, Leo
2010-01-01
Many government organizations use web heuristics for the quality assurance of their websites. Heuristics may be used by web designers to guide the decisions about a website in development, or by web evaluators to optimize or assess the quality of an existing website. Despite their popularity, very
A Comparison of Genetic Programming Variants for Hyper-Heuristics
Energy Technology Data Exchange (ETDEWEB)
Harris, Sean [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-03-01
Modern society is faced with ever more complex problems, many of which can be formulated as generate-and-test optimization problems. General-purpose optimization algorithms are not well suited for real-world scenarios where many instances of the same problem class need to be repeatedly and efficiently solved, such as routing vehicles over highways with constantly changing traffic flows, because they are not targeted to a particular scenario. Hyper-heuristics automate the design of algorithms to create a custom algorithm for a particular scenario. Hyper-heuristics typically employ Genetic Programming (GP) and this project has investigated the relationship between the choice of GP and performance in Hyper-heuristics. Results are presented demonstrating the existence of problems for which there is a statistically significant performance differential between the use of different types of GP.
Optimized digital filtering techniques for radiation detection with HPGe detectors
Energy Technology Data Exchange (ETDEWEB)
Salathe, Marco, E-mail: marco.salathe@mpi-hd.mpg.de; Kihm, Thomas, E-mail: mizzi@mpi-hd.mpg.de
2016-02-01
This paper describes state-of-the-art digital filtering techniques that are part of GEANA, an automatic data analysis software used for the GERDA experiment. The discussed filters include a novel, nonlinear correction method for ballistic deficits, which is combined with one of three shaping filters: a pseudo-Gaussian, a modified trapezoidal, or a modified cusp filter. The performance of the filters is demonstrated with a 762 g Broad Energy Germanium (BEGe) detector, produced by Canberra, that measures γ-ray lines from radioactive sources in an energy range between 59.5 and 2614.5 keV. At 1332.5 keV, together with the ballistic deficit correction method, all filters produce a comparable energy resolution of ~1.61 keV FWHM. This value is superior to those measured by the manufacturer and those found in publications with detectors of a similar design and mass. At 59.5 keV, the modified cusp filter without a ballistic deficit correction produced the best result, with an energy resolution of 0.46 keV. It is observed that the loss in resolution by using a constant shaping time over the entire energy range is small when using the ballistic deficit correction method.
Optimization of digital radiography techniques for specific application
International Nuclear Information System (INIS)
Harara, W.
2010-12-01
A low cost digital radiography system (DRS) for testing weld joints and castings in laboratory was assembled. The DRS is composed from X-ray source, scintillator, first surface mirror with Aluminum coating, charged coupled device (CCD) camera and lens. The DRS was used to test flawed carbon steel welded plates with thicknesses up to 12 mm. The comparison between the digital radiographs of the plates weldments and the radiographs of the same plates weldments using medium speed film type had shown that, the detection capability of the weld flaws are nearly identical for the two radiography techniques, while the sensitivity achieved in digital radiography of the plates weldments was one IQI wire less than the sensitivity achieved by conventional radiography of the same plates weldments according to EN 462-1. Further, the DRS was also successfully used to test (100 x 100 x 100) mm Aluminum casting with artificial flaws of varied dimensions and orientations. The resulted digital radiographs of the casting show that, all the flaws had been detected and their dimensions can be measured accurately, this confirm that, The proposed DRS can be used to detect and measure the flaws in the Aluminum and others light metals castings accurately. (author)
Heuristic introduction to gravitational waves
International Nuclear Information System (INIS)
Sandberg, V.D.
1982-01-01
The purpose of this article is to provide a rough and somewhat heuristic theoretical background and introduction to gravitational radiation, its generation, and its detection based on Einstein's general theory of relativity
Heuristic reasoning and relative incompleteness
Treur, J.
1993-01-01
In this paper an approach is presented in which heuristic reasoning is interpreted as strategic reasoning. This type of reasoning enables one to derive which hypothesis to investigate, and which observable information to acquire next (to be able to verify the chosen hypothesis). A compositional architecture for reasoning systems that perform such heuristic reasoning is introduced, called SIX (for Strategic Interactive eXpert systems). This compositional architecture enables user interaction a...
Interliminal Design: Understanding cognitive heuristics to mitigate design distortion
Directory of Open Access Journals (Sweden)
Andrew McCollough
2014-12-01
Full Text Available Cognitive heuristics are mental shortcuts adapted over time to enable rapid interpretation of our complex environment. They are intrinsic to human cognition and resist modification. Heuristics applied outside the context to which they are best suited are termed cognitive bias, and are the cause of systematic errors in judgment and reasoning. As both a cognitive and intuitive discipline, design by individuals is vulnerable to context-inappropriate heuristic usage. Designing in groups can act positively to counterbalance these tendencies, but is subject to heuristic misuse and biases particular to social environments. Mismatch between desired and actual outcomes– termed here, design distortion – occurs when such usage goes unnoticed and unaddressed, and can affect multiple dimensions of a system. We propose a methodology, interliminal design, emerging from the Program in Collaborative Design at Pacific Northwest College of Art, to specifically address the influence of cognitive heuristics in design. This adaptive approach involves reflective, dialogic, inquiry-driven practices intended to increase awareness of heuristic usage, and identify aspects of the design process vulnerable to misuse on both individual and group levels. By facilitating the detection and mitigation of potentially costly errors in judgment and decision-making that create distortion, such metacognitive techniques can meaningfully improve design.
International Nuclear Information System (INIS)
Saur, S; Frengen, J; Fjellsboe, L M B; Lindmo, T
2009-01-01
The contralateral breast (CLB) doses for three tangential techniques were characterized by using a female thorax phantom and GafChromic EBT film. Dose calculations by the pencil beam and collapsed cone algorithms were included for comparison. The film dosimetry reveals a highly inhomogeneous dose distribution within the CLB, and skin doses due to the medial fields that are several times higher than the interior dose. These phenomena are not correctly reproduced by the calculation algorithms. All tangential techniques were found to give a mean CLB dose of approximately 0.5 Gy. All wedged fields resulted in higher CLB doses than the corresponding open fields, and the lateral open fields resulted in higher CLB doses than the medial open fields. More than a twofold increase in the mean CLB dose from the medial open field was observed for a 90 deg. change of the collimator orientation. Replacing the physical wedge with a virtual wedge reduced the mean dose to the CLB by 35% and 16% for the medial and lateral fields, respectively. Lead shielding reduced the skin dose for a tangential technique by approximately 50%, but the mean CLB dose was only reduced by approximately 11%. Finally, a technique based on open medial fields in combination with several IMRT fields is proposed as a technique for minimizing the CLB dose. With and without lead shielding, the mean CLB dose using this technique was found to be 0.20 and 0.27 Gy, respectively.
2017-11-01
on Bio -Inspired Optimization Techniques by Canh Ly, Nghia Tran, and Ozlem Kilic Approved for public release; distribution is...Research Laboratory Methodology for Designing and Developing a New Ultra-Wideband Antenna Based on Bio -Inspired Optimization Techniques by...SUBTITLE Methodology for Designing and Developing a New Ultra-Wideband Antenna Based on Bio -Inspired Optimization Techniques 5a. CONTRACT NUMBER
The optimal injection technique for the osteoarthritic ankle: A randomized, cross-over trial
Witteveen, Angelique G. H.; Kok, Aimee; Sierevelt, Inger N.; Kerkhoffs, Gino M. M. J.; van Dijk, C. Niek
2013-01-01
Background: To optimize the injection technique for the osteoarthritic ankle in order to enhance the effect of intra-articular injections and minimize adverse events. Methods: Randomized cross-over trial. Comparing two injection techniques in patients with symptomatic ankle osteoarthritis. Patients
Virtual Power Plant and Microgrids controller for Energy Management based on optimization techniques
Directory of Open Access Journals (Sweden)
Maher G. M. Abdolrasol
2017-06-01
Full Text Available This paper discuss virtual power plant (VPP and Microgrid controller for energy management system (EMS based on optimization techniques by using two optimization techniques namely Backtracking search algorithm (BSA and particle swarm optimization algorithm (PSO. The research proposes use of multi Microgrid in the distribution networks to aggregate the power form distribution generation and form it into single Microgrid and let these Microgrid deal directly with the central organizer called virtual power plant. VPP duties are price forecast, demand forecast, weather forecast, production forecast, shedding loads, make intelligent decision and for aggregate & optimizes the data. This huge system has been tested and simulated by using Matlab simulink. These paper shows optimizations of two methods were really significant in the results. But BSA is better than PSO to search for better parameters which could make more power saving as in the results and the discussion.
Directory of Open Access Journals (Sweden)
Joao CARDOSO NETO
2012-01-01
Full Text Available Chile is a country with great attractions for tourists in South America and the whole world. Among the many tourist Chilean attractions the city of Vina del Mar is one of the highlights, recognized nationally and internationally as one of the most beautiful places for summer. In Vina del Mar tourists have many options for leisure, besides pretty beaches, e.g. playa renaca, the city has beautiful squares and castles, e.g. Castillo Wulff built more than 100 (one hundred years ago. It is noteworthy that already exist over there five (5 tourist itineraries, so this work was developed in order to determine the best routes to these existing itineraries, and create a unique route that includes all the tourist points in Vina del Mar, because in this way, the tourists visiting this city can minimize the time spent in traveling, as well as optimize their moments of leisure, taking the opportunity to know all the city attractions. To determine shorter ways to do it and then propose some suggestions for improvement of the quality of the tourist service offered, it had used the exact method, by solving the mathematical model of the TSP (Traveling Salesman Problem, and the heuristic method, using the most economic insertion algorithm.
Directory of Open Access Journals (Sweden)
Hyo Seon Park
2014-01-01
Full Text Available Since genetic algorithm-based optimization methods are computationally expensive for practical use in the field of structural optimization, a resizing technique-based hybrid genetic algorithm for the drift design of multistory steel frame buildings is proposed to increase the convergence speed of genetic algorithms. To reduce the number of structural analyses required for the convergence, a genetic algorithm is combined with a resizing technique that is an efficient optimal technique to control the drift of buildings without the repetitive structural analysis. The resizing technique-based hybrid genetic algorithm proposed in this paper is applied to the minimum weight design of three steel frame buildings. To evaluate the performance of the algorithm, optimum weights, computational times, and generation numbers from the proposed algorithm are compared with those from a genetic algorithm. Based on the comparisons, it is concluded that the hybrid genetic algorithm shows clear improvements in convergence properties.
International Nuclear Information System (INIS)
Li Chen; Liao Huailin; Huang Ru; Wang Yangyuan
2008-01-01
In this paper, a complementary metal-oxide semiconductor (CMOS)-compatible silicon substrate optimization technique is proposed to achieve effective isolation. The selective growth of porous silicon is used to effectively suppress the substrate crosstalk. The isolation structures are fabricated in standard CMOS process and then this post-CMOS substrate optimization technique is carried out to greatly improve the performances of crosstalk isolation. Three-dimensional electro-magnetic simulation is implemented to verify the obvious effect of our substrate optimization technique. The morphologies and growth condition of porous silicon fabricated have been investigated in detail. Furthermore, a thick selectively grown porous silicon (SGPS) trench for crosstalk isolation has been formed and about 20dB improvement in substrate isolation is achieved. These results demonstrate that our post-CMOS SGPS technique is very promising for RF IC applications. (cross-disciplinary physics and related areas of science and technology)
Heuristic space diversity management in a meta-hyper-heuristic framework
CSIR Research Space (South Africa)
Grobler, J
2014-07-01
Full Text Available This paper introduces the concept of heuristic space diversity and investigates various strategies for the management of heuristic space diversity within the context of a meta-hyper-heuristic algorithm. Evaluation on a diverse set of floating...
Heuristic space diversity control for improved meta-hyper-heuristic performance
CSIR Research Space (South Africa)
Grobler, J
2015-04-01
Full Text Available This paper expands on the concept of heuristic space diversity and investigates various strategies for the management of heuristic space diversity within the context of a meta-hyper-heuristic algorithm in search of greater performance benefits...
A rescheduling heuristic for the single machine total tardiness problem
African Journals Online (AJOL)
In this paper, we propose a rescheduling heuristic for scheduling N jobs on a .... the schedules which start both jobs i and j after time T, there is an optimal ..... [14] Picard J & Queyranne M, 1978, The time-dependent traveling salesman problem.
The afforestation problem: a heuristic method based on simulated annealing
DEFF Research Database (Denmark)
Vidal, Rene Victor Valqui
1992-01-01
This paper presents the afforestation problem, that is the location and design of new forest compartments to be planted in a given area. This optimization problem is solved by a two-step heuristic method based on simulated annealing. Tests and experiences with this method are also presented....
An adaptive dual-optimal path-planning technique for unmanned air vehicles
Directory of Open Access Journals (Sweden)
Whitfield Clifford A.
2016-01-01
Full Text Available A multi-objective technique for unmanned air vehicle path-planning generation through task allocation has been developed. The dual-optimal path-planning technique generates real-time adaptive flight paths based on available flight windows and environmental influenced objectives. The environmentally-influenced flight condition determines the aircraft optimal orientation within a downstream virtual window of possible vehicle destinations that is based on the vehicle’s kinematics. The intermittent results are then pursued by a dynamic optimization technique to determine the flight path. This path-planning technique is a multi-objective optimization procedure consisting of two goals that do not require additional information to combine the conflicting objectives into a single-objective. The technique was applied to solar-regenerative high altitude long endurance flight which can benefit significantly from an adaptive real-time path-planning technique. The objectives were to determine the minimum power required flight paths while maintaining maximum solar power for continual surveillance over an area of interest (AOI. The simulated path generation technique prolonged the flight duration over a sustained turn loiter flight path by approximately 2 months for a year of flight. The potential for prolonged solar powered flight was consistent for all latitude locations, including 2 months of available flight at 60° latitude, where sustained turn flight was no longer capable.
Mode analysis of heuristic behavior of searching for multimodal optimum point
Energy Technology Data Exchange (ETDEWEB)
Kamei, K; Araki, Y; Inoue, K
1982-01-01
Describes an experimental study of a heuristic behavior of searching for the global optimum (maximum) point of a two-dimensional, multimodal, nonlinear and unknown function. First, the authors define three modes dealing with the trial purposes, called the purpose modes and show the heuristic search behaviors expressed by the purpose modes which the human subjects select in the search experiments. Second, the authors classify the heuristic search behaviors into three types according to the mode transitions and extracts eight states of searches which cause the mode transitions. Third, a model of the heuristic search behavior is composed of the eight mode transitions. The analysis of the heuristic search behaviors by use of the purpose modes plays an important role in the heuristic search techniques. 6 references.
Yang, Y.; Özgen, S.
2017-06-01
During the last few decades, CFD (Computational Fluid Dynamics) has developed greatly and has become a more reliable tool for the conceptual phase of aircraft design. This tool is generally combined with an optimization algorithm. In the optimization phase, the need for regenerating the computational mesh might become cumbersome, especially when the number of design parameters is high. For this reason, several mesh generation and deformation techniques have been developed in the past decades. One of the most widely used techniques is the Spring Analogy. There are numerous spring analogy related techniques reported in the literature: linear spring analogy, torsional spring analogy, semitorsional spring analogy, and ball vertex spring analogy. This paper gives the explanation of linear spring analogy method and angle inclusion in the spring analogy method. In the latter case, two di¨erent solution methods are proposed. The best feasible method will later be used for two-dimensional (2D) Airfoil Design Optimization with objective function being to minimize sectional drag for a required lift coe©cient at di¨erent speeds. Design variables used in the optimization include camber and thickness distribution of the airfoil. SU2 CFD is chosen as the §ow solver during the optimization procedure. The optimization is done by using Phoenix ModelCenter Optimization Tool.
Directory of Open Access Journals (Sweden)
K.D. Mohapatra
2016-11-01
Full Text Available The objective of the present work is to use a suitable method that can optimize the process parameters like pulse on time (TON, pulse off time (TOFF, wire feed rate (WF, wire tension (WT and servo voltage (SV to attain the maximum value of MRR and minimum value of surface roughness during the production of a fine pitch spur gear made of copper. The spur gear has a pressure angle of 20⁰ and pitch circle diameter of 70 mm. The wire has a diameter of 0.25 mm and is made of brass. Experiments were conducted according to Taguchi’s orthogonal array concept with five factors and two levels. Thus, Taguchi quality loss design technique is used to optimize the output responses carried out from the experiments. Another optimization technique i.e. desirability with grey Taguchi technique has been used to optimize the process parameters. Both the optimized results are compared to find out the best combination of MRR and surface roughness. A confirmation test was carried out to identify the significant improvement in the machining performance in case of Taguchi quality loss. Finally, it was concluded that desirability with grey Taguchi technique produced a better result than the Taguchi quality loss technique in case of MRR and Taguchi quality loss gives a better result in case of surface roughness. The quality of the wire after the cutting operation has been presented in the scanning electron microscopy (SEM figure.
Optimization of freeform surfaces using intelligent deformation techniques for LED applications
Isaac, Annie Shalom; Neumann, Cornelius
2018-04-01
For many years, optical designers have great interests in designing efficient optimization algorithms to bring significant improvement to their initial design. However, the optimization is limited due to a large number of parameters present in the Non-uniform Rationaly b-Spline Surfaces. This limitation was overcome by an indirect technique known as optimization using freeform deformation (FFD). In this approach, the optical surface is placed inside a cubical grid. The vertices of this grid are modified, which deforms the underlying optical surface during the optimization. One of the challenges in this technique is the selection of appropriate vertices of the cubical grid. This is because these vertices share no relationship with the optical performance. When irrelevant vertices are selected, the computational complexity increases. Moreover, the surfaces created by them are not always feasible to manufacture, which is the same problem faced in any optimization technique while creating freeform surfaces. Therefore, this research addresses these two important issues and provides feasible design techniques to solve them. Finally, the proposed techniques are validated using two different illumination examples: street lighting lens and stop lamp for automobiles.
Directory of Open Access Journals (Sweden)
Muqaddas Naz
2018-02-01
Full Text Available With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE, is proposed by merging enhanced differential evolution (EDE and gray wolf optimization (GWO scheme using real-time pricing (RTP and critical peak pricing (CPP. Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30- and 60-min operational time interval (OTI. On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff.
Heuristic errors in clinical reasoning.
Rylander, Melanie; Guerrasio, Jeannette
2016-08-01
Errors in clinical reasoning contribute to patient morbidity and mortality. The purpose of this study was to determine the types of heuristic errors made by third-year medical students and first-year residents. This study surveyed approximately 150 clinical educators inquiring about the types of heuristic errors they observed in third-year medical students and first-year residents. Anchoring and premature closure were the two most common errors observed amongst third-year medical students and first-year residents. There was no difference in the types of errors observed in the two groups. Errors in clinical reasoning contribute to patient morbidity and mortality Clinical educators perceived that both third-year medical students and first-year residents committed similar heuristic errors, implying that additional medical knowledge and clinical experience do not affect the types of heuristic errors made. Further work is needed to help identify methods that can be used to reduce heuristic errors early in a clinician's education. © 2015 John Wiley & Sons Ltd.
Towards an Understanding of Instructional Design Heuristics: An Exploratory Delphi Study
York, Cindy S.; Ertmer, Peggy A.
2011-01-01
Evidence suggests that experienced instructional designers often use heuristics and adapted models when engaged in the instructional design problem-solving process. This study used the Delphi technique to identify a core set of heuristics designers reported as being important to the success of the design process. The overarching purpose of the…
Using Heuristic Task Analysis to Create Web-Based Instructional Design Theory
Fiester, Herbert R.
2010-01-01
The first purpose of this study was to identify procedural and heuristic knowledge used when creating web-based instruction. The second purpose of this study was to develop suggestions for improving the Heuristic Task Analysis process, a technique for eliciting, analyzing, and representing expertise in cognitively complex tasks. Three expert…
Studies Regarding Design and Optimization of Mechanisms Using Modern Techniques of CAD and CAE
Directory of Open Access Journals (Sweden)
Marius Tufoi
2010-01-01
Full Text Available The paper presents applications of modern techniques of CAD (Computer Aided Design and CAE (Computer Aided Engineering to design and optimize the mechanisms used in mechanical engineering. The use exemplification of these techniques was achieved by designing and optimizing parts of a drawing installation for horizontal continuous casting of metals. By applying these design methods and using finite element method at simulations on designed mechanisms results a number of advantages over traditional methods of drawing and design: speed in drawing, design and optimization of parts and mechanisms, kinematic analysis option, kinetostatic and dynamic through simulation, without requiring physical realization of the part or mechanism, the determination by finite element method of tension, elongations, travel and safety factor and the possibility of optimization for these sizes to ensure the mechanical strength of each piece separately. Achieving these studies was possible using SolidWorks 2009 software suite.
Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks
Lee, Charles H.; Cheung, Kar-Ming
2012-01-01
In this paper, we propose to solve the constrained optimization problem in two phases. The first phase uses heuristic methods such as the ant colony method, particle swarming optimization, and genetic algorithm to seek a near optimal solution among a list of feasible initial populations. The final optimal solution can be found by using the solution of the first phase as the initial condition to the SQP algorithm. We demonstrate the above problem formulation and optimization schemes with a large-scale network that includes the DSN ground stations and a number of spacecraft of deep space missions.
A greedy double swap heuristic for nurse scheduling
Directory of Open Access Journals (Sweden)
Murphy Choy
2012-10-01
Full Text Available One of the key challenges of nurse scheduling problem (NSP is the number of constraints placed on preparing the timetable, both from the regulatory requirements as well as the patients’ demand for the appropriate nursing care specialists. In addition, the preferences of the nursing staffs related to their work schedules add another dimension of complexity. Most solutions proposed for solving nurse scheduling involve the use of mathematical programming and generally considers only the hard constraints. However, the psychological needs of the nurses are ignored and this resulted in subsequent interventions by the nursing staffs to remedy any deficiency and often results in last minute changes to the schedule. In this paper, we present a staff preference optimization framework solved with a greedy double swap heuristic. The heuristic yields good performance in speed at solving the problem. The heuristic is simple and we will demonstrate its performance by implementing it on open source spreadsheet software.
Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial.
Ibrahim, Ahmed; Alfa, Attahiru
2017-08-01
This paper is intended to serve as an overview of, and mostly a tutorial to illustrate, the optimization techniques used in several different key design aspects that have been considered in the literature of wireless sensor networks (WSNs). It targets the researchers who are new to the mathematical optimization tool, and wish to apply it to WSN design problems. We hence divide the paper into two main parts. One part is dedicated to introduce optimization theory and an overview on some of its techniques that could be helpful in design problem in WSNs. In the second part, we present a number of design aspects that we came across in the WSN literature in which mathematical optimization methods have been used in the design. For each design aspect, a key paper is selected, and for each we explain the formulation techniques and the solution methods implemented. We also provide in-depth analyses and assessments of the problem formulations, the corresponding solution techniques and experimental procedures in some of these papers. The analyses and assessments, which are provided in the form of comments, are meant to reflect the points that we believe should be taken into account when using optimization as a tool for design purposes.
Study of heuristics in ant system for nuclear reload optimisation
International Nuclear Information System (INIS)
Lima, Alan M.M. de; Schirru, Roberto; Silva, Fernando C. da; Machado, Marcelo D.; Medeiros, Jose A.C.C.
2007-01-01
A Pressurized Water Reactor core must be reloaded every time the fuel burnup reaches a level when it is not possible to sustain nominal power operation. The nuclear core fuel reload optimization consists in finding a burned-up and fresh-fuel-assembly loading pattern that maximizes the number of effective full power days, minimizing the relationship cost/benefit. This problem is NP-hard, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Besides that, the problem is non-linear and its search space is highly discontinual and multimodal. In this work a parallel computational system based on Ant Colony System (ACS) called Artificial-Ant-Colony Networks is used to solve the nuclear reactor core fuel reload optimization problem, with compatibles heuristics. ACS is a system based on artificial agents that uses the reinforcement learning technique and was originally developed to solve the Traveling Salesman Problem, which is conceptually similar to the nuclear fuel reload problem. (author)
Study of heuristics in ant system for nuclear reload optimisation
Energy Technology Data Exchange (ETDEWEB)
Lima, Alan M.M. de; Schirru, Roberto; Silva, Fernando C. da; Machado, Marcelo D.; Medeiros, Jose A.C.C. [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear]. E-mail: alan@lmp.ufrj.br; schirru@lmp.ufrj.br; fernando@con.ufrj.br; marcelo@lmp.ufrj.br; canedo@lmp.ufrj.br
2007-07-01
A Pressurized Water Reactor core must be reloaded every time the fuel burnup reaches a level when it is not possible to sustain nominal power operation. The nuclear core fuel reload optimization consists in finding a burned-up and fresh-fuel-assembly loading pattern that maximizes the number of effective full power days, minimizing the relationship cost/benefit. This problem is NP-hard, meaning that complexity grows exponentially with the number of fuel assemblies in the core. Besides that, the problem is non-linear and its search space is highly discontinual and multimodal. In this work a parallel computational system based on Ant Colony System (ACS) called Artificial-Ant-Colony Networks is used to solve the nuclear reactor core fuel reload optimization problem, with compatibles heuristics. ACS is a system based on artificial agents that uses the reinforcement learning technique and was originally developed to solve the Traveling Salesman Problem, which is conceptually similar to the nuclear fuel reload problem. (author)
New Techniques for Optimal Treatment Planning for LINAC-based Sterotactic Radiosurgery
International Nuclear Information System (INIS)
Suh, Tae Suk
1992-01-01
Since LINAC-based stereotactic radiosurgery uses multiple noncoplanar arcs, three-dimensional dose evaluation and many beam parameters, a lengthy computation time is required to optimize even the simplest case by a trial and error. The basic approach presented in this paper is to show promising methods using an experimental optimization and an analytic optimization. The purpose of this paper is not to describe the detailed methods, but introduce briefly, proceeding research done currently or in near future. A more detailed description will be shown in ongoing published papers. Experimental optimization is based on two approaches. One is shaping the target volumes through the use of multiple isocenters determined from dose experience and testing. The other method is conformal therapy using a beam eye view technique and field shaping. The analytic approach is to adapt computer-aided design optimization in finding optimum irradiation parameters automatically
Connection between optimal control theory and adiabatic-passage techniques in quantum systems
Assémat, E.; Sugny, D.
2012-08-01
This work explores the relationship between optimal control theory and adiabatic passage techniques in quantum systems. The study is based on a geometric analysis of the Hamiltonian dynamics constructed from Pontryagin's maximum principle. In a three-level quantum system, we show that the stimulated Raman adiabatic passage technique can be associated to a peculiar Hamiltonian singularity. One deduces that the adiabatic pulse is solution of the optimal control problem only for a specific cost functional. This analysis is extended to the case of a four-level quantum system.
Directory of Open Access Journals (Sweden)
Thenmozhi Srinivasan
2015-01-01
Full Text Available Clusters of high-dimensional data techniques are emerging, according to data noisy and poor quality challenges. This paper has been developed to cluster data using high-dimensional similarity based PCM (SPCM, with ant colony optimization intelligence which is effective in clustering nonspatial data without getting knowledge about cluster number from the user. The PCM becomes similarity based by using mountain method with it. Though this is efficient clustering, it is checked for optimization using ant colony algorithm with swarm intelligence. Thus the scalable clustering technique is obtained and the evaluation results are checked with synthetic datasets.
Newsom, J. R.; Mukhopadhyay, V.
1983-01-01
A method for designing robust feedback controllers for multiloop systems is presented. Robustness is characterized in terms of the minimum singular value of the system return difference matrix at the plant input. Analytical gradients of the singular values with respect to design variables in the controller are derived. A cumulative measure of the singular values and their gradients with respect to the design variables is used with a numerical optimization technique to increase the system's robustness. Both unconstrained and constrained optimization techniques are evaluated. Numerical results are presented for a two output drone flight control system.
An Adaptation of the Kernighan-Lin Heuristic to the Simple Graph Partitioning Problem
DEFF Research Database (Denmark)
Sørensen, Michael Malmros
1999-01-01
to this problem of the Kernighan-Lin exchange heuristic, which was originally developed for the closely related 2-partition problem. The evaluation is carried out on problem instances on graphs with up to 50 nodes for which the optimal partition values are known or upper bounds are available. The computational...... results show that among all instances with known optimal values the best partition values found by a randomized version of this heuristic lie well within 1% off the optimum....
Tuning of PID controller using optimization techniques for a MIMO process
Thulasi dharan, S.; Kavyarasan, K.; Bagyaveereswaran, V.
2017-11-01
In this paper, two processes were considered one is Quadruple tank process and the other is CSTR (Continuous Stirred Tank Reactor) process. These are majorly used in many industrial applications for various domains, especially, CSTR in chemical plants.At first mathematical model of both the process is to be done followed by linearization of the system due to MIMO process and controllers are the major part to control the whole process to our desired point as per the applications so the tuning of the controller plays a major role among the whole process. For tuning of parameters we use two optimizations techniques like Particle Swarm Optimization, Genetic Algorithm. The above techniques are majorly used in different applications to obtain which gives the best among all, we use these techniques to obtain the best tuned values among many. Finally, we will compare the performance of the each process with both the techniques.
A Novel Analytical Technique for Optimal Allocation of Capacitors in Radial Distribution Systems
Directory of Open Access Journals (Sweden)
Sarfaraz Nawaz
2017-07-01
Full Text Available In this paper, a novel analytical technique is proposed to determine the optimal size and location of shunt capacitor units in radial distribution systems. An objective function is formulated to reduce real power loss, to improve the voltage profile and to increase annual cost savings. A new constant, the Loss Sensitivity Constant (LSC, is proposed here. The value of LSC decides the location and size of candidate buses. The technique is demonstrated on an IEEE-33 bus system at different load levels and the 130-bus distribution system of Jamawa Ramgarh village, Jaipur city. The obtained results are compared with the latest optimization techniques to show the effectiveness and robustness of the proposed technique.
Optimization and optimal control in automotive systems
Kolmanovsky, Ilya; Steinbuch, Maarten; Re, Luigi
2014-01-01
This book demonstrates the use of the optimization techniques that are becoming essential to meet the increasing stringency and variety of requirements for automotive systems. It shows the reader how to move away from earlier approaches, based on some degree of heuristics, to the use of more and more common systematic methods. Even systematic methods can be developed and applied in a large number of forms so the text collects contributions from across the theory, methods and real-world automotive applications of optimization. Greater fuel economy, significant reductions in permissible emissions, new drivability requirements and the generally increasing complexity of automotive systems are among the criteria that the contributing authors set themselves to meet. In many cases multiple and often conflicting requirements give rise to multi-objective constrained optimization problems which are also considered. Some of these problems fall into the domain of the traditional multi-disciplinary optimization applie...
Better Drumming Through Calibration: Techniques for Pre-Performance Robotic Percussion Optimization
Murphy, Jim; Kapur, Ajay; Carnegie, Dale
2012-01-01
A problem with many contemporary musical robotic percussion systems lies in the fact that solenoids fail to respond lin-early to linear increases in input velocity. This nonlinearity forces performers to individually tailor their compositions to specific robotic drummers. To address this problem, we introduce a method of pre-performance calibration using metaheuristic search techniques. A variety of such techniques are introduced and evaluated and the results of the optimized solenoid-based p...
Evolutionary algorithm based heuristic scheme for nonlinear heat transfer equations.
Ullah, Azmat; Malik, Suheel Abdullah; Alimgeer, Khurram Saleem
2018-01-01
In this paper, a hybrid heuristic scheme based on two different basis functions i.e. Log Sigmoid and Bernstein Polynomial with unknown parameters is used for solving the nonlinear heat transfer equations efficiently. The proposed technique transforms the given nonlinear ordinary differential equation into an equivalent global error minimization problem. Trial solution for the given nonlinear differential equation is formulated using a fitness function with unknown parameters. The proposed hybrid scheme of Genetic Algorithm (GA) with Interior Point Algorithm (IPA) is opted to solve the minimization problem and to achieve the optimal values of unknown parameters. The effectiveness of the proposed scheme is validated by solving nonlinear heat transfer equations. The results obtained by the proposed scheme are compared and found in sharp agreement with both the exact solution and solution obtained by Haar Wavelet-Quasilinearization technique which witnesses the effectiveness and viability of the suggested scheme. Moreover, the statistical analysis is also conducted for investigating the stability and reliability of the presented scheme.
Evolutionary algorithm based heuristic scheme for nonlinear heat transfer equations.
Directory of Open Access Journals (Sweden)
Azmat Ullah
Full Text Available In this paper, a hybrid heuristic scheme based on two different basis functions i.e. Log Sigmoid and Bernstein Polynomial with unknown parameters is used for solving the nonlinear heat transfer equations efficiently. The proposed technique transforms the given nonlinear ordinary differential equation into an equivalent global error minimization problem. Trial solution for the given nonlinear differential equation is formulated using a fitness function with unknown parameters. The proposed hybrid scheme of Genetic Algorithm (GA with Interior Point Algorithm (IPA is opted to solve the minimization problem and to achieve the optimal values of unknown parameters. The effectiveness of the proposed scheme is validated by solving nonlinear heat transfer equations. The results obtained by the proposed scheme are compared and found in sharp agreement with both the exact solution and solution obtained by Haar Wavelet-Quasilinearization technique which witnesses the effectiveness and viability of the suggested scheme. Moreover, the statistical analysis is also conducted for investigating the stability and reliability of the presented scheme.
Concentrated Hitting Times of Randomized Search Heuristics with Variable Drift
DEFF Research Database (Denmark)
Lehre, Per Kristian; Witt, Carsten
2014-01-01
Drift analysis is one of the state-of-the-art techniques for the runtime analysis of randomized search heuristics (RSHs) such as evolutionary algorithms (EAs), simulated annealing etc. The vast majority of existing drift theorems yield bounds on the expected value of the hitting time for a target...
On the Optimization of Aerospace Plane Ascent Trajectory
Al-Garni, Ahmed; Kassem, Ayman Hamdy
A hybrid heuristic optimization technique based on genetic algorithms and particle swarm optimization has been developed and tested for trajectory optimization problems with multi-constraints and a multi-objective cost function. The technique is used to calculate control settings for two types for ascending trajectories (constant dynamic pressure and minimum-fuel-minimum-heat) for a two-dimensional model of an aerospace plane. A thorough statistical analysis is done on the hybrid technique to make comparisons with both basic genetic algorithms and particle swarm optimization techniques with respect to convergence and execution time. Genetic algorithm optimization showed better execution time performance while particle swarm optimization showed better convergence performance. The hybrid optimization technique, benefiting from both techniques, showed superior robust performance compromising convergence trends and execution time.
Alanis Pena, Antonio Alejandro
Major commercial electricity generation is done by burning fossil fuels out of which coal-fired power plants produce a substantial quantity of electricity worldwide. The United States has large reserves of coal, and it is cheaply available, making it a good choice for the generation of electricity on a large scale. However, one major problem associated with using coal for combustion is that it produces a group of pollutants known as nitrogen oxides (NO x). NOx are strong oxidizers and contribute to ozone formation and respiratory illness. The Environmental Protection Agency (EPA) regulates the quantity of NOx emitted to the atmosphere in the United States. One technique coal-fired power plants use to reduce NOx emissions is Selective Catalytic Reduction (SCR). SCR uses layers of catalyst that need to be added or changed to maintain the required performance. Power plants do add or change catalyst layers during temporary shutdowns, but it is expensive. However, many companies do not have only one power plant, but instead they can have a fleet of coal-fired power plants. A fleet of power plants can use EPA cap and trade programs to have an outlet NOx emission below the allowances for the fleet. For that reason, the main aim of this research is to develop an SCR management mathematical optimization methods that, with a given set of scheduled outages for a fleet of power plants, minimizes the total cost of the entire fleet of power plants and also maintain outlet NO x below the desired target for the entire fleet. We use a multi commodity network flow problem (MCFP) that creates edges that represent all the SCR catalyst layers for each plant. This MCFP is relaxed because it does not consider average daily NOx constraint, and it is solved by a binary integer program. After that, we add the average daily NOx constraint to the model with a schedule elimination constraint (MCFPwSEC). The MCFPwSEC eliminates, one by one, the solutions that do not satisfy the average daily
Solar photovoltaic power forecasting using optimized modified extreme learning machine technique
Directory of Open Access Journals (Sweden)
Manoja Kumar Behera
2018-06-01
Full Text Available Prediction of photovoltaic power is a significant research area using different forecasting techniques mitigating the effects of the uncertainty of the photovoltaic generation. Increasingly high penetration level of photovoltaic (PV generation arises in smart grid and microgrid concept. Solar source is irregular in nature as a result PV power is intermittent and is highly dependent on irradiance, temperature level and other atmospheric parameters. Large scale photovoltaic generation and penetration to the conventional power system introduces the significant challenges to microgrid a smart grid energy management. It is very critical to do exact forecasting of solar power/irradiance in order to secure the economic operation of the microgrid and smart grid. In this paper an extreme learning machine (ELM technique is used for PV power forecasting of a real time model whose location is given in the Table 1. Here the model is associated with the incremental conductance (IC maximum power point tracking (MPPT technique that is based on proportional integral (PI controller which is simulated in MATLAB/SIMULINK software. To train single layer feed-forward network (SLFN, ELM algorithm is implemented whose weights are updated by different particle swarm optimization (PSO techniques and their performance are compared with existing models like back propagation (BP forecasting model. Keywords: PV array, Extreme learning machine, Maximum power point tracking, Particle swarm optimization, Craziness particle swarm optimization, Accelerate particle swarm optimization, Single layer feed-forward network
Alirezaei, M.; Kanarachos, S.A.; Scheepers, B.T.M.; Maurice, J.P.
2013-01-01
Development and experimentally evaluation of an optimal Vehicle Dynamic Control (VDC) strategy based on the State Dependent Riccati Equation (SDRE) control technique is presented. The proposed nonlinear controller is based on a nonlinear vehicle model with nonlinear tire characteristics. A novel
Space-mapping techniques applied to the optimization of a safety isolating transformer
T.V. Tran; S. Brisset; D. Echeverria (David); D.J.P. Lahaye (Domenico); P. Brochet
2007-01-01
textabstractSpace-mapping optimization techniques allow to allign low-fidelity and high-fidelity models in order to reduce the computational time and increase the accuracy of the solution. The main idea is to build an approximate model from the difference of response between both models. Therefore
Cheng, Jie; Qian, Zhaogang; Irani, Keki B.; Etemad, Hossein; Elta, Michael E.
1991-03-01
To meet the ever-increasing demand of the rapidly-growing semiconductor manufacturing industry it is critical to have a comprehensive methodology integrating techniques for process optimization real-time monitoring and adaptive process control. To this end we have accomplished an integrated knowledge-based approach combining latest expert system technology machine learning method and traditional statistical process control (SPC) techniques. This knowledge-based approach is advantageous in that it makes it possible for the task of process optimization and adaptive control to be performed consistently and predictably. Furthermore this approach can be used to construct high-level and qualitative description of processes and thus make the process behavior easy to monitor predict and control. Two software packages RIST (Rule Induction and Statistical Testing) and KARSM (Knowledge Acquisition from Response Surface Methodology) have been developed and incorporated with two commercially available packages G2 (real-time expert system) and ULTRAMAX (a tool for sequential process optimization).
Directory of Open Access Journals (Sweden)
Jude Hemanth Duraisamy
2016-01-01
Full Text Available Image steganography is one of the ever growing computational approaches which has found its application in many fields. The frequency domain techniques are highly preferred for image steganography applications. However, there are significant drawbacks associated with these techniques. In transform based approaches, the secret data is embedded in random manner in the transform coefficients of the cover image. These transform coefficients may not be optimal in terms of the stego image quality and embedding capacity. In this work, the application of Genetic Algorithm (GA and Particle Swarm Optimization (PSO have been explored in the context of determining the optimal coefficients in these transforms. Frequency domain transforms such as Bandelet Transform (BT and Finite Ridgelet Transform (FRIT are used in combination with GA and PSO to improve the efficiency of the image steganography system.
International Nuclear Information System (INIS)
Li Qin; Yang Lizhi; Song Lixia; Qin De'en; Xue Yongshe; Wang Zhipeng
2012-01-01
Aim at high rate of large blast fragmentation, a big difficulty in long hole drilling and blasting underground uranium mine stope, it is pointed out at the same time of taking integrated technical management measures, the key is to optimize the drilling and blasting parameters and insure safety the act of one that primes, adopt 'minimum burden' blasting technique, renew the stope fragmentation process, and use new process of hole bottom indirect initiation fragmentation; optimize the detonating circuit and use safe, reliable and economically rational duplex non-electric detonating circuit. The production practice shows that under the guarantee of strictly controlled construction quality, the application of optimized blast fragmentation technique has enhanced the reliability of safety detonation and preferably solved the problem of high rate of large blast fragments. (authors)
Combined Heuristic Attack Strategy on Complex Networks
Directory of Open Access Journals (Sweden)
Marek Šimon
2017-01-01
Full Text Available Usually, the existence of a complex network is considered an advantage feature and efforts are made to increase its robustness against an attack. However, there exist also harmful and/or malicious networks, from social ones like spreading hoax, corruption, phishing, extremist ideology, and terrorist support up to computer networks spreading computer viruses or DDoS attack software or even biological networks of carriers or transport centers spreading disease among the population. New attack strategy can be therefore used against malicious networks, as well as in a worst-case scenario test for robustness of a useful network. A common measure of robustness of networks is their disintegration level after removal of a fraction of nodes. This robustness can be calculated as a ratio of the number of nodes of the greatest remaining network component against the number of nodes in the original network. Our paper presents a combination of heuristics optimized for an attack on a complex network to achieve its greatest disintegration. Nodes are deleted sequentially based on a heuristic criterion. Efficiency of classical attack approaches is compared to the proposed approach on Barabási-Albert, scale-free with tunable power-law exponent, and Erdős-Rényi models of complex networks and on real-world networks. Our attack strategy results in a faster disintegration, which is counterbalanced by its slightly increased computational demands.
A Geographical Heuristic Routing Protocol for VANETs
Urquiza-Aguiar, Luis; Tripp-Barba, Carolina; Aguilar Igartua, Mónica
2016-01-01
Vehicular ad hoc networks (VANETs) leverage the communication system of Intelligent Transportation Systems (ITS). Recently, Delay-Tolerant Network (DTN) routing protocols have increased their popularity among the research community for being used in non-safety VANET applications and services like traffic reporting. Vehicular DTN protocols use geographical and local information to make forwarding decisions. However, current proposals only consider the selection of the best candidate based on a local-search. In this paper, we propose a generic Geographical Heuristic Routing (GHR) protocol that can be applied to any DTN geographical routing protocol that makes forwarding decisions hop by hop. GHR includes in its operation adaptations simulated annealing and Tabu-search meta-heuristics, which have largely been used to improve local-search results in discrete optimization. We include a complete performance evaluation of GHR in a multi-hop VANET simulation scenario for a reporting service. Our study analyzes all of the meaningful configurations of GHR and offers a statistical analysis of our findings by means of MANOVA tests. Our results indicate that the use of a Tabu list contributes to improving the packet delivery ratio by around 5% to 10%. Moreover, if Tabu is used, then the simulated annealing routing strategy gets a better performance than the selection of the best node used with carry and forwarding (default operation). PMID:27669254
Heuristic Biases in Mathematical Reasoning
Inglis, Matthew; Simpson, Adrian
2005-01-01
In this paper we briefly describe the dual process account of reasoning, and explain the role of heuristic biases in human thought. Concentrating on the so-called matching bias effect, we describe a piece of research that indicates a correlation between success at advanced level mathematics and an ability to override innate and misleading…
Heuristic reasoning and relative incompleteness
Treur, J.
1993-01-01
In this paper an approach is presented in which heuristic reasoning is interpreted as strategic reasoning. This type of reasoning enables one to derive which hypothesis to investigate, and which observable information to acquire next (to be able to verify the chosen hypothesis). A compositional
Design refinement of multilayer optical thin film devices with two optimization techniques
International Nuclear Information System (INIS)
Apparao, K.V.S.R.
1992-01-01
The design efficiency of two different optimization techniques of designing multilayer optical thin film devices is compared. Ten different devices of varying complexities are chosen as design examples for the comparison. The design refinement efficiency and the design parameter characteristics of all the sample designs obtained with the two techniques are compared. The results of the comparison demonstrate that the new method of design developed using damped least squares technique with indirect derivatives give superior and efficient designs compared to the method developed with direct derivatives. (author). 23 refs., 4 tabs., 14 figs
A Standalone PV System with a Hybrid P&O MPPT Optimization Technique
Directory of Open Access Journals (Sweden)
S. Hota
2017-12-01
Full Text Available In this paper a maximum power point tracking (MPPT design for a photovoltaic (PV system using a hybrid optimization technique is proposed. For maximum power transfer, maximum harvestable power from a PV cell in a dynamically changing surrounding should be known. The proposed technique is compared with the conventional Perturb and Observe (P&O technique. A comparative analysis of power-voltage and current-voltage characteristics of a PV cell with and without the MPPT module when connected to the grid was performed in SIMULINK, to demonstrate the increment in the efficiency of the PV module after using the MPPT module.
Heuristic learning parameter identification for surveillance and diagnostics of nuclear power plants
International Nuclear Information System (INIS)
Machado, E.L.
1983-01-01
A new method of heuristic reinforcement learning was developed for parameter identification purposes. In essence, this new parameter identification technique is based on the idea of breaking a multidimensional search for the minimum of a given functional into a set of unidirectional searches in parameter space. Each search situation is associated with one block in a memory organized into cells, where the information learned about the situations is stored (e.g. the optimal directions in parameter space). Whenever the search falls into an existing memory cell, the system chooses the learned direction. For new search situations, the system creates additional memory cells. This algorithm imitates the following cognitive process: 1) characterize a situation, 2) select an optimal action, 3) evaluate the consequences of the action, and 4) memorize the results for future use. As a result, this algorithm is trainable in the sense that it can learn from previous experience within a specific class of parameter identification problems
Optimization Techniques for Improving the Performance of Silicone-Based Dielectric Elastomers
DEFF Research Database (Denmark)
Skov, Anne Ladegaard; Yu, Liyun
2017-01-01
the electro-mechanical performance of dielectric elastomers are highlighted. Various optimization methods for improved energy transduction are investigated and discussed, with special emphasis placed on the promise each method holds. The compositing and blending of elastomers are shown to be simple, versatile...... methods that can solve a number of optimization issues. More complicated methods, involving chemical modification of the silicone backbone as well as controlling the network structure for improved mechanical properties, are shown to solve yet more issues. From the analysis, it is obvious...... that there is not a single optimization technique that will lead to the universal optimization of dielectric elastomer films, though each method may lead to elastomers with certain features, and thus certain potentials....
DEFF Research Database (Denmark)
Vlachogiannis, Ioannis (John); Lee, K. Y.
2010-01-01
a memory of its best position ever encountered, and is attracted only by other particles with better achievements than its own with the exception of the particle with the best achievement, which moves randomly.The ICA-PSO algorithm is tested on a number of power systems, including the systems with 6, 13...
International Nuclear Information System (INIS)
Seeram, Euclid; Davidson, Rob; Bushong, Stewart; Swan, Hans
2013-01-01
The purpose of this paper is to review the literature on exposure technique approaches in Computed Radiography (CR) imaging as a means of radiation dose optimization in CR imaging. Specifically the review assessed three approaches: optimization of kVp; optimization of mAs; and optimization of the Exposure Indicator (EI) in practice. Only papers dating back to 2005 were described in this review. The major themes, patterns, and common findings from the literature reviewed showed that important features are related to radiation dose management strategies for digital radiography include identification of the EI as a dose control mechanism and as a “surrogate for dose management”. In addition the use of the EI has been viewed as an opportunity for dose optimization. Furthermore optimization research has focussed mainly on optimizing the kVp in CR imaging as a means of implementing the ALARA philosophy, and studies have concentrated on mainly chest imaging using different CR systems such as those commercially available from Fuji, Agfa, Kodak, and Konica-Minolta. These studies have produced “conflicting results”. In addition, a common pattern was the use of automatic exposure control (AEC) and the measurement of constant effective dose, and the use of a dose-area product (DAP) meter
Al-Khatib, Ra'ed M; Rashid, Nur'Aini Abdul; Abdullah, Rosni
2011-08-01
The secondary structure of RNA pseudoknots has been extensively inferred and scrutinized by computational approaches. Experimental methods for determining RNA structure are time consuming and tedious; therefore, predictive computational approaches are required. Predicting the most accurate and energy-stable pseudoknot RNA secondary structure has been proven to be an NP-hard problem. In this paper, a new RNA folding approach, termed MSeeker, is presented; it includes KnotSeeker (a heuristic method) and Mfold (a thermodynamic algorithm). The global optimization of this thermodynamic heuristic approach was further enhanced by using a case-based reasoning technique as a local optimization method. MSeeker is a proposed algorithm for predicting RNA pseudoknot structure from individual sequences, especially long ones. This research demonstrates that MSeeker improves the sensitivity and specificity of existing RNA pseudoknot structure predictions. The performance and structural results from this proposed method were evaluated against seven other state-of-the-art pseudoknot prediction methods. The MSeeker method had better sensitivity than the DotKnot, FlexStem, HotKnots, pknotsRG, ILM, NUPACK and pknotsRE methods, with 79% of the predicted pseudoknot base-pairs being correct.
Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen
2018-01-01
With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP
Liang, Bin; Li, Yongbao; Wei, Ran; Guo, Bin; Xu, Xuang; Liu, Bo; Li, Jiafeng; Wu, Qiuwen; Zhou, Fugen
2018-01-05
With robot-controlled linac positioning, robotic radiotherapy systems such as CyberKnife significantly increase freedom of radiation beam placement, but also impose more challenges on treatment plan optimization. The resampling mechanism in the vendor-supplied treatment planning system (MultiPlan) cannot fully explore the increased beam direction search space. Besides, a sparse treatment plan (using fewer beams) is desired to improve treatment efficiency. This study proposes a singular value decomposition linear programming (SVDLP) optimization technique for circular collimator based robotic radiotherapy. The SVDLP approach initializes the input beams by simulating the process of covering the entire target volume with equivalent beam tapers. The requirements on dosimetry distribution are modeled as hard and soft constraints, and the sparsity of the treatment plan is achieved by compressive sensing. The proposed linear programming (LP) model optimizes beam weights by minimizing the deviation of soft constraints subject to hard constraints, with a constraint on the l 1 norm of the beam weight. A singular value decomposition (SVD) based acceleration technique was developed for the LP model. Based on the degeneracy of the influence matrix, the model is first compressed into lower dimension for optimization, and then back-projected to reconstruct the beam weight. After beam weight optimization, the number of beams is reduced by removing the beams with low weight, and optimizing the weights of the remaining beams using the same model. This beam reduction technique is further validated by a mixed integer programming (MIP) model. The SVDLP approach was tested on a lung case. The results demonstrate that the SVD acceleration technique speeds up the optimization by a factor of 4.8. Furthermore, the beam reduction achieves a similar plan quality to the globally optimal plan obtained by the MIP model, but is one to two orders of magnitude faster. Furthermore, the SVDLP
Directory of Open Access Journals (Sweden)
GHOLAMIAN, A. S.
2009-06-01
Full Text Available In this paper, a magnet shape optimization method for reduction of cogging torque and torque ripple in Permanent Magnet (PM brushless DC motors is presented by using the reduced basis technique coupled by finite element and design of experiments methods. The primary objective of the method is to reduce the enormous number of design variables required to define the magnet shape. The reduced basis technique is a weighted combination of several basis shapes. The aim of the method is to find the best combination using the weights for each shape as the design variables. A multi-level design process is developed to find suitable basis shapes or trial shapes at each level that can be used in the reduced basis technique. Each level is treated as a separated optimization problem until the required objective is achieved. The experimental design of Taguchi method is used to build the approximation model and to perform optimization. This method is demonstrated on the magnet shape optimization of a 6-poles/18-slots PM BLDC motor.
A characteristic study of CCF modeling techniques and optimization of CCF defense strategies
International Nuclear Information System (INIS)
Kim, Min Chull
2000-02-01
Common Cause Failures (CCFs ) are among the major contributors to risk and core damage frequency (CDF ) from operating nuclear power plants (NPPs ). Our study on CCF focused on the following aspects : 1) a characteristic study on the CCF modeling techniques and 2) development of the optimal CCF defense strategy. Firstly, the characteristics of CCF modeling techniques were studied through sensitivity study of CCF occurrence probability upon system redundancy. The modeling techniques considered in this study include those most widely used worldwide, i.e., beta factor, MGL, alpha factor, and binomial failure rate models. We found that MGL and alpha factor models are essentially identical in terms of the CCF probability. Secondly, in the study for CCF defense, the various methods identified in the previous studies for defending against CCF were classified into five different categories. Based on these categories, we developed a generic method by which the optimal CCF defense strategy can be selected. The method is not only qualitative but also quantitative in nature: the selection of the optimal strategy among candidates is based on the use of analytic hierarchical process (AHP). We applied this method to two motor-driven valves for containment sump isolation in Ulchin 3 and 4 nuclear power plants. The result indicates that the method for developing an optimal CCF defense strategy is effective
Loading pattern optimization by multi-objective simulated annealing with screening technique
International Nuclear Information System (INIS)
Tong, K. P.; Hyun, C. L.; Hyung, K. J.; Chang, H. K.
2006-01-01
This paper presents a new multi-objective function which is made up of the main objective term as well as penalty terms related to the constraints. All the terms are represented in the same functional form and the coefficient of each term is normalized so that each term has equal weighting in the subsequent simulated annealing optimization calculations. The screening technique introduced in the previous work is also adopted in order to save computer time in 3-D neutronics evaluation of trial loading patterns. For numerical test of the new multi-objective function in the loading pattern optimization, the optimum loading patterns for the initial and the cycle 7 reload PWR core of Yonggwang Unit 4 are calculated by the simulated annealing algorithm with screening technique. A total of 10 optimum loading patterns are obtained for the initial core through 10 independent simulated annealing optimization runs. For the cycle 7 reload core one optimum loading pattern has been obtained from a single simulated annealing optimization run. More SA optimization runs will be conducted to optimum loading patterns for the cycle 7 reload core and results will be presented in the further work. (authors)
A study of optimization techniques in HDR brachytherapy for the prostate
Pokharel, Ghana Shyam
. Based on our study, DVH based objective function performed better than traditional variance based objective function in creating a clinically acceptable plan when executed under identical conditions. Thirdly, we studied the multiobjective optimization strategy using both DVH and variance based objective functions. The optimization strategy was to create several Pareto optimal solutions by scanning the clinically relevant part of the Pareto front. This strategy was adopted to decouple optimization from decision such that user could select final solution from the pool of alternative solutions based on his/her clinical goals. The overall quality of treatment plan improved using this approach compared to traditional class solution approach. In fact, the final optimized plan selected using decision engine with DVH based objective was comparable to typical clinical plan created by an experienced physicist. Next, we studied the hybrid technique comprising both stochastic and deterministic algorithm to optimize both dwell positions and dwell times. The simulated annealing algorithm was used to find optimal catheter distribution and the DVH based algorithm was used to optimize 3D dose distribution for given catheter distribution. This unique treatment planning and optimization tool was capable of producing clinically acceptable highly reproducible treatment plans in clinically reasonable time. As this algorithm was able to create clinically acceptable plans within clinically reasonable time automatically, it is really appealing for real time procedures. Next, we studied the feasibility of multiobjective optimization using evolutionary algorithm for real time HDR brachytherapy for the prostate. The algorithm with properly tuned algorithm specific parameters was able to create clinically acceptable plans within clinically reasonable time. However, the algorithm was let to run just for limited number of generations not considered optimal, in general, for such algorithms. This was
Radioactive tracer technique in process optimization: applications in the chemical industry
International Nuclear Information System (INIS)
Charlton, J.S.
1989-01-01
Process optimization is concerned with the selection of the most appropriate technological design of the process and with controlling its operation to obtain maximum benefit. The role of radioactive tracers in process optimization is discussed and the various circumstances under which such techniques may be beneficially applied are identified. Case studies are presented which illustrate how radioisotopes may be used to monitor plant performance under dynamic conditions to improve production efficiency and to investigate the cause of production limitations. In addition, the use of sealed sources to provide information complementary to the tracer study is described. (author)
Analysis on the Metrics used in Optimizing Electronic Business based on Learning Techniques
Directory of Open Access Journals (Sweden)
Irina-Steliana STAN
2014-09-01
Full Text Available The present paper proposes a methodology of analyzing the metrics related to electronic business. The drafts of the optimizing models include KPIs that can highlight the business specific, if only they are integrated by using learning-based techniques. Having set the most important and high-impact elements of the business, the models should get in the end the link between them, by automating business flows. The human resource will be found in the situation of collaborating more and more with the optimizing models which will translate into high quality decisions followed by profitability increase.
Linear triangular optimization technique and pricing scheme in residential energy management systems
Anees, Amir; Hussain, Iqtadar; AlKhaldi, Ali Hussain; Aslam, Muhammad
2018-06-01
This paper presents a new linear optimization algorithm for power scheduling of electric appliances. The proposed system is applied in a smart home community, in which community controller acts as a virtual distribution company for the end consumers. We also present a pricing scheme between community controller and its residential users based on real-time pricing and likely block rates. The results of the proposed optimization algorithm demonstrate that by applying the anticipated technique, not only end users can minimise the consumption cost, but it can also reduce the power peak to an average ratio which will be beneficial for the utilities as well.
Familiarity and recollection in heuristic decision making.
Schwikert, Shane R; Curran, Tim
2014-12-01
Heuristics involve the ability to utilize memory to make quick judgments by exploiting fundamental cognitive abilities. In the current study we investigated the memory processes that contribute to the recognition heuristic and the fluency heuristic, which are both presumed to capitalize on the byproducts of memory to make quick decisions. In Experiment 1, we used a city-size comparison task while recording event-related potentials (ERPs) to investigate the potential contributions of familiarity and recollection to the 2 heuristics. ERPs were markedly different for recognition heuristic-based decisions and fluency heuristic-based decisions, suggesting a role for familiarity in the recognition heuristic and recollection in the fluency heuristic. In Experiment 2, we coupled the same city-size comparison task with measures of subjective preexperimental memory for each stimulus in the task. Although previous literature suggests the fluency heuristic relies on recognition speed alone, our results suggest differential contributions of recognition speed and recollected knowledge to these decisions, whereas the recognition heuristic relies on familiarity. Based on these results, we created a new theoretical framework that explains decisions attributed to both heuristics based on the underlying memory associated with the choice options. PsycINFO Database Record (c) 2014 APA, all rights reserved.
Tuning Parameters in Heuristics by Using Design of Experiments Methods
Arin, Arif; Rabadi, Ghaith; Unal, Resit
2010-01-01
With the growing complexity of today's large scale problems, it has become more difficult to find optimal solutions by using exact mathematical methods. The need to find near-optimal solutions in an acceptable time frame requires heuristic approaches. In many cases, however, most heuristics have several parameters that need to be "tuned" before they can reach good results. The problem then turns into "finding best parameter setting" for the heuristics to solve the problems efficiently and timely. One-Factor-At-a-Time (OFAT) approach for parameter tuning neglects the interactions between parameters. Design of Experiments (DOE) tools can be instead employed to tune the parameters more effectively. In this paper, we seek the best parameter setting for a Genetic Algorithm (GA) to solve the single machine total weighted tardiness problem in which n jobs must be scheduled on a single machine without preemption, and the objective is to minimize the total weighted tardiness. Benchmark instances for the problem are available in the literature. To fine tune the GA parameters in the most efficient way, we compare multiple DOE models including 2-level (2k ) full factorial design, orthogonal array design, central composite design, D-optimal design and signal-to-noise (SIN) ratios. In each DOE method, a mathematical model is created using regression analysis, and solved to obtain the best parameter setting. After verification runs using the tuned parameter setting, the preliminary results for optimal solutions of multiple instances were found efficiently.
Special relativity a heuristic approach
Hassani, Sadri
2017-01-01
Special Relativity: A Heuristic Approach provides a qualitative exposition of relativity theory on the basis of the constancy of the speed of light. Using Einstein's signal velocity as the defining idea for the notion of simultaneity and the fact that the speed of light is independent of the motion of its source, chapters delve into a qualitative exposition of the relativity of time and length, discuss the time dilation formula using the standard light clock, explore the Minkowski four-dimensional space-time distance based on how the time dilation formula is derived, and define the components of the two-dimensional space-time velocity, amongst other topics. Provides a heuristic derivation of the Minkowski distance formula Uses relativistic photography to see Lorentz transformation and vector algebra manipulation in action Includes worked examples to elucidate and complement the topic being discussed Written in a very accessible style
Optimal Turbine Allocation for Offshore and Onshore Wind Farms
DEFF Research Database (Denmark)
Fischetti, Martina; Fischetti, Matteo; Monaci, Michele
2016-01-01
. In particular, lots of money and energy are spent on the optimal design of wind farms, as an efficient use of the available resources is instrumental for their economical success. In the present paper we address the optimization of turbine positions, which is one of the most relevant problems in the design...... of a wind farm, and propose a heuristic approach based on Mixed-Integer Linear Programming techniques. Computational results on very large scale instances prove the practical viability of the approach....
Triple Modular Redundancy verification via heuristic netlist analysis
Directory of Open Access Journals (Sweden)
Giovanni Beltrame
2015-08-01
Full Text Available Triple Modular Redundancy (TMR is a common technique to protect memory elements for digital processing systems subject to radiation effects (such as in space, high-altitude, or near nuclear sources. This paper presents an approach to verify the correct implementation of TMR for the memory elements of a given netlist (i.e., a digital circuit specification using heuristic analysis. The purpose is detecting any issues that might incur during the use of automatic tools for TMR insertion, optimization, place and route, etc. Our analysis does not require a testbench and can perform full, exhaustive coverage within less than an hour even for large designs. This is achieved by applying a divide et impera approach, splitting the circuit into smaller submodules without loss of generality, instead of applying formal verification to the whole netlist at once. The methodology has been applied to a production netlist of the LEON2-FT processor that had reported errors during radiation testing, successfully showing a number of unprotected memory elements, namely 351 flip-flops.
A Preconditioning Technique for First-Order Primal-Dual Splitting Method in Convex Optimization
Directory of Open Access Journals (Sweden)
Meng Wen
2017-01-01
Full Text Available We introduce a preconditioning technique for the first-order primal-dual splitting method. The primal-dual splitting method offers a very general framework for solving a large class of optimization problems arising in image processing. The key idea of the preconditioning technique is that the constant iterative parameters are updated self-adaptively in the iteration process. We also give a simple and easy way to choose the diagonal preconditioners while the convergence of the iterative algorithm is maintained. The efficiency of the proposed method is demonstrated on an image denoising problem. Numerical results show that the preconditioned iterative algorithm performs better than the original one.
Artificial intelligence search techniques for optimization of the cold source geometry
International Nuclear Information System (INIS)
Azmy, Y.Y.
1988-01-01
Most optimization studies of cold neutron sources have concentrated on the numerical prediction or experimental measurement of the cold moderator optimum thickness which produces the largest cold neutron leakage for a given thermal neutron source. Optimizing the geometrical shape of the cold source, however, is a more difficult problem because the optimized quantity, the cold neutron leakage, is an implicit function of the shape which is the unknown in such a study. We draw an analogy between this problem and a state space search, then we use a simple Artificial Intelligence (AI) search technique to determine the optimum cold source shape based on a two-group, r-z diffusion model. We implemented this AI design concept in the computer program AID which consists of two modules, a physical model module and a search module, which can be independently modified, improved, or made more sophisticated. 7 refs., 1 fig
Artificial intelligence search techniques for the optimization of cold source geometry
International Nuclear Information System (INIS)
Azmy, Y.Y.
1988-01-01
Most optimization studies of cold neutron sources have concentrated on the numerical prediction or experimental measurement of the cold moderator optimum thickness that produces the largest cold neutron leakage for a given thermal neutron source. Optimizing the geometric shape of the cold source, however, is a more difficult problem because the optimized quantity, the cold neutron leakage, is an implicit function of the shape, which is the unknown in such a study. An analogy is drawn between this problem and a state space search, then a simple artificial intelligence (AI) search technique is used to determine the optimum cold source shape based on a two-group, r-z diffusion model. This AI design concept was implemented in the computer program AID, which consists of two modules, a physical model module, and a search module, which can be independently modified, improved, or made more sophisticated
Active load sharing technique for on-line efficiency optimization in DC microgrids
DEFF Research Database (Denmark)
Sanseverino, E. Riva; Zizzo, G.; Boscaino, V.
2017-01-01
Recently, DC power distribution is gaining more and more importance over its AC counterpart achieving increased efficiency, greater flexibility, reduced volumes and capital cost. In this paper, a 24-120-325V two-level DC distribution system for home appliances, each including three parallel DC......-DC converters, is modeled. An active load sharing technique is proposed for the on-line optimization of the global efficiency of the DC distribution network. The algorithm aims at the instantaneous efficiency optimization of the whole DC network, based on the on-line load current sampling. A Look Up Table......, is created to store the real efficiencies of the converters taking into account components tolerances. A MATLAB/Simulink model of the DC distribution network has been set up and a Genetic Algorithm has been employed for the global efficiency optimization. Simulation results are shown to validate the proposed...
Optimization models and techniques for implementation and pricing of electricity markets
International Nuclear Information System (INIS)
Madrigal Martinez, M.
2001-01-01
The operation and planning of vertically integrated electric power systems can be optimized using models that simulate solutions to problems. As the electric power industry is going through a period of restructuring, there is a need for new optimization tools. This thesis describes the importance of optimization tools and presents techniques for implementing them. It also presents methods for pricing primary electricity markets. Three modeling groups are studied. The first considers a simplified continuous and discrete model for power pool auctions. The second considers the unit commitment problem, and the third makes use of a new type of linear network-constrained clearing system model for daily markets for power and spinning reserve. The newly proposed model considers bids for supply and demand and bilateral contracts. It is a direct current model for the transmission network
International Nuclear Information System (INIS)
Tong, S.S.; Powell, D.; Goel, S.
1992-02-01
A new software system called Engineous combines artificial intelligence and numerical methods for the design and optimization of complex aerospace systems. Engineous combines the advanced computational techniques of genetic algorithms, expert systems, and object-oriented programming with the conventional methods of numerical optimization and simulated annealing to create a design optimization environment that can be applied to computational models in various disciplines. Engineous has produced designs with higher predicted performance gains that current manual design processes - on average a 10-to-1 reduction of turnaround time - and has yielded new insights into product design. It has been applied to the aerodynamic preliminary design of an aircraft engine turbine, concurrent aerodynamic and mechanical preliminary design of an aircraft engine turbine blade and disk, a space superconductor generator, a satellite power converter, and a nuclear-powered satellite reactor and shield. 23 refs
A reduced scale two loop PWR core designed with particle swarm optimization technique
International Nuclear Information System (INIS)
Lima Junior, Carlos A. Souza; Pereira, Claudio M.N.A; Lapa, Celso M.F.; Cunha, Joao J.; Alvim, Antonio C.M.
2007-01-01
Reduced scale experiments are often employed in engineering projects because they are much cheaper than real scale testing. Unfortunately, designing reduced scale thermal-hydraulic circuit or equipment, with the capability of reproducing, both accurately and simultaneously, all physical phenomena that occur in real scale and at operating conditions, is a difficult task. To solve this problem, advanced optimization techniques, such as Genetic Algorithms, have been applied. Following this research line, we have performed investigations, using the Particle Swarm Optimization (PSO) Technique, to design a reduced scale two loop Pressurized Water Reactor (PWR) core, considering 100% of nominal power and non accidental operating conditions. Obtained results show that the proposed methodology is a promising approach for forced flow reduced scale experiments. (author)
Comparing the performance of different meta-heuristics for unweighted parallel machine scheduling
Directory of Open Access Journals (Sweden)
Adamu, Mumuni Osumah
2015-08-01
Full Text Available This article considers the due window scheduling problem to minimise the number of early and tardy jobs on identical parallel machines. This problem is known to be NP complete and thus finding an optimal solution is unlikely. Three meta-heuristics and their hybrids are proposed and extensive computational experiments are conducted. The purpose of this paper is to compare the performance of these meta-heuristics and their hybrids and to determine the best among them. Detailed comparative tests have also been conducted to analyse the different heuristics with the simulated annealing hybrid giving the best result.
Directory of Open Access Journals (Sweden)
Mehiddin Al-Baali
2015-12-01
Full Text Available We deal with the design of parallel algorithms by using variable partitioning techniques to solve nonlinear optimization problems. We propose an iterative solution method that is very efficient for separable functions, our scope being to discuss its performance for general functions. Experimental results on an illustrative example have suggested some useful modifications that, even though they improve the efficiency of our parallel method, leave some questions open for further investigation.
Wroblewski, David [Mentor, OH; Katrompas, Alexander M [Concord, OH; Parikh, Neel J [Richmond Heights, OH
2009-09-01
A method and apparatus for optimizing the operation of a power generating plant using artificial intelligence techniques. One or more decisions D are determined for at least one consecutive time increment, where at least one of the decisions D is associated with a discrete variable for the operation of a power plant device in the power generating plant. In an illustrated embodiment, the power plant device is a soot cleaning device associated with a boiler.
A novel heuristic algorithm for capacitated vehicle routing problem
Kır, Sena; Yazgan, Harun Reşit; Tüncel, Emre
2017-09-01
The vehicle routing problem with the capacity constraints was considered in this paper. It is quite difficult to achieve an optimal solution with traditional optimization methods by reason of the high computational complexity for large-scale problems. Consequently, new heuristic or metaheuristic approaches have been developed to solve this problem. In this paper, we constructed a new heuristic algorithm based on the tabu search and adaptive large neighborhood search (ALNS) with several specifically designed operators and features to solve the capacitated vehicle routing problem (CVRP). The effectiveness of the proposed algorithm was illustrated on the benchmark problems. The algorithm provides a better performance on large-scaled instances and gained advantage in terms of CPU time. In addition, we solved a real-life CVRP using the proposed algorithm and found the encouraging results by comparison with the current situation that the company is in.
Multiple sensitive estimation and optimal sample size allocation in the item sum technique.
Perri, Pier Francesco; Rueda García, María Del Mar; Cobo Rodríguez, Beatriz
2018-01-01
For surveys of sensitive issues in life sciences, statistical procedures can be used to reduce nonresponse and social desirability response bias. Both of these phenomena provoke nonsampling errors that are difficult to deal with and can seriously flaw the validity of the analyses. The item sum technique (IST) is a very recent indirect questioning method derived from the item count technique that seeks to procure more reliable responses on quantitative items than direct questioning while preserving respondents' anonymity. This article addresses two important questions concerning the IST: (i) its implementation when two or more sensitive variables are investigated and efficient estimates of their unknown population means are required; (ii) the determination of the optimal sample size to achieve minimum variance estimates. These aspects are of great relevance for survey practitioners engaged in sensitive research and, to the best of our knowledge, were not studied so far. In this article, theoretical results for multiple estimation and optimal allocation are obtained under a generic sampling design and then particularized to simple random sampling and stratified sampling designs. Theoretical considerations are integrated with a number of simulation studies based on data from two real surveys and conducted to ascertain the efficiency gain derived from optimal allocation in different situations. One of the surveys concerns cannabis consumption among university students. Our findings highlight some methodological advances that can be obtained in life sciences IST surveys when optimal allocation is achieved. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Search method optimization technique for thermal design of high power RFQ structure
International Nuclear Information System (INIS)
Sharma, N.K.; Joshi, S.C.
2009-01-01
RRCAT has taken up the development of 3 MeV RFQ structure for the low energy part of 100 MeV H - ion injector linac. RFQ is a precision machined resonating structure designed for high rf duty factor. RFQ structural stability during high rf power operation is an important design issue. The thermal analysis of RFQ has been performed using ANSYS finite element analysis software and optimization of various parameters is attempted using Search Method optimization technique. It is an effective optimization technique for the systems governed by a large number of independent variables. The method involves examining a number of combinations of values of independent variables and drawing conclusions from the magnitude of the objective function at these combinations. In these methods there is a continuous improvement in the objective function throughout the course of the search and hence these methods are very efficient. The method has been employed in optimization of various parameters (called independent variables) of RFQ like cooling water flow rate, cooling water inlet temperatures, cavity thickness etc. involved in RFQ thermal design. The temperature rise within RFQ structure is the objective function during the thermal design. Using ANSYS Programming Development Language (APDL), various multiple iterative programmes are written and the analysis are performed to minimize the objective function. The dependency of the objective function on various independent variables is established and the optimum values of the parameters are evaluated. The results of the analysis are presented in the paper. (author)
Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem.
Nallaperuma, Samadhi; Neumann, Frank; Sudholt, Dirk
2017-01-01
Randomized search heuristics are frequently applied to NP-hard combinatorial optimization problems. The runtime analysis of randomized search heuristics has contributed tremendously to our theoretical understanding. Recently, randomized search heuristics have been examined regarding their achievable progress within a fixed-time budget. We follow this approach and present a fixed-budget analysis for an NP-hard combinatorial optimization problem. We consider the well-known Traveling Salesperson Problem (TSP) and analyze the fitness increase that randomized search heuristics are able to achieve within a given fixed-time budget. In particular, we analyze Manhattan and Euclidean TSP instances and Randomized Local Search (RLS), (1+1) EA and (1+[Formula: see text]) EA algorithms for the TSP in a smoothed complexity setting, and derive the lower bounds of the expected fitness gain for a specified number of generations.
Directory of Open Access Journals (Sweden)
Zhiwei Ye
2015-01-01
Full Text Available Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.
Ye, Zhiwei; Wang, Mingwei; Hu, Zhengbing; Liu, Wei
2015-01-01
Image enhancement is an important procedure of image processing and analysis. This paper presents a new technique using a modified measure and blending of cuckoo search and particle swarm optimization (CS-PSO) for low contrast images to enhance image adaptively. In this way, contrast enhancement is obtained by global transformation of the input intensities; it employs incomplete Beta function as the transformation function and a novel criterion for measuring image quality considering three factors which are threshold, entropy value, and gray-level probability density of the image. The enhancement process is a nonlinear optimization problem with several constraints. CS-PSO is utilized to maximize the objective fitness criterion in order to enhance the contrast and detail in an image by adapting the parameters of a novel extension to a local enhancement technique. The performance of the proposed method has been compared with other existing techniques such as linear contrast stretching, histogram equalization, and evolutionary computing based image enhancement methods like backtracking search algorithm, differential search algorithm, genetic algorithm, and particle swarm optimization in terms of processing time and image quality. Experimental results demonstrate that the proposed method is robust and adaptive and exhibits the better performance than other methods involved in the paper.
Wieberger, Florian; Kolb, Tristan; Neuber, Christian; Ober, Christopher K; Schmidt, Hans-Werner
2013-04-08
In this article we present several developed and improved combinatorial techniques to optimize processing conditions and material properties of organic thin films. The combinatorial approach allows investigations of multi-variable dependencies and is the perfect tool to investigate organic thin films regarding their high performance purposes. In this context we develop and establish the reliable preparation of gradients of material composition, temperature, exposure, and immersion time. Furthermore we demonstrate the smart application of combinations of composition and processing gradients to create combinatorial libraries. First a binary combinatorial library is created by applying two gradients perpendicular to each other. A third gradient is carried out in very small areas and arranged matrix-like over the entire binary combinatorial library resulting in a ternary combinatorial library. Ternary combinatorial libraries allow identifying precise trends for the optimization of multi-variable dependent processes which is demonstrated on the lithographic patterning process. Here we verify conclusively the strong interaction and thus the interdependency of variables in the preparation and properties of complex organic thin film systems. The established gradient preparation techniques are not limited to lithographic patterning. It is possible to utilize and transfer the reported combinatorial techniques to other multi-variable dependent processes and to investigate and optimize thin film layers and devices for optical, electro-optical, and electronic applications.
Directory of Open Access Journals (Sweden)
Hans-Werner Schmidt
2013-04-01
Full Text Available In this article we present several developed and improved combinatorial techniques to optimize processing conditions and material properties of organic thin films. The combinatorial approach allows investigations of multi-variable dependencies and is the perfect tool to investigate organic thin films regarding their high performance purposes. In this context we develop and establish the reliable preparation of gradients of material composition, temperature, exposure, and immersion time. Furthermore we demonstrate the smart application of combinations of composition and processing gradients to create combinatorial libraries. First a binary combinatorial library is created by applying two gradients perpendicular to each other. A third gradient is carried out in very small areas and arranged matrix-like over the entire binary combinatorial library resulting in a ternary combinatorial library. Ternary combinatorial libraries allow identifying precise trends for the optimization of multi-variable dependent processes which is demonstrated on the lithographic patterning process. Here we verify conclusively the strong interaction and thus the interdependency of variables in the preparation and properties of complex organic thin film systems. The established gradient preparation techniques are not limited to lithographic patterning. It is possible to utilize and transfer the reported combinatorial techniques to other multi-variable dependent processes and to investigate and optimize thin film layers and devices for optical, electro-optical, and electronic applications.
Heuristic Scheduling Algorithm Oriented Dynamic Tasks for Imaging Satellites
Directory of Open Access Journals (Sweden)
Maocai Wang
2014-01-01
Full Text Available Imaging satellite scheduling is an NP-hard problem with many complex constraints. This paper researches the scheduling problem for dynamic tasks oriented to some emergency cases. After the dynamic properties of satellite scheduling were analyzed, the optimization model is proposed in this paper. Based on the model, two heuristic algorithms are proposed to solve the problem. The first heuristic algorithm arranges new tasks by inserting or deleting them, then inserting them repeatedly according to the priority from low to high, which is named IDI algorithm. The second one called ISDR adopts four steps: insert directly, insert by shifting, insert by deleting, and reinsert the tasks deleted. Moreover, two heuristic factors, congestion degree of a time window and the overlapping degree of a task, are employed to improve the algorithm’s performance. Finally, a case is given to test the algorithms. The results show that the IDI algorithm is better than ISDR from the running time point of view while ISDR algorithm with heuristic factors is more effective with regard to algorithm performance. Moreover, the results also show that our method has good performance for the larger size of the dynamic tasks in comparison with the other two methods.
Bandaru, Sunith; Deb, Kalyanmoy
2011-09-01
In this article, a methodology is proposed for automatically extracting innovative design principles which make a system or process (subject to conflicting objectives) optimal using its Pareto-optimal dataset. Such 'higher knowledge' would not only help designers to execute the system better, but also enable them to predict how changes in one variable would affect other variables if the system has to retain its optimal behaviour. This in turn would help solve other similar systems with different parameter settings easily without the need to perform a fresh optimization task. The proposed methodology uses a clustering-based optimization technique and is capable of discovering hidden functional relationships between the variables, objective and constraint functions and any other function that the designer wishes to include as a 'basis function'. A number of engineering design problems are considered for which the mathematical structure of these explicit relationships exists and has been revealed by a previous study. A comparison with the multivariate adaptive regression splines (MARS) approach reveals the practicality of the proposed approach due to its ability to find meaningful design principles. The success of this procedure for automated innovization is highly encouraging and indicates its suitability for further development in tackling more complex design scenarios.
Vilas, Carlos; Balsa-Canto, Eva; García, Maria-Sonia G; Banga, Julio R; Alonso, Antonio A
2012-07-02
Systems biology allows the analysis of biological systems behavior under different conditions through in silico experimentation. The possibility of perturbing biological systems in different manners calls for the design of perturbations to achieve particular goals. Examples would include, the design of a chemical stimulation to maximize the amplitude of a given cellular signal or to achieve a desired pattern in pattern formation systems, etc. Such design problems can be mathematically formulated as dynamic optimization problems which are particularly challenging when the system is described by partial differential equations.This work addresses the numerical solution of such dynamic optimization problems for spatially distributed biological systems. The usual nonlinear and large scale nature of the mathematical models related to this class of systems and the presence of constraints on the optimization problems, impose a number of difficulties, such as the presence of suboptimal solutions, which call for robust and efficient numerical techniques. Here, the use of a control vector parameterization approach combined with efficient and robust hybrid global optimization methods and a reduced order model methodology is proposed. The capabilities of this strategy are illustrated considering the solution of a two challenging problems: bacterial chemotaxis and the FitzHugh-Nagumo model. In the process of chemotaxis the objective was to efficiently compute the time-varying optimal concentration of chemotractant in one of the spatial boundaries in order to achieve predefined cell distribution profiles. Results are in agreement with those previously published in the literature. The FitzHugh-Nagumo problem is also efficiently solved and it illustrates very well how dynamic optimization may be used to force a system to evolve from an undesired to a desired pattern with a reduced number of actuators. The presented methodology can be used for the efficient dynamic optimization of
Greedy heuristics for minimization of number of terminal nodes in decision trees
Hussain, Shahid
2014-10-01
This paper describes, in detail, several greedy heuristics for construction of decision trees. We study the number of terminal nodes of decision trees, which is closely related with the cardinality of the set of rules corresponding to the tree. We compare these heuristics empirically for two different types of datasets (datasets acquired from UCI ML Repository and randomly generated data) as well as compare with the optimal results obtained using dynamic programming method.
Greedy heuristics for minimization of number of terminal nodes in decision trees
Hussain, Shahid
2014-01-01
This paper describes, in detail, several greedy heuristics for construction of decision trees. We study the number of terminal nodes of decision trees, which is closely related with the cardinality of the set of rules corresponding to the tree. We compare these heuristics empirically for two different types of datasets (datasets acquired from UCI ML Repository and randomly generated data) as well as compare with the optimal results obtained using dynamic programming method.
A MILP-based heuristic for a commercial train timetabling problem
Gestrelius, Sara; Aronsson, Martin; Peterson, Anders
2017-01-01
Using mathematical methods to support the yearly timetable planning process has many advantages. Unfortunately, the train timetabling problem for large geographical areas and many trains is intractable for optimization models alone. In this paper, we therefore present a MILP-based heuristic that has been designed to generate good-enough timetables for large geographical areas and many trains. In the incremental fix and release heuristic (IFRH), trains are added to the timetable in batches. Fo...
Methods of modeling and optimization of work effects for chosen mineral processing systems
Directory of Open Access Journals (Sweden)
Tomasz Niedoba
2005-11-01
Full Text Available The methods being used in the mineral processing modeling are reviewed in this paper. Particularly, the heuristic approach was presented. The new, modern techniques of modeling and optimization were proposed, including the least median squares method and genetic algorithms. The rules of the latter were described in details.
International Nuclear Information System (INIS)
Schoenbrod, Betina; Quispe, Benjamin; Cattaneo, Alberto; Rodriguez, Ivanna; Chocron, Mauricio; Farias, Silvia
2012-09-01
Atucha II NPP is a Pressurized Vessel Heavy Water Reactor (PVHWR) of 740 MWe designed by SIEMENSKWU. After some years of delay, this NPP is in advanced construction state, being the beginning of commercial operation expected for 2013. Nucleoelectrica Argentina (N.A.S.A.) is the company in charge of the finalization of this project and the future operation of the plant. The Comision Nacional de Energia Atomica (C.N.E.A.) is the R and D nuclear institution in the country that, among many other topics, provides technical support to the stations. The Commissioning Chemistry Division of CNAII is in charge of the commissioning of the demineralization water plant and the organization of the chemical laboratory. The water plant started operating successfully in July 2010 and is providing the plant with nuclear grade purity water. Currently, in the conventional ('cold') laboratory several activities are taking place. On one hand, analytical techniques for the future operation of the plant are being tested and optimized. On the other hand, the laboratory is participating in the cleaning and conservation of the different components of the plant, providing technical support and the necessary analysis. To define the analytical techniques for the normal operation of the plant, the parameters to be measured and their range were established in the Chemistry Manual. The necessary equipment and reagents were bought. In this work, a summary of the analytical techniques that are being implemented and optimized is presented. Common anions (chloride, sulfate, fluoride, bromide and nitrate) are analyzed by ion chromatography. Cations, mainly sodium, are determined by absorption spectrometry. A UV-Vis spectrometer is used to determine silicates, iron, ammonia, DQO, total solids, true color and turbidity. TOC measurements are performed with a TOC analyzer. To optimize the methods, several parameters are evaluated: linearity, detection and quantification limits, precision and
Determination of the optimal tolerance for MLC positioning in sliding window and VMAT techniques
International Nuclear Information System (INIS)
Hernandez, V.; Abella, R.; Calvo, J. F.; Jurado-Bruggemann, D.; Sancho, I.; Carrasco, P.
2015-01-01
Purpose: Several authors have recommended a 2 mm tolerance for multileaf collimator (MLC) positioning in sliding window treatments. In volumetric modulated arc therapy (VMAT) treatments, however, the optimal tolerance for MLC positioning remains unknown. In this paper, the authors present the results of a multicenter study to determine the optimal tolerance for both techniques. Methods: The procedure used is based on dynalog file analysis. The study was carried out using seven Varian linear accelerators from five different centers. Dynalogs were collected from over 100 000 clinical treatments and in-house software was used to compute the number of tolerance faults as a function of the user-defined tolerance. Thus, the optimal value for this tolerance, defined as the lowest achievable value, was investigated. Results: Dynalog files accurately predict the number of tolerance faults as a function of the tolerance value, especially for low fault incidences. All MLCs behaved similarly and the Millennium120 and the HD120 models yielded comparable results. In sliding window techniques, the number of beams with an incidence of hold-offs >1% rapidly decreases for a tolerance of 1.5 mm. In VMAT techniques, the number of tolerance faults sharply drops for tolerances around 2 mm. For a tolerance of 2.5 mm, less than 0.1% of the VMAT arcs presented tolerance faults. Conclusions: Dynalog analysis provides a feasible method for investigating the optimal tolerance for MLC positioning in dynamic fields. In sliding window treatments, the tolerance of 2 mm was found to be adequate, although it can be reduced to 1.5 mm. In VMAT treatments, the typically used 5 mm tolerance is excessively high. Instead, a tolerance of 2.5 mm is recommended
DEFF Research Database (Denmark)
Wang, Yong; Cai, Zixing; Zhou, Yuren
2009-01-01
A novel approach to deal with numerical and engineering constrained optimization problems, which incorporates a hybrid evolutionary algorithm and an adaptive constraint-handling technique, is presented in this paper. The hybrid evolutionary algorithm simultaneously uses simplex crossover and two...... mutation operators to generate the offspring population. Additionally, the adaptive constraint-handling technique consists of three main situations. In detail, at each situation, one constraint-handling mechanism is designed based on current population state. Experiments on 13 benchmark test functions...... and four well-known constrained design problems verify the effectiveness and efficiency of the proposed method. The experimental results show that integrating the hybrid evolutionary algorithm with the adaptive constraint-handling technique is beneficial, and the proposed method achieves competitive...
Energy Technology Data Exchange (ETDEWEB)
Delahaye, P., E-mail: delahaye@ganil.fr; Jardin, P.; Maunoury, L. [GANIL, CEA/DSM-CNRS/IN2P3, Blvd. Becquerel, BP 55027, 14076 Caen Cedex 05 (France); Galatà, A.; Patti, G. [INFN–Laboratori Nazionali di Legnaro, Viale dell’Università 2, 35020 Legnaro (Padova) (Italy); Angot, J.; Lamy, T.; Thuillier, T. [LPSC–Université Grenoble Alpes–CNRS/IN2P3, 53 rue des Martyrs, 38026 Grenoble Cedex (France); Cam, J. F.; Traykov, E.; Ban, G. [LPC Caen, 6 Blvd. Maréchal Juin, 14050 Caen Cedex (France); Celona, L. [INFN–Laboratori Nazionali del Sud, via S. Sofia 62, 95125 Catania (Italy); Choinski, J.; Gmaj, P. [Heavy Ion Laboratory, University of Warsaw, ul. Pasteura 5a, 02 093 Warsaw (Poland); Koivisto, H.; Kolhinen, V.; Tarvainen, O. [Department of Physics, University of Jyväskylä, PB 35 (YFL), 40351 Jyväskylä (Finland); Vondrasek, R. [Argonne National Laboratory, 9700 S. Cass Avenue, Argonne, Illinois 60439 (United States); Wenander, F. [ISOLDE, CERN, 1211 Geneva 23 (Switzerland)
2016-02-15
The present paper summarizes the results obtained from the past few years in the framework of the Enhanced Multi-Ionization of short-Lived Isotopes for Eurisol (EMILIE) project. The EMILIE project aims at improving the charge breeding techniques with both Electron Cyclotron Resonance Ion Sources (ECRIS) and Electron Beam Ion Sources (EBISs) for European Radioactive Ion Beam (RIB) facilities. Within EMILIE, an original technique for debunching the beam from EBIS charge breeders is being developed, for making an optimal use of the capabilities of CW post-accelerators of the future facilities. Such a debunching technique should eventually resolve duty cycle and time structure issues which presently complicate the data-acquisition of experiments. The results of the first tests of this technique are reported here. In comparison with charge breeding with an EBIS, the ECRIS technique had lower performance in efficiency and attainable charge state for metallic ion beams and also suffered from issues related to beam contamination. In recent years, improvements have been made which significantly reduce the differences between the two techniques, making ECRIS charge breeding more attractive especially for CW machines producing intense beams. Upgraded versions of the Phoenix charge breeder, originally developed by LPSC, will be used at SPES and GANIL/SPIRAL. These two charge breeders have benefited from studies undertaken within EMILIE, which are also briefly summarized here.
A Hybrid Ant Colony Optimization Algorithm for the Extended Capacitated Arc Routing Problem.
Li-Ning Xing; Rohlfshagen, P; Ying-Wu Chen; Xin Yao
2011-08-01
The capacitated arc routing problem (CARP) is representative of numerous practical applications, and in order to widen its scope, we consider an extended version of this problem that entails both total service time and fixed investment costs. We subsequently propose a hybrid ant colony optimization (ACO) algorithm (HACOA) to solve instances of the extended CARP. This approach is characterized by the exploitation of heuristic information, adaptive parameters, and local optimization techniques: Two kinds of heuristic information, arc cluster information and arc priority information, are obtained continuously from the solutions sampled to guide the subsequent optimization process. The adaptive parameters ease the burden of choosing initial values and facilitate improved and more robust results. Finally, local optimization, based on the two-opt heuristic, is employed to improve the overall performance of the proposed algorithm. The resulting HACOA is tested on four sets of benchmark problems containing a total of 87 instances with up to 140 nodes and 380 arcs. In order to evaluate the effectiveness of the proposed method, some existing capacitated arc routing heuristics are extended to cope with the extended version of this problem; the experimental results indicate that the proposed ACO method outperforms these heuristics.
Heuristic Approach for Balancing Shift Schedules
International Nuclear Information System (INIS)
Kim, Dae Ho; Yun, Young Su; Lee, Yong Hee
2005-01-01
In this paper, a heuristic approach for balancing shift schedules is proposed. For the shift schedules, various constraints which have usually been considered in realworld industry are used, and the objective is to minimize the differences of the workloads in each workgroup. The constraints and objective function are implemented in the proposed heuristic approach. Using a simple instance, the efficiency of the proposed heuristic approach is proved
Artificial intelligent techniques for optimizing water allocation in a reservoir watershed
Chang, Fi-John; Chang, Li-Chiu; Wang, Yu-Chung
2014-05-01
This study proposes a systematical water allocation scheme that integrates system analysis with artificial intelligence techniques for reservoir operation in consideration of the great uncertainty upon hydrometeorology for mitigating droughts impacts on public and irrigation sectors. The AI techniques mainly include a genetic algorithm and adaptive-network based fuzzy inference system (ANFIS). We first derive evaluation diagrams through systematic interactive evaluations on long-term hydrological data to provide a clear simulation perspective of all possible drought conditions tagged with their corresponding water shortages; then search the optimal reservoir operating histogram using genetic algorithm (GA) based on given demands and hydrological conditions that can be recognized as the optimal base of input-output training patterns for modelling; and finally build a suitable water allocation scheme through constructing an adaptive neuro-fuzzy inference system (ANFIS) model with a learning of the mechanism between designed inputs (water discount rates and hydrological conditions) and outputs (two scenarios: simulated and optimized water deficiency levels). The effectiveness of the proposed approach is tested on the operation of the Shihmen Reservoir in northern Taiwan for the first paddy crop in the study area to assess the water allocation mechanism during drought periods. We demonstrate that the proposed water allocation scheme significantly and substantially avails water managers of reliably determining a suitable discount rate on water supply for both irrigation and public sectors, and thus can reduce the drought risk and the compensation amount induced by making restrictions on agricultural use water.
Andriani, Dian; Wresta, Arini; Atmaja, Tinton Dwi; Saepudin, Aep
2014-02-01
Biogas from anaerobic digestion of organic materials is a renewable energy resource that consists mainly of CH4 and CO2. Trace components that are often present in biogas are water vapor, hydrogen sulfide, siloxanes, hydrocarbons, ammonia, oxygen, carbon monoxide, and nitrogen. Considering the biogas is a clean and renewable form of energy that could well substitute the conventional source of energy (fossil fuels), the optimization of this type of energy becomes substantial. Various optimization techniques in biogas production process had been developed, including pretreatment, biotechnological approaches, co-digestion as well as the use of serial digester. For some application, the certain purity degree of biogas is needed. The presence of CO2 and other trace components in biogas could affect engine performance adversely. Reducing CO2 content will significantly upgrade the quality of biogas and enhancing the calorific value. Upgrading is generally performed in order to meet the standards for use as vehicle fuel or for injection in the natural gas grid. Different methods for biogas upgrading are used. They differ in functioning, the necessary quality conditions of the incoming gas, and the efficiency. Biogas can be purified from CO2 using pressure swing adsorption, membrane separation, physical or chemical CO2 absorption. This paper reviews the various techniques, which could be used to optimize the biogas production as well as to upgrade the biogas quality.
Heuristic Evaluation on Mobile Interfaces: A New Checklist
Yáñez Gómez, Rosa; Cascado Caballero, Daniel; Sevillano, José-Luis
2014-01-01
The rapid evolution and adoption of mobile devices raise new usability challenges, given their limitations (in screen size, battery life, etc.) as well as the specific requirements of this new interaction. Traditional evaluation techniques need to be adapted in order for these requirements to be met. Heuristic evaluation (HE), an Inspection Method based on evaluation conducted by experts over a real system or prototype, is based on checklists which are desktop-centred and do not adequately detect mobile-specific usability issues. In this paper, we propose a compilation of heuristic evaluation checklists taken from the existing bibliography but readapted to new mobile interfaces. Selecting and rearranging these heuristic guidelines offer a tool which works well not just for evaluation but also as a best-practices checklist. The result is a comprehensive checklist which is experimentally evaluated as a design tool. This experimental evaluation involved two software engineers without any specific knowledge about usability, a group of ten users who compared the usability of a first prototype designed without our heuristics, and a second one after applying the proposed checklist. The results of this experiment show the usefulness of the proposed checklist for avoiding usability gaps even with nontrained developers. PMID:25295300
Heuristic Evaluation on Mobile Interfaces: A New Checklist
Directory of Open Access Journals (Sweden)
Rosa Yáñez Gómez
2014-01-01
Full Text Available The rapid evolution and adoption of mobile devices raise new usability challenges, given their limitations (in screen size, battery life, etc. as well as the specific requirements of this new interaction. Traditional evaluation techniques need to be adapted in order for these requirements to be met. Heuristic evaluation (HE, an Inspection Method based on evaluation conducted by experts over a real system or prototype, is based on checklists which are desktop-centred and do not adequately detect mobile-specific usability issues. In this paper, we propose a compilation of heuristic evaluation checklists taken from the existing bibliography but readapted to new mobile interfaces. Selecting and rearranging these heuristic guidelines offer a tool which works well not just for evaluation but also as a best-practices checklist. The result is a comprehensive checklist which is experimentally evaluated as a design tool. This experimental evaluation involved two software engineers without any specific knowledge about usability, a group of ten users who compared the usability of a first prototype designed without our heuristics, and a second one after applying the proposed checklist. The results of this experiment show the usefulness of the proposed checklist for avoiding usability gaps even with nontrained developers.
Optimally Stopped Optimization
Vinci, Walter; Lidar, Daniel
We combine the fields of heuristic optimization and optimal stopping. We propose a strategy for benchmarking randomized optimization algorithms that minimizes the expected total cost for obtaining a good solution with an optimal number of calls to the solver. To do so, rather than letting the objective function alone define a cost to be minimized, we introduce a further cost-per-call of the algorithm. We show that this problem can be formulated using optimal stopping theory. The expected cost is a flexible figure of merit for benchmarking probabilistic solvers that can be computed when the optimal solution is not known, and that avoids the biases and arbitrariness that affect other measures. The optimal stopping formulation of benchmarking directly leads to a real-time, optimal-utilization strategy for probabilistic optimizers with practical impact. We apply our formulation to benchmark the performance of a D-Wave 2X quantum annealer and the HFS solver, a specialized classical heuristic algorithm designed for low tree-width graphs. On a set of frustrated-loop instances with planted solutions defined on up to N = 1098 variables, the D-Wave device is between one to two orders of magnitude faster than the HFS solver.
Social biases determine spatiotemporal sparseness of ciliate mating heuristics.
Clark, Kevin B
2012-01-01
Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate's initial subjective bias, responsiveness, or preparedness, as defined by Stevens' Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present
Social biases determine spatiotemporal sparseness of ciliate mating heuristics
2012-01-01
Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate’s initial subjective bias, responsiveness, or preparedness, as defined by Stevens’ Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The
International Nuclear Information System (INIS)
Hosseini-Ashrafi, M.E.; Bagherebadian, H.; Yahaqi, E.
1999-01-01
A method has been developed which, by using the geometric information from treatment sample cases, selects from a given data set an initial treatment plan as a step for treatment plan optimization. The method uses an artificial neural network (ANN) classification technique to select a best matching plan from the 'optimized' ANN database. Separate back-propagation ANN classifiers were trained using 50, 60 and 77 examples for three groups of treatment case classes (up to 21 examples from each class were used). The performance of the classifiers in selecting the correct treatment class was tested using the leave-one-out method; the networks were optimized with respect their architecture. For the three groups used in this study, successful classification fractions of 0.83, 0.98 and 0.93 were achieved by the optimized ANN classifiers. The automated response of the ANN may be used to arrive at a pre-plan where many treatment parameters may be identified and therefore a significant reduction in the steps required to arrive at the optimum plan may be achieved. Treatment planning 'experience' and also results from lengthy calculations may be used for training the ANN. (author)
A single cognitive heuristic process meets the complexity of domain-specific moral heuristics.
Dubljević, Veljko; Racine, Eric
2014-10-01
The inherence heuristic (a) offers modest insights into the complex nature of both the is-ought tension in moral reasoning and moral reasoning per se, and (b) does not reflect the complexity of domain-specific moral heuristics. Formal and general in nature, we contextualize the process described as "inherence heuristic" in a web of domain-specific heuristics (e.g., agent specific; action specific; consequences specific).
Hernandez, Wilmar
2007-01-01
In this paper a survey on recent applications of optimal signal processing techniques to improve the performance of mechanical sensors is made. Here, a comparison between classical filters and optimal filters for automotive sensors is made, and the current state of the art of the application of robust and optimal control and signal processing techniques to the design of the intelligent (or smart) sensors that today's cars need is presented through several experimental results that show that the fusion of intelligent sensors and optimal signal processing techniques is the clear way to go. However, the switch between the traditional methods of designing automotive sensors and the new ones cannot be done overnight because there are some open research issues that have to be solved. This paper draws attention to one of the open research issues and tries to arouse researcher's interest in the fusion of intelligent sensors and optimal signal processing techniques.
Heuristic Synthesis of Reversible Logic – A Comparative Study
Directory of Open Access Journals (Sweden)
Chua Shin Cheng
2014-01-01
Full Text Available Reversible logic circuits have been historically motivated by theoretical research in low-power, and recently attracted interest as components of the quantum algorithm, optical computing and nanotechnology. However due to the intrinsic property of reversible logic, traditional irreversible logic design and synthesis methods cannot be carried out. Thus a new set of algorithms are developed correctly to synthesize reversible logic circuit. This paper presents a comprehensive literature review with comparative study on heuristic based reversible logic synthesis. It reviews a range of heuristic based reversible logic synthesis techniques reported by researchers (BDD-based, cycle-based, search-based, non-search-based, rule-based, transformation-based, and ESOP-based. All techniques are described in detail and summarized in a table based on their features, limitation, library used and their consideration metric. Benchmark comparison of gate count and quantum cost are analysed for each synthesis technique. Comparing the synthesis algorithm outputs over the years, it can be observed that different approach has been used for the synthesis of reversible circuit. However, the improvements are not significant. Quantum cost and gate count has improved over the years, but arguments and debates are still on certain issues such as the issue of garbage outputs that remain the same. This paper provides the information of all heuristic based synthesis of reversible logic method proposed over the years. All techniques are explained in detail and thus informative for new reversible logic researchers and bridging the knowledge gap in this area.
Optimized scheduling technique of null subcarriers for peak power control in 3GPP LTE downlink.
Cho, Soobum; Park, Sang Kyu
2014-01-01
Orthogonal frequency division multiple access (OFDMA) is a key multiple access technique for the long term evolution (LTE) downlink. However, high peak-to-average power ratio (PAPR) can cause the degradation of power efficiency. The well-known PAPR reduction technique, dummy sequence insertion (DSI), can be a realistic solution because of its structural simplicity. However, the large usage of subcarriers for the dummy sequences may decrease the transmitted data rate in the DSI scheme. In this paper, a novel DSI scheme is applied to the LTE system. Firstly, we obtain the null subcarriers in single-input single-output (SISO) and multiple-input multiple-output (MIMO) systems, respectively; then, optimized dummy sequences are inserted into the obtained null subcarrier. Simulation results show that Walsh-Hadamard transform (WHT) sequence is the best for the dummy sequence and the ratio of 16 to 20 for the WHT and randomly generated sequences has the maximum PAPR reduction performance. The number of near optimal iteration is derived to prevent exhausted iterations. It is also shown that there is no bit error rate (BER) degradation with the proposed technique in LTE downlink system.
Directory of Open Access Journals (Sweden)
G. Senthilkumar
2014-09-01
Full Text Available In this work, transesterification of sunflower oil for obtaining biodiesel was studied. Taguchi’s methodology (L9 orthogonal array was selected to optimize the most significant variables (methanol, catalyst concentration and stirrer speed in transesterification process. Experiments have conducted based on development of L9 orthogonal array by using Taguchi technique. Analysis of Variance (ANOVA and the regression equations were used to find the optimum yield of sunflower methyl ester under the influence of methanol, catalyst & stirrer speed. The study resulted in a maximum yield of sun flower methyl ester as 96% with the optimal conditions of methanol 110 ml with 0.5% by wt. of sodium hydroxide (NaOH stirred at 1200 rpm. The yield was analyzed on the basis of “larger is better”. Finally, confirmation tests were carried out to verify the experimental results.
Development of a parameter optimization technique for the design of automatic control systems
Whitaker, P. H.
1977-01-01
Parameter optimization techniques for the design of linear automatic control systems that are applicable to both continuous and digital systems are described. The model performance index is used as the optimization criterion because of the physical insight that can be attached to it. The design emphasis is to start with the simplest system configuration that experience indicates would be practical. Design parameters are specified, and a digital computer program is used to select that set of parameter values which minimizes the performance index. The resulting design is examined, and complexity, through the use of more complex information processing or more feedback paths, is added only if performance fails to meet operational specifications. System performance specifications are assumed to be such that the desired step function time response of the system can be inferred.
Inverse Optimization and Forecasting Techniques Applied to Decision-making in Electricity Markets
DEFF Research Database (Denmark)
Saez Gallego, Javier
patterns that the load traditionally exhibited. On the other hand, this thesis is motivated by the decision-making processes of market players. In response to these challenges, this thesis provides mathematical models for decision-making under uncertainty in electricity markets. Demand-side bidding refers......This thesis deals with the development of new mathematical models that support the decision-making processes of market players. It addresses the problems of demand-side bidding, price-responsive load forecasting and reserve determination. From a methodological point of view, we investigate a novel...... approach to model the response of aggregate price-responsive load as a constrained optimization model, whose parameters are estimated from data by using inverse optimization techniques. The problems tackled in this dissertation are motivated, on one hand, by the increasing penetration of renewable energy...
Directory of Open Access Journals (Sweden)
Maria Oksa
2011-09-01
Full Text Available In this work High Velocity Oxy-fuel (HVOF thermal spray techniques, spraying process optimization, and characterization of coatings are reviewed. Different variants of the technology are described and the main differences in spray conditions in terms of particle kinetics and thermal energy are rationalized. Methods and tools for controlling the spray process are presented as well as their use in optimizing the coating process. It will be shown how the differences from the starting powder to the final coating formation affect the coating microstructure and performance. Typical properties of HVOF sprayed coatings and coating performance is described. Also development of testing methods used for the evaluation of coating properties and current status of standardization is presented. Short discussion of typical applications is done.
International Nuclear Information System (INIS)
Giniyatulin, R.N.; Komarov, V.L.; Kuzmin, E.G.; Makhankov, A.N.; Mazul, I.V.; Yablokov, N.A.; Zhuk, A.N.
2002-01-01
Joining of tungsten with copper-based cooling structure and armour geometry optimization are the major aspects in development of the tungsten-armoured plasma facing components (PFC). Fabrication techniques and high heat flux (HHF) tests of tungsten-armoured components have to reflect different PFC designs and acceptable manufacturing cost. The authors present the recent results of tungsten-armoured mock-ups development based on manufacturing and HHF tests. Two aspects were investigated--selection of armour geometry and examination of tungsten-copper bonding techniques. Brazing and casting tungsten-copper bonding techniques were used in small mock-ups. The mock-ups with armour tiles (20x5x10, 10x10x10, 20x20x10, 27x27x10) mm 3 in dimensions were tested by cyclic heat fluxes in the range of (5-20) MW/m 2 , the number of thermal cycles varied from hundreds to several thousands for each mock-up. The results of the tests show the applicability of different geometry and different bonding technique to corresponding heat loading. A medium-scale mock-up 0.6-m in length was manufactured and tested. HHF tests of the medium-scale mock-up have demonstrated the applicability of the applied bonding techniques and armour geometry for full-scale PFC's manufacturing
An Efficient Meta Heuristic Algorithm to Solve Economic Load Dispatch Problems
Directory of Open Access Journals (Sweden)
R Subramanian
2013-12-01
Full Text Available The Economic Load Dispatch (ELD problems in power generation systems are to reduce the fuel cost by reducing the total cost for the generation of electric power. This paper presents an efficient Modified Firefly Algorithm (MFA, for solving ELD Problem. The main objective of the problems is to minimize the total fuel cost of the generating units having quadratic cost functions subjected to limits on generator true power output and transmission losses. The MFA is a stochastic, Meta heuristic approach based on the idealized behaviour of the flashing characteristics of fireflies. This paper presents an application of MFA to ELD for six generator test case system. MFA is applied to ELD problem and compared its solution quality and computation efficiency to Genetic algorithm (GA, Differential Evolution (DE, Particle swarm optimization (PSO, Artificial Bee Colony optimization (ABC, Biogeography-Based Optimization (BBO, Bacterial Foraging optimization (BFO, Firefly Algorithm (FA techniques. The simulation result shows that the proposed algorithm outperforms previous optimization methods.
Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo
2017-07-01
This paper presents the design of a novel adaptive event-triggered control method based on the heuristic dynamic programming (HDP) technique for nonlinear discrete-time systems with unknown system dynamics. In the proposed method, the control law is only updated when the event-triggered condition is violated. Compared with the periodic updates in the traditional adaptive dynamic programming (ADP) control, the proposed method can reduce the computation and transmission cost. An actor-critic framework is used to learn the optimal event-triggered control law and the value function. Furthermore, a model network is designed to estimate the system state vector. The main contribution of this paper is to design a new trigger threshold for discrete-time systems. A detailed Lyapunov stability analysis shows that our proposed event-triggered controller can asymptotically stabilize the discrete-time systems. Finally, we test our method on two different discrete-time systems, and the simulation results are included.
Zainal Ariffin, S.; Razlan, A.; Ali, M. Mohd; Efendee, A. M.; Rahman, M. M.
2018-03-01
Background/Objectives: The paper discusses about the optimum cutting parameters with coolant techniques condition (1.0 mm nozzle orifice, wet and dry) to optimize surface roughness, temperature and tool wear in the machining process based on the selected setting parameters. The selected cutting parameters for this study were the cutting speed, feed rate, depth of cut and coolant techniques condition. Methods/Statistical Analysis Experiments were conducted and investigated based on Design of Experiment (DOE) with Response Surface Method. The research of the aggressive machining process on aluminum alloy (A319) for automotive applications is an effort to understand the machining concept, which widely used in a variety of manufacturing industries especially in the automotive industry. Findings: The results show that the dominant failure mode is the surface roughness, temperature and tool wear when using 1.0 mm nozzle orifice, increases during machining and also can be alternative minimize built up edge of the A319. The exploration for surface roughness, productivity and the optimization of cutting speed in the technical and commercial aspects of the manufacturing processes of A319 are discussed in automotive components industries for further work Applications/Improvements: The research result also beneficial in minimizing the costs incurred and improving productivity of manufacturing firms. According to the mathematical model and equations, generated by CCD based RSM, experiments were performed and cutting coolant condition technique using size nozzle can reduces tool wear, surface roughness and temperature was obtained. Results have been analyzed and optimization has been carried out for selecting cutting parameters, shows that the effectiveness and efficiency of the system can be identified and helps to solve potential problems.
Directory of Open Access Journals (Sweden)
Ali Gerami Matin
2017-10-01
Full Text Available Optimized road maintenance planning seeks for solutions that can minimize the life-cycle cost of a road network and concurrently maximize pavement condition. Aiming at proposing an optimal set of road maintenance solutions, robust meta-heuristic algorithms are used in research. Two main optimization techniques are applied including single-objective and multi-objective optimization. Genetic algorithms (GA, particle swarm optimization (PSO, and combination of genetic algorithm and particle swarm optimization (GAPSO as single-objective techniques are used, while the non-domination sorting genetic algorithm II (NSGAII and multi-objective particle swarm optimization (MOPSO which are sufficient for solving computationally complex large-size optimization problems as multi-objective techniques are applied and compared. A real case study from the rural transportation network of Iran is employed to illustrate the sufficiency of the optimum algorithm. The formulation of the optimization model is carried out in such a way that a cost-effective maintenance strategy is reached by preserving the performance level of the road network at a desirable level. So, the objective functions are pavement performance maximization and maintenance cost minimization. It is concluded that multi-objective algorithms including non-domination sorting genetic algorithm II (NSGAII and multi-objective particle swarm optimization performed better than the single objective algorithms due to the capability to balance between both objectives. And between multi-objective algorithms the NSGAII provides the optimum solution for the road maintenance planning.
Dynamical optimization techniques for the calculation of electronic structure in solids
International Nuclear Information System (INIS)
Benedek, R.; Min, B.I.; Garner, J.
1989-01-01
The method of dynamical simulated annealing, recently introduced by Car and Parrinello, provides a new tool for electronic structure computation as well as for molecular dynamics simulation. In this paper, we explore an optimization technique that is complementary to dynamical simulated annealing, the method of steepest descents (SD). As an illustration, SD is applied to calculate the total energy of diamond-Si, a system previously treated by Car and Parrinello. The adaptation of SD to treat metallic systems is discussed and a numerical application is presented. (author) 18 refs., 3 figs
Energy Technology Data Exchange (ETDEWEB)
Jackson, C. E.; Illfelder, H. M. J.; Pineda, G.
1998-12-31
Field implementation of an integrated wellsite geological steering service is described. The service provides timely, useful feedback from real-time logging-while-drilling (LWD) measurements for making immediate course corrections. Interactive multi-dimensional displays of both the geological and petrophysical properties of the formation being penetrated by the wellbore are a prominent feature of the service; the optimization of the drilling is the result of the visualization afforded by the displays. The paper reviews forward modelling techniques, provides a detailed explanation of the principles underlying this new application, and illustrates the application by examples from the field. 5 refs., 1 tab., 8 figs.
A three-stage strategy for optimal price offering by a retailer based on clustering techniques
International Nuclear Information System (INIS)
Mahmoudi-Kohan, N.; Shayesteh, E.; Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K.
2010-01-01
In this paper, an innovative strategy for optimal price offering to customers for maximizing the profit of a retailer is proposed. This strategy is based on load profile clustering techniques and includes three stages. For the purpose of clustering, an improved weighted fuzzy average K-means is proposed. Also, in this paper a new acceptance function for increasing the profit of the retailer is proposed. The new method is evaluated by implementation on a group of 300 customers of a 20 kV distribution network. (author)
Techniques for Optimizing Surgical Scars, Part 2: Hypertrophic Scars and Keloids.
Potter, Kathryn; Konda, Sailesh; Ren, Vicky Zhen; Wang, Apphia Lihan; Srinivasan, Aditya; Chilukuri, Suneel
2017-01-01
Surgical management of benign or malignant cutaneous tumors may result in noticeable scars that are of great concern to patients, regardless of sex, age, or ethnicity. Techniques to optimize surgical scars are discussed in this three-part review. Part 2 focuses on scar revision for hypertrophic and keloids scars. Scar revision options for hypertrophic and keloid scars include corticosteroids, bleomycin, fluorouracil, verapamil, avotermin, hydrogel scaffold, nonablative fractional lasers, ablative and fractional ablative lasers, pulsed dye laser (PDL), flurandrenolide tape, imiquimod, onion extract, silicone, and scar massage.
A three-stage strategy for optimal price offering by a retailer based on clustering techniques
Energy Technology Data Exchange (ETDEWEB)
Mahmoudi-Kohan, N.; Shayesteh, E. [Islamic Azad University (Garmsar Branch), Garmsar (Iran); Moghaddam, M. Parsa; Sheikh-El-Eslami, M.K. [Tarbiat Modares University, Tehran (Iran)
2010-12-15
In this paper, an innovative strategy for optimal price offering to customers for maximizing the profit of a retailer is proposed. This strategy is based on load profile clustering techniques and includes three stages. For the purpose of clustering, an improved weighted fuzzy average K-means is proposed. Also, in this paper a new acceptance function for increasing the profit of the retailer is proposed. The new method is evaluated by implementation on a group of 300 customers of a 20 kV distribution network. (author)
Visualization for Hyper-Heuristics: Back-End Processing
Energy Technology Data Exchange (ETDEWEB)
Simon, Luke [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-03-01
Modern society is faced with increasingly complex problems, many of which can be formulated as generate-and-test optimization problems. Yet, general-purpose optimization algorithms may sometimes require too much computational time. In these instances, hyperheuristics may be used. Hyper-heuristics automate the design of algorithms to create a custom algorithm for a particular scenario, finding the solution significantly faster than its predecessor. However, it may be difficult to understand exactly how a design was derived and why it should be trusted. This project aims to address these issues by creating an easy-to-use graphical user interface (GUI) for hyper-heuristics and an easy-to-understand scientific visualization for the produced solutions. To support the development of this GUI, my portion of the research involved developing algorithms that would allow for parsing of the data produced by the hyper-heuristics. This data would then be sent to the front-end, where it would be displayed to the end user.
Directory of Open Access Journals (Sweden)
Mansoor Ahmed Siddiqui
2017-06-01
Full Text Available This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC Simulation techniques. In this article, effort has been made to develop a maintenance model that incorporates three distinct states for each unit, while taking into account their different levels of deterioration. Calculations are carried out using the proposed model for two distinct cases of corrective repair, namely perfect and imperfect repairs, with as well as without opportunistic maintenance. Initially, results are accomplished using an analytical technique i.e., Markov Model. Validation of the results achieved is later carried out with the help of MC Simulation. In addition, MC Simulation based codes also work well for the frameworks that follow non-exponential failure and repair rates, and thus overcome the limitations offered by the Markov Model.
DATA MINING WORKSPACE AS AN OPTIMIZATION PREDICTION TECHNIQUE FOR SOLVING TRANSPORT PROBLEMS
Directory of Open Access Journals (Sweden)
Anastasiia KUPTCOVA
2016-09-01
Full Text Available This article addresses the study related to forecasting with an actual high-speed decision making under careful modelling of time series data. The study uses data-mining modelling for algorithmic optimization of transport goals. Our finding brings to the future adequate techniques for the fitting of a prediction model. This model is going to be used for analyses of the future transaction costs in the frontiers of the Czech Republic. Time series prediction methods for the performance of prediction models in the package of Statistics are Exponential, ARIMA and Neural Network approaches. The primary target for a predictive scenario in the data mining workspace is to provide modelling data faster and with more versatility than the other management techniques.
International Nuclear Information System (INIS)
Miranda, A.; Echevarria, J.F.; Rondon, S.; Leiva, P.; Sendoya, F.A.; Amalfi, J.; Lopez, M.; Dominguez, H.
1999-01-01
The paper deals with the study of the main parameters of thermal cycle in Orbital Automatic Weld, as a particular process of the GTAW Weld technique. Also is concerned with the investigation of microstructural and mechanical properties of welded joints made with Orbital Technique in SA 210 Steel, a particular alloy widely use during the construction of Economizers of Power Plants. A number of PC software were used in this sense in order to anticipate the main mechanical and structural characteristics of Weld metal and the Heat Affected Zone (HAZ). The papers also might be of great value during selection of optimal Weld parameters to produce sound and high quality Welds during the construction / assembling of structural components in high requirements industrial sectors and also to make a reliable prediction of weld properties
examining the predictive power of the VRIO-Framework and the Recognition Heuristic
Powalla, Christian
2010-01-01
Boundedly rational managers regularly have to make complex strategic decisions under uncertainty. In this context heuristics can play an important supporting role. They are used to reasonably structure the decision making process, to reduce the information search, and to achieve a good solution with an acceptable problem-solving effort. This empirical research project analyzes the practical usefulness of several selected heuristic techniques, which can be used within strategic analysis, by...
A flow-first route-next heuristic for liner shipping network design
DEFF Research Database (Denmark)
Krogsgaard, Alexander; Pisinger, David; Thorsen, Jesper
2018-01-01
Having a well-designed liner shipping network is paramount to ensure competitive freight rates, adequate capacity on trade-lanes, and reasonable transportation times.The most successful algorithms for liner shipping network design make use of a two-phase approach, where they ﬁrst design the routes...... diﬀerent operators are used to modify the network. Since each iteration of the local search method involves solving a very complex multi-commodity ﬂow problem to route the containers through the network, the ﬂow problem is solved heuristically by use of a fast Lagrange heuristic. Although the Lagrange...... heuristic for ﬂowing containers is 2–5% from the optimal solution, the solution quality is suﬃciently good to guide the variable neighborhood search method in designing the network. Computational results are reported, showing that the developed heuristic is able to ﬁnd improved solutions for large...
Madni, Syed Hamid Hussain; Abd Latiff, Muhammad Shafie; Abdullahi, Mohammed; Usman, Mohammed Joda
2017-01-01
Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing. PMID:28467505
Madni, Syed Hamid Hussain; Abd Latiff, Muhammad Shafie; Abdullahi, Mohammed; Abdulhamid, Shafi'i Muhammad; Usman, Mohammed Joda
2017-01-01
Cloud computing infrastructure is suitable for meeting computational needs of large task sizes. Optimal scheduling of tasks in cloud computing environment has been proved to be an NP-complete problem, hence the need for the application of heuristic methods. Several heuristic algorithms have been developed and used in addressing this problem, but choosing the appropriate algorithm for solving task assignment problem of a particular nature is difficult since the methods are developed under different assumptions. Therefore, six rule based heuristic algorithms are implemented and used to schedule autonomous tasks in homogeneous and heterogeneous environments with the aim of comparing their performance in terms of cost, degree of imbalance, makespan and throughput. First Come First Serve (FCFS), Minimum Completion Time (MCT), Minimum Execution Time (MET), Max-min, Min-min and Sufferage are the heuristic algorithms considered for the performance comparison and analysis of task scheduling in cloud computing.
The Probability Heuristics Model of Syllogistic Reasoning.
Chater, Nick; Oaksford, Mike
1999-01-01
Proposes a probability heuristic model for syllogistic reasoning and confirms the rationality of this heuristic by an analysis of the probabilistic validity of syllogistic reasoning that treats logical inference as a limiting case of probabilistic inference. Meta-analysis and two experiments involving 40 adult participants and using generalized…
Cooperative heuristic multi-agent planning
De Weerdt, M.M.; Tonino, J.F.M.; Witteveen, C.
2001-01-01
In this paper we will use the framework to study cooperative heuristic multi-agent planning. During the construction of their plans, the agents use a heuristic function inspired by the FF planner (l3l). At any time in the process of planning the agents may exchange available resources, or they may
"A Heuristic for Visual Thinking in History"
Staley, David J.
2007-01-01
This article details a heuristic history teachers can use in assigning and evaluating multimedia projects in history. To use this heuristic successfully, requires more than simply following the steps in the list or stages in a recipe: in many ways, it requires a reorientation in what it means to think like an historian. This article, as much as…
Effective Heuristics for New Venture Formation
Kraaijenbrink, Jeroen
2010-01-01
Entrepreneurs are often under time pressure and may only have a short window of opportunity to launch their new venture. This means they often have no time for rational analytical decisions and rather rely on heuristics. Past research on entrepreneurial heuristics has primarily focused on predictive
Religion, Heuristics, and Intergenerational Risk Management
Rupert Read; Nassim Nicholas Taleb
2014-01-01
Religions come with risk-managing interdicts and heuristics, and they carry such interdicts and heuristics across generations. We remark on such facets of religion in relation to a propensity among some decision scientists and others to regard practices that they cannot understand as being irrational, biased, and so on.
Optimization of MKID noise performance via readout technique for astronomical applications
Czakon, Nicole G.; Schlaerth, James A.; Day, Peter K.; Downes, Thomas P.; Duan, Ran P.; Gao, Jiansong; Glenn, Jason; Golwala, Sunil R.; Hollister, Matt I.; LeDuc, Henry G.; Mazin, Benjamin A.; Maloney, Philip R.; Noroozian, Omid; Nguyen, Hien T.; Sayers, Jack; Siegel, Seth; Vaillancourt, John E.; Vayonakis, Anastasios; Wilson, Philip R.; Zmuidzinas, Jonas
2010-07-01
Detectors employing superconducting microwave kinetic inductance detectors (MKIDs) can be read out by measuring changes in either the resonator frequency or dissipation. We will discuss the pros and cons of both methods, in particular, the readout method strategies being explored for the Multiwavelength Sub/millimeter Inductance Camera (MUSIC) to be commissioned at the CSO in 2010. As predicted theoretically and observed experimentally, the frequency responsivity is larger than the dissipation responsivity, by a factor of 2-4 under typical conditions. In the absence of any other noise contributions, it should be easier to overcome amplifier noise by simply using frequency readout. The resonators, however, exhibit excess frequency noise which has been ascribed to a surface distribution of two-level fluctuators sensitive to specific device geometries and fabrication techniques. Impressive dark noise performance has been achieved using modified resonator geometries employing interdigitated capacitors (IDCs). To date, our noise measurement and modeling efforts have assumed an onresonance readout, with the carrier power set well below the nonlinear regime. Several experimental indicators suggested to us that the optimal readout technique may in fact require a higher readout power, with the carrier tuned somewhat off resonance, and that a careful systematic study of the optimal readout conditions was needed. We will present the results of such a study, and discuss the optimum readout conditions as well as the performance that can be achieved relative to BLIP.
Energy Technology Data Exchange (ETDEWEB)
Ito, Fuminori, E-mail: fuminoito@spice.ocn.ne.jp [Tokyo Metropolitan University, Department of Applied Chemistry, Graduate School of Urban Environmental Sciences (Japan)
2016-09-15
In this study, we report the optimization of a solvent evaporation technique for preparing monodisperse poly-(lactide-co-glycolide) (PLGA) nanospheres, from a mixture of solvents composed of ethanol and PVA solution. Various experimental conditions were investigated in order to control the particle size and size distribution of the nanospheres. In addition, nanospheres containing rifampicin (RFP, an antituberculosis drug), were prepared using PLGA of various molecular weights, to study the effects of RFP as a model hydrophobic drug. The results showed that a higher micro-homogenizer stirring rate facilitated the preparation of monodisperse PLGA nanospheres with a low coefficient of variation (~20 %), with sizes below 200 nm. Increasing the PLGA concentration from 0.1 to 0.5 g resulted in an increase in the size of the obtained nanospheres from 130 to 174 nm. The molecular weight of PLGA had little effect on the particle sizes and particle size distributions of the nanospheres. However, the drug loading efficiencies of the obtained RFP/PLGA nanospheres decreased when the molecular weight of PLGA was increased. Based on these experiments, an optimized technique was established for the preparation of monodisperse PLGA nanospheres, using the method developed by the authors.Graphical Abstract.
Analysis and Optimization of Distributed Real-Time Embedded Systems
DEFF Research Database (Denmark)
Pop, Paul; Eles, Petru; Peng, Zebo
2006-01-01
and scheduling policies. In this context, the task of designing such systems is becoming increasingly difficult. The success of new adequate design methods depends on the availability of efficient analysis as well as optimization techniques. In this paper, we present both analysis and optimization approaches...... characteristic to this class of systems: mapping of functionality, the optimization of the access to the communication channel, and the assignment of scheduling policies to processes. Optimization heuristics aiming at producing a schedulable system, with a given amount of resources, are presented....
Quad-rotor flight path energy optimization
Kemper, Edward
Quad-Rotor unmanned areal vehicles (UAVs) have been a popular area of research and development in the last decade, especially with the advent of affordable microcontrollers like the MSP 430 and the Raspberry Pi. Path-Energy Optimization is an area that is well developed for linear systems. In this thesis, this idea of path-energy optimization is extended to the nonlinear model of the Quad-rotor UAV. The classical optimization technique is adapted to the nonlinear model that is derived for the problem at hand, coming up with a set of partial differential equations and boundary value conditions to solve these equations. Then, different techniques to implement energy optimization algorithms are tested using simulations in Python. First, a purely nonlinear approach is used. This method is shown to be computationally intensive, with no practical solution available in a reasonable amount of time. Second, heuristic techniques to minimize the energy of the flight path are tested, using Ziegler-Nichols' proportional integral derivative (PID) controller tuning technique. Finally, a brute force look-up table based PID controller is used. Simulation results of the heuristic method show that both reliable control of the system and path-energy optimization are achieved in a reasonable amount of time.
A comprehensive review of prostate cancer brachytherapy: defining an optimal technique
International Nuclear Information System (INIS)
Vicini, Frank A.; Kini, Vijay R.; Edmundson, Gregory B.S.; Gustafson, Gary S.; Stromberg, Jannifer; Martinez, Alvaro
1999-01-01
Purpose: A comprehensive review of prostate cancer brachytherapy literature was performed to determine if an optimal method of implantation could be identified, and to compare and contrast techniques currently in use. Methods and Materials: A MEDLINE search was conducted to obtain all articles in the English language on prostate cancer brachytherapy from 1985 through 1998. Articles were reviewed and grouped to determine the primary technique of implantation, the method or philosophy of source placement and/or dose specification, the technique to evaluate implant quality, overall treatment results (based upon pretreatment prostate specific antigen, (PSA), and biochemical control) and clinical, pathological or biochemical outcome based upon implant quality. Results: A total of 178 articles were identified in the MEDLINE database. Of these, 53 studies discussed evaluable techniques of implantation and were used for this analysis. Of these studies, 52% used preoperative ultrasound to determine the target volume to be implanted, 16% used preoperative computerized tomography (CT) scans, and 18% placed seeds with an open surgical technique. An additional 11% of studies placed seeds or needles under ultrasound guidance using interactive real-time dosimetry. The number and distribution of radioactive sources to be implanted or the method used to prescribe dose was determined using nomograms in 27% of studies, a least squares optimization technique in 11%, or not stated in 35%. In the remaining 26%, sources were described as either uniformly, differentially, or peripherally placed in the gland. To evaluate implant quality, 28% of studies calculated some type of dose-volume histogram, 21% calculated the matched peripheral dose, 19% the minimum peripheral dose, 14% used some type of CT-based qualitative review and, in 18% of studies, no implant quality evaluation was mentioned. Six studies correlated outcome with implant dose. One study showed an association of implant dose
Mohamed, Ahmed F.; Elarini, Mahdi M.; Othman, Ahmed M.
2013-01-01
One of the most recent optimization techniques applied to the optimal design of photovoltaic system to supply an isolated load demand is the Artificial Bee Colony Algorithm (ABC). The proposed methodology is applied to optimize the cost of the PV system including photovoltaic, a battery bank, a battery charger controller, and inverter. Two objective functions are proposed: the first one is the PV module output power which is to be maximized and the second one is the life cycle cost (LCC) whic...
A custom three-dimensional electron bolus technique for optimization of postmastectomy irradiation
International Nuclear Information System (INIS)
Perkins, George H.; McNeese, Marsha D.; Antolak, John A.; Buchholz, Thomas A.; Strom, Eric A.; Hogstrom, Kenneth R.
2001-01-01
Purpose: Postmastectomy irradiation (PMI) is a technically complex treatment requiring consideration of the primary tumor location, possible risk of internal mammary node involvement, varying chest wall thicknesses secondary to surgical defects or body habitus, and risk of damaging normal underlying structures. In this report, we describe the application of a customized three-dimensional (3D) electron bolus technique for delivering PMI. Methods and Materials: A customized electron bolus was designed using a 3D planning system. Computed tomography (CT) images of each patient were obtained in treatment position and the volume to be treated was identified. The distal surface of the wax bolus matched the skin surface, and the proximal surface was designed to conform to the 90% isodose surface to the distal surface of the planning target volume (PTV). Dose was calculated with a pencil-beam algorithm correcting for patient heterogeneity. The bolus was then fabricated from modeling wax using a computer-controlled milling device. To aid in quality assurance, CT images with the bolus in place were generated and the dose distribution was computed using these images. Results: This technique optimized the dose distribution while minimizing irradiation of normal tissues. The use of a single anterior field eliminated field junction sites. Two patients who benefited from this option are described: one with altered chest wall geometry (congenital pectus excavatum), and one with recurrent disease in the medial chest wall and internal mammary chain (IMC) area. Conclusion: The use of custom 3D electron bolus for PMI is an effective method for optimizing dose delivery. The radiation dose distribution is highly conformal, dose heterogeneity is reduced compared to standard techniques in certain suboptimal settings, and excellent immediate outcome is obtained
International Nuclear Information System (INIS)
Chang, Chiou-Shiung; Hwang, Jing-Min; Tai, Po-An; Chang, You-Kang; Wang, Yu-Nong; Shih, Rompin; Chuang, Keh-Shih
2016-01-01
(p < 0.05) than either DCA or IMRS plans, at 9.2 ± 7% and 8.2 ± 6%, respectively. Owing to the multiple arc or beam planning designs of IMRS and VMAT, both of these techniques required higher MU delivery than DCA, with the averages being twice as high (p < 0.05). If linear accelerator is only 1 modality can to establish for SRS treatment. Based on statistical evidence retrospectively, we recommend VMAT as the optimal technique for delivering treatment to tumors adjacent to brainstem.
The Hit and Away technique: optimal usage of the ultrasonic scalpel in laparoscopic gastrectomy.
Irino, Tomoyuki; Hiki, Naoki; Ohashi, Manabu; Nunobe, Souya; Sano, Takeshi; Yamaguchi, Toshiharu
2016-01-01
Thermal injury and unexpected bleeding caused by ultrasonic scalpels can lead to fatal complications in laparoscopic gastrectomy (LG), such as postoperative pancreatic fistulas (POPF). In this study, we developed the "Hit and Away" protocol for optimal usage of the ultrasonic scalpel, which in essence involves dividing tissues and vessels in batches using the tip of the scalpel to control tissue temperature. To assess the effectiveness of the technique, the surface temperature of the mesocolon of female swine after ultrasonic scalpel activations was measured, and tissue samples were collected to evaluate microscopic thermal injury to the pancreas. In parallel, we retrospectively surveyed 216 patients who had undergone LG before or after the introduction of this technique and assessed the ability of this technique to reduce POPF. The tissue temperature of the swine mesocolon reached 43 °C, a temperature at which adipose tissue melted but fibrous tissue, including vessels, remained intact. The temperature returned to baseline within 3 s of turning off the ultrasonic scalpel, demonstrating the advantage of using ultrasonic scalpel in a pulsatile manner. Tissue samples from the pancreas demonstrated that the extent of thermal injury post-procedure was limited to the capsule of the pancreas. Moreover, with respect to the clinical outcomes before and after the introduction of this technique, POPF incidence decreased significantly from 7.8 to 1.0% (p = 0.021). The "Hit and Away" technique can reduce blood loss and thermal injury to the pancreas and help to ensure the safety of lymph node dissection in LG.
Heuristic Strategies in Systems Biology
Directory of Open Access Journals (Sweden)
Fridolin Gross
2016-06-01
Full Text Available Systems biology is sometimes presented as providing a superior approach to the problem of biological complexity. Its use of ‘unbiased’ methods and formal quantitative tools might lead to the impression that the human factor is effectively eliminated. However, a closer look reveals that this impression is misguided. Systems biologists cannot simply assemble molecular information and compute biological behavior. Instead, systems biology’s main contribution is to accelerate the discovery of mechanisms by applying models as heuristic tools. These models rely on a variety of idealizing and simplifying assumptions in order to be efficient for this purpose. The strategies of systems biologists are similar to those of experimentalists in that they attempt to reduce the complexity of the discovery process. Analyzing and comparing these strategies, or ‘heuristics’, reveals the importance of the human factor in computational approaches and helps to situate systems biology within the epistemic landscape of the life sciences.
Heuristic approach to train rescheduling
Directory of Open Access Journals (Sweden)
Mladenović Snežana
2007-01-01
Full Text Available Starting from the defined network topology and the timetable assigned beforehand, the paper considers a train rescheduling in respond to disturbances that have occurred. Assuming that the train trips are jobs, which require the elements of infrastructure - resources, it was done by the mapping of the initial problem into a special case of job shop scheduling problem. In order to solve the given problem, a constraint programming approach has been used. A support to fast finding "enough good" schedules is offered by original separation, bound and search heuristic algorithms. In addition, to improve the time performance, instead of the actual objective function with a large domain, a surrogate objective function is used with a smaller domain, if there is such. .
Chang, Chiou-Shiung; Hwang, Jing-Min; Tai, Po-An; Chang, You-Kang; Wang, Yu-Nong; Shih, Rompin; Chuang, Keh-Shih
2016-01-01
Stereotactic radiosurgery (SRS) is a well-established technique that is replacing whole-brain irradiation in the treatment of intracranial lesions, which leads to better preservation of brain functions, and therefore a better quality of life for the patient. There are several available forms of linear accelerator (LINAC)-based SRS, and the goal of the present study is to identify which of these techniques is best (as evaluated by dosimetric outcomes statistically) when the target is located adjacent to brainstem. We collected the records of 17 patients with lesions close to the brainstem who had previously been treated with single-fraction radiosurgery. In all, 5 different lesion catalogs were collected, and the patients were divided into 2 distance groups-1 consisting of 7 patients with a target-to-brainstem distance of less than 0.5cm, and the other of 10 patients with a target-to-brainstem distance of ≥ 0.5 and linear accelerator is only 1 modality can to establish for SRS treatment. Based on statistical evidence retrospectively, we recommend VMAT as the optimal technique for delivering treatment to tumors adjacent to brainstem. Copyright © 2016 American Association of Medical Dosimetrists. All rights reserved.
THE HEURISTIC FUNCTION OF SPORT
Directory of Open Access Journals (Sweden)
Adam Petrović
2012-09-01
Full Text Available Being a significant area of human activity, sport has multiple functions. One of the more important functions of sport, especially top sport, is the inventive heuristic function. Creative work, being a process of creating new values, represents a significant possibility for advancement of sport. This paper aims at pointing at the various dimensions of human creative work, at the creative work which can be seen in sport (in a narrow sense and at the scientific and practical areas which borderline sport. The method of theoretical analysis of different approaches to the phenomenon of creative work , both in general and in sport, was applied in this paper. This area can be systematized according to various criterion : the level of creative work, different fields where it appears, the subjects of creative work - creators etc. Case analysis shows that the field of creative work in sport is widening and deepening constantly. There are different levels of creativity not only in the system of training and competition, but in a wider social context of sport as well. As a process of human spirit and mind the creative work belongs not just to athletes and coaches, but also to all the people and social groups who's creative power manifests itself in sport. The classification of creative work in sport according to various criterion allows for heuristic function of sport to be explained comprehensively and to create an image how do the sparks of human spirit improve the micro cosmos of sport. A thorough classification of creative work in sport allows for a detailed analysis of all the elements of creative work and each of it’s area in sport. In this way the progress in sport , as a consequence of innovations in both competitions and athletes’ training and of everything that goes with those activities, can be guided into the needed direction more easily as well as studied and applied.
Numerical and Evolutionary Optimization Workshop
Trujillo, Leonardo; Legrand, Pierrick; Maldonado, Yazmin
2017-01-01
This volume comprises a selection of works presented at the Numerical and Evolutionary Optimization (NEO) workshop held in September 2015 in Tijuana, Mexico. The development of powerful search and optimization techniques is of great importance in today’s world that requires researchers and practitioners to tackle a growing number of challenging real-world problems. In particular, there are two well-established and widely known fields that are commonly applied in this area: (i) traditional numerical optimization techniques and (ii) comparatively recent bio-inspired heuristics. Both paradigms have their unique strengths and weaknesses, allowing them to solve some challenging problems while still failing in others. The goal of the NEO workshop series is to bring together people from these and related fields to discuss, compare and merge their complimentary perspectives in order to develop fast and reliable hybrid methods that maximize the strengths and minimize the weaknesses of the underlying paradigms. Throu...
Karthivashan, Govindarajan; Masarudin, Mas Jaffri; Kura, Aminu Umar; Abas, Faridah; Fakurazi, Sharida
2016-01-01
This study involves adaptation of bulk or sequential technique to load multiple flavonoids in a single phytosome, which can be termed as "flavonosome". Three widely established and therapeutically valuable flavonoids, such as quercetin (Q), kaempferol (K), and apigenin (A), were quantified in the ethyl acetate fraction of Moringa oleifera leaves extract and were commercially obtained and incorporated in a single flavonosome (QKA-phosphatidylcholine) through four different methods of synthesis - bulk (M1) and serialized (M2) co-sonication and bulk (M3) and sequential (M4) co-loading. The study also established an optimal formulation method based on screening the synthesized flavonosomes with respect to their size, charge, polydispersity index, morphology, drug-carrier interaction, antioxidant potential through in vitro 1,1-diphenyl-2-picrylhydrazyl kinetics, and cytotoxicity evaluation against human hepatoma cell line (HepaRG). Furthermore, entrapment and loading efficiency of flavonoids in the optimal flavonosome have been identified. Among the four synthesis methods, sequential loading technique has been optimized as the best method for the synthesis of QKA-phosphatidylcholine flavonosome, which revealed an average diameter of 375.93±33.61 nm, with a zeta potential of -39.07±3.55 mV, and the entrapment efficiency was >98% for all the flavonoids, whereas the drug-loading capacity of Q, K, and A was 31.63%±0.17%, 34.51%±2.07%, and 31.79%±0.01%, respectively. The in vitro 1,1-diphenyl-2-picrylhydrazyl kinetics of the flavonoids indirectly depicts the release kinetic behavior of the flavonoids from the carrier. The QKA-loaded flavonosome had no indication of toxicity toward human hepatoma cell line as shown by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide result, wherein even at the higher concentration of 200 µg/mL, the flavonosomes exert >85% of cell viability. These results suggest that sequential loading technique may be a promising
Simply criminal: predicting burglars' occupancy decisions with a simple heuristic.
Snook, Brent; Dhami, Mandeep K; Kavanagh, Jennifer M
2011-08-01
Rational choice theories of criminal decision making assume that offenders weight and integrate multiple cues when making decisions (i.e., are compensatory). We tested this assumption by comparing how well a compensatory strategy called Franklin's Rule captured burglars' decision policies regarding residence occupancy compared to a non-compensatory strategy (i.e., Matching Heuristic). Forty burglars each decided on the occupancy of 20 randomly selected photographs of residences (for which actual occupancy was known when the photo was taken). Participants also provided open-ended reports on the cues that influenced their decisions in each case, and then rated the importance of eight cues (e.g., deadbolt visible) over all decisions. Burglars predicted occupancy beyond chance levels. The Matching Heuristic was a significantly better predictor of burglars' decisions than Franklin's Rule, and cue use in the Matching Heuristic better corresponded to the cue ecological validities in the environment than cue use in Franklin's Rule. The most important cue in burglars' models was also the most ecologically valid or predictive of actual occupancy (i.e., vehicle present). The majority of burglars correctly identified the most important cue in their models, and the open-ended technique showed greater correspondence between self-reported and captured cue use than the rating over decision technique. Our findings support a limited rationality perspective to understanding criminal decision making, and have implications for crime prevention.
International Nuclear Information System (INIS)
Santos Coelho, Leandro dos
2009-01-01
The reliability-redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability-redundancy optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solutions. One of these meta-heuristics is the particle swarm optimization (PSO). PSO is a population-based heuristic optimization technique inspired by social behavior of bird flocking and fish schooling. This paper presents an efficient PSO algorithm based on Gaussian distribution and chaotic sequence (PSO-GC) to solve the reliability-redundancy optimization problems. In this context, two examples in reliability-redundancy design problems are evaluated. Simulation results demonstrate that the proposed PSO-GC is a promising optimization technique. PSO-GC performs well for the two examples of mixed-integer programming in reliability-redundancy applications considered in this paper. The solutions obtained by the PSO-GC are better than the previously best-known solutions available in the recent literature
Energy Technology Data Exchange (ETDEWEB)
Santos Coelho, Leandro dos [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR, Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: leandro.coelho@pucpr.br
2009-04-15
The reliability-redundancy optimization problems can involve the selection of components with multiple choices and redundancy levels that produce maximum benefits, and are subject to the cost, weight, and volume constraints. Many classical mathematical methods have failed in handling nonconvexities and nonsmoothness in reliability-redundancy optimization problems. As an alternative to the classical optimization approaches, the meta-heuristics have been given much attention by many researchers due to their ability to find an almost global optimal solutions. One of these meta-heuristics is the particle swarm optimization (PSO). PSO is a population-based heuristic optimization technique inspired by social behavior of bird flocking and fish schooling. This paper presents an efficient PSO algorithm based on Gaussian distribution and chaotic sequence (PSO-GC) to solve the reliability-redundancy optimization problems. In this context, two examples in reliability-redundancy design problems are evaluated. Simulation results demonstrate that the proposed PSO-GC is a promising optimization technique. PSO-GC performs well for the two examples of mixed-integer programming in reliability-redundancy applications considered in this paper. The solutions obtained by the PSO-GC are better than the previously best-known solutions available in the recent literature.
International Nuclear Information System (INIS)
Chao, Ming; Yuan, Yading; Rosenzweig, Kenneth E; Lo, Yeh-Chi; Wei, Jie; Li, Tianfang
2016-01-01
We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as −0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients. (paper)
International Nuclear Information System (INIS)
Ghasemi, Mojtaba; Ghavidel, Sahand; Aghaei, Jamshid; Gitizadeh, Mohsen; Falah, Hasan
2014-01-01
Highlights: • Chaotic invasive weed optimization techniques based on chaos. • Nonlinear environmental OPF problem considering non-smooth fuel cost curves. • A comparative study of CIWO techniques for environmental OPF problem. - Abstract: This paper presents efficient chaotic invasive weed optimization (CIWO) techniques based on chaos for solving optimal power flow (OPF) problems with non-smooth generator fuel cost functions (non-smooth OPF) with the minimum pollution level (environmental OPF) in electric power systems. OPF problem is used for developing corrective strategies and to perform least cost dispatches. However, cost based OPF problem solutions usually result in unattractive system gaze emission issue (environmental OPF). In the present paper, the OPF problem is formulated by considering the emission issue. The total emission can be expressed as a non-linear function of power generation, as a multi-objective optimization problem, where optimal control settings for simultaneous minimization of fuel cost and gaze emission issue are obtained. The IEEE 30-bus test power system is presented to illustrate the application of the environmental OPF problem using CIWO techniques. Our experimental results suggest that CIWO techniques hold immense promise to appear as efficient and powerful algorithm for optimization in the power systems
Sintering process optimization for multi-layer CGO membranes by in situ techniques
DEFF Research Database (Denmark)
Kaiser, Andreas; Prasad, A.S.; Foghmoes, Søren Preben Vagn
2013-01-01
The sintering of asymmetric CGO bi-layers (thin dense membrane on a porous support; Ce0.9Gd0.1O1.95-delta = CGO) with Co3O4 as sintering additive has been optimized by combination of two in situ techniques. Optical dilatometry revealed that bi-layer shape and microstructure are dramatically...... changing in a narrow temperature range of less than 100 degrees C. Below 1030 degrees C, a higher densification rate in the dense membrane layer than in the porous support leads to concave shape, whereas the densification rate of the support is dominant above 1030 degrees C, leading to convex shape. A fiat...... bi-layer could be prepared at 1030 degrees C, when shrinkage rates were similar. In situ van der Pauw measurements on tape cast layers during sintering allowed following the conductivity during sintering. A strong increase in conductivity and in activation energy E-a for conduction was observed...
International Nuclear Information System (INIS)
Cooper, G.S. Jr.; Kaluarachchi, J.J.; Peralta, R.C.
1993-01-01
An innovative approach is presented to minimize pumping for immobilizing a floating plume of a light non-aqueous phase liquid (LNAPL). The best pumping strategy is determined to contain the free oil product and provide for gradient control of the water table. This approach combined detailed simulation, statistical analysis, and optimization. This modeling technique uses regression equations that describe system response to variable pumping stimuli. The regression equations were developed from analysis of systematically performed simulations of multiphase flow in an areal region of an unconfined aquifer. Simulations were performed using ARMOS, a finite element model. ARMOS can be used to simulate a spill, leakage from subsurface storage facilities and recovery of hydrocarbons from trenches or pumping wells to design remediation schemes
An Exploratory Study on the Optimized Test Conditions of the Lock-in Thermography Technique
International Nuclear Information System (INIS)
Cho, Yong Jin
2011-01-01
This work is devoted to the technique application of lock-in infrared thermography in the shipbuilding and ocean engineering industry. For this purpose, an exploratory study to find the optimized test conditions is carried out by the design of experiments. It has been confirmed to be useful method that the phase contrast images were quantified by a reference image and weighted by defect hole size. Illuminated optical intensity of lower or medium strength give a good result for getting a phase contrast image. In order to get a good phase contrast image, lock-in frequency factors should be high in proportion to the illuminated optical intensity. The integration time of infrared camera should have been inversely proportional to the optical intensity. The other hand, the difference of specimen materials gave a slightly biased results not being discriminative reasoning
International Nuclear Information System (INIS)
Harding, D.C.; Eldred, M.S.; Witkowski, W.R.
1995-01-01
Type B radioactive material transport packages must meet strict Nuclear Regulatory Commission (NRC) regulations specified in 10 CFR 71. Type B containers include impact limiters, radiation or thermal shielding layers, and one or more containment vessels. In the past, each component was typically designed separately based on its driving constraint and the expertise of the designer. The components were subsequently assembled and the design modified iteratively until all of the design criteria were met. This approach neglects the fact that components may serve secondary purposes as well as primary ones. For example, an impact limiter's primary purpose is to act as an energy absorber and protect the contents of the package, but can also act as a heat dissipater or insulator. Designing the component to maximize its performance with respect to both objectives can be accomplished using numerical optimization techniques
Engine Yaw Augmentation for Hybrid-Wing-Body Aircraft via Optimal Control Allocation Techniques
Taylor, Brian R.; Yoo, Seung Yeun
2011-01-01
Asymmetric engine thrust was implemented in a hybrid-wing-body non-linear simulation to reduce the amount of aerodynamic surface deflection required for yaw stability and control. Hybrid-wing-body aircraft are especially susceptible to yaw surface deflection due to their decreased bare airframe yaw stability resulting from the lack of a large vertical tail aft of the center of gravity. Reduced surface deflection, especially for trim during cruise flight, could reduce the fuel consumption of future aircraft. Designed as an add-on, optimal control allocation techniques were used to create a control law that tracks total thrust and yaw moment commands with an emphasis on not degrading the baseline system. Implementation of engine yaw augmentation is shown and feasibility is demonstrated in simulation with a potential drag reduction of 2 to 4 percent. Future flight tests are planned to demonstrate feasibility in a flight environment.
Analysis and optimization of a proton exchange membrane fuel cell using modeling techniques
International Nuclear Information System (INIS)
Torre Valdés, Ing. Raciel de la; García Parra, MSc. Lázaro Roger; González Rodríguez, MSc. Daniel
2015-01-01
This paper proposes a three-dimensional, non-isothermal and steady-state model of Proton Exchange Membrane Fuel Cell using Computational Fluid Dynamic techniques, specifically ANSYS FLUENT 14.5. It's considered multicomponent diffusion and two-phasic flow. The model was compared with experimental published data and with another model. The operation parameters: reactants pressure and temperature, gases flow direction, gas diffusion layer and catalyst layer porosity, reactants humidification and oxygen concentration are analyzed. The model allows the fuel cell design optimization taking in consideration the channels dimensions, the channels length and the membrane thickness. Furthermore, fuel cell performance is analyzed working with SPEEK membrane, an alternative electrolyte to Nafion. In order to carry on membrane material study, it's necessary to modify the expression that describes the electrolyte ionic conductivity. It's found that the device performance has got a great sensibility to pressure, temperature, reactant humidification and oxygen concentration variations. (author)
Optimal Draft requirement for vibratory tillage equipment using Genetic Algorithm Technique
Rao, Gowripathi; Chaudhary, Himanshu; Singh, Prem
2018-03-01
Agriculture is an important sector of Indian economy. Primary and secondary tillage operations are required for any land preparation process. Conventionally different tractor-drawn implements such as mouldboard plough, disc plough, subsoiler, cultivator and disc harrow, etc. are used for primary and secondary manipulations of soils. Among them, oscillatory tillage equipment is one such type which uses vibratory motion for tillage purpose. Several investigators have reported that the requirement for draft consumption in primary tillage implements is more as compared to oscillating one because they are always in contact with soil. Therefore in this paper, an attempt is made to find out the optimal parameters from the experimental data available in the literature to obtain minimum draft consumption through genetic algorithm technique.
Optimization of GPS water vapor tomography technique with radiosonde and COSMIC historical data
Directory of Open Access Journals (Sweden)
S. Ye
2016-09-01
Full Text Available The near-real-time high spatial resolution of atmospheric water vapor distribution is vital in numerical weather prediction. GPS tomography technique has been proved effectively for three-dimensional water vapor reconstruction. In this study, the tomography processing is optimized in a few aspects by the aid of radiosonde and COSMIC historical data. Firstly, regional tropospheric zenith hydrostatic delay (ZHD models are improved and thus the zenith wet delay (ZWD can be obtained at a higher accuracy. Secondly, the regional conversion factor of converting the ZWD to the precipitable water vapor (PWV is refined. Next, we develop a new method for dividing the tomography grid with an uneven voxel height and a varied water vapor layer top. Finally, we propose a Gaussian exponential vertical interpolation method which can better reflect the vertical variation characteristic of water vapor. GPS datasets collected in Hong Kong in February 2014 are employed to evaluate the optimized tomographic method by contrast with the conventional method. The radiosonde-derived and COSMIC-derived water vapor densities are utilized as references to evaluate the tomographic results. Using radiosonde products as references, the test results obtained from our optimized method indicate that the water vapor density accuracy is improved by 15 and 12 % compared to those derived from the conventional method below the height of 3.75 km and above the height of 3.75 km, respectively. Using the COSMIC products as references, the results indicate that the water vapor density accuracy is improved by 15 and 19 % below 3.75 km and above 3.75 km, respectively.
Liu, Jing-Han; Zhou, Jun; Ouyang, Xi-Lin; Li, Xi-Jin; Lu, Fa-Qiang
2005-08-01
This study was aimed to further optimize trehalose loading technique including loading temperature, loading time, loading solution and loading concentration of trehalose, based on the established parameters. Loading efficiency in plasma was compared with that in buffer at 37 degrees C; the curves of intracellular trehalose concentration versus loading time at 37 degrees C and 16 degrees C were measured; curves of mean platelet volume (MPV) versus loading time and loading concentration were investigated and compared. According to results obtained, the loaing time, loading temperature, loading solution and trehalose concentration were ascertained for high loading efficiency of trehalose into human platelet. The results showed that the loading efficiency in plasma was markedly higher than that in buffer at 37 degrees C, the loading efficiency in plasma at 37 degrees C was significantly higher than that at 16 degrees C and reached 19.51% after loading for 4 hours, but 6.16% at 16 degrees C. MPV at 16 degrees C was increased by 43.2% than that at 37 degrees C, but had no distinct changes with loading time and loading concentration. In loading at 37 degrees C, MPV increased with loading time and loading concentration positively. Loading time and loading concentration displayed synergetic effect on MPV. MPV increased with loading time and concentration while trehalose loading concentration was above 50 mmol/L. It is concluded that the optimization parameters of trehalose loading technique are 37 degrees C (temperature), 4 hours (leading time), plasma (loading solution), 50 mmol/L (feasible trehalose concentration). The trehalose concentration can be adjusted to meet the requirement of lyophilization.
Comparison of metaheuristic techniques to determine optimal placement of biomass power plants
International Nuclear Information System (INIS)
Reche-Lopez, P.; Ruiz-Reyes, N.; Garcia Galan, S.; Jurado, F.
2009-01-01
This paper deals with the application and comparison of several metaheuristic techniques to optimize the placement and supply area of biomass-fueled power plants. Both, trajectory and population-based methods are applied for our goal. In particular, two well-known trajectory method, such as Simulated Annealing (SA) and Tabu Search (TS), and two commonly used population-based methods, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are hereby considered. In addition, a new binary PSO algorithm has been proposed, which incorporates an inertia weight factor, like the classical continuous approach. The fitness function for the metaheuristics is the profitability index, defined as the ratio between the net present value and the initial investment. In this work, forest residues are considered as biomass source, and the problem constraints are: the generation system must be located inside the supply area, and its maximum electric power is 5 MW. The comparative results obtained by all considered metaheuristics are discussed. Random walk has also been assessed for the problem we deal with.
Multi-view 3D scene reconstruction using ant colony optimization techniques
International Nuclear Information System (INIS)
Chrysostomou, Dimitrios; Gasteratos, Antonios; Nalpantidis, Lazaros; Sirakoulis, Georgios C
2012-01-01
This paper presents a new method performing high-quality 3D object reconstruction of complex shapes derived from multiple, calibrated photographs of the same scene. The novelty of this research is found in two basic elements, namely: (i) a novel voxel dissimilarity measure, which accommodates the elimination of the lighting variations of the models and (ii) the use of an ant colony approach for further refinement of the final 3D models. The proposed reconstruction procedure employs a volumetric method based on a novel projection test for the production of a visual hull. While the presented algorithm shares certain aspects with the space carving algorithm, it is, nevertheless, first enhanced with the lightness compensating image comparison method, and then refined using ant colony optimization. The algorithm is fast, computationally simple and results in accurate representations of the input scenes. In addition, compared to previous publications, the particular nature of the proposed algorithm allows accurate 3D volumetric measurements under demanding lighting environmental conditions, due to the fact that it can cope with uneven light scenes, resulting from the characteristics of the voxel dissimilarity measure applied. Besides, the intelligent behavior of the ant colony framework provides the opportunity to formulate the process as a combinatorial optimization problem, which can then be solved by means of a colony of cooperating artificial ants, resulting in very promising results. The method is validated with several real datasets, along with qualitative comparisons with other state-of-the-art 3D reconstruction techniques, following the Middlebury benchmark. (paper)
Comparison of metaheuristic techniques to determine optimal placement of biomass power plants
Energy Technology Data Exchange (ETDEWEB)
Reche-Lopez, P.; Ruiz-Reyes, N.; Garcia Galan, S. [Telecommunication Engineering Department, University of Jaen Polytechnic School, C/ Alfonso X el Sabio 28, 23700 Linares, Jaen (Spain); Jurado, F. [Electrical Engineering Department, University of Jaen Polytechnic School, C/ Alfonso X el Sabio 28, 23700 Linares, Jaen (Spain)
2009-08-15
This paper deals with the application and comparison of several metaheuristic techniques to optimize the placement and supply area of biomass-fueled power plants. Both, trajectory and population-based methods are applied for our goal. In particular, two well-known trajectory method, such as Simulated Annealing (SA) and Tabu Search (TS), and two commonly used population-based methods, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are hereby considered. In addition, a new binary PSO algorithm has been proposed, which incorporates an inertia weight factor, like the classical continuous approach. The fitness function for the metaheuristics is the profitability index, defined as the ratio between the net present value and the initial investment. In this work, forest residues are considered as biomass source, and the problem constraints are: the generation system must be located inside the supply area, and its maximum electric power is 5 MW. The comparative results obtained by all considered metaheuristics are discussed. Random walk has also been assessed for the problem we deal with. (author)
Optimal Sizing and Location of Distributed Generators Based on PBIL and PSO Techniques
Directory of Open Access Journals (Sweden)
Luis Fernando Grisales-Noreña
2018-04-01
Full Text Available The optimal location and sizing of distributed generation is a suitable option for improving the operation of electric systems. This paper proposes a parallel implementation of the Population-Based Incremental Learning (PBIL algorithm to locate distributed generators (DGs, and the use of Particle Swarm Optimization (PSO to define the size those devices. The resulting method is a master-slave hybrid approach based on both the parallel PBIL (PPBIL algorithm and the PSO, which reduces the computation time in comparison with other techniques commonly used to address this problem. Moreover, the new hybrid method also reduces the active power losses and improves the nodal voltage profiles. In order to verify the performance of the new method, test systems with 33 and 69 buses are implemented in Matlab, using Matpower, for evaluating multiple cases. Finally, the proposed method is contrasted with the Loss Sensitivity Factor (LSF, a Genetic Algorithm (GA and a Parallel Monte-Carlo algorithm. The results demonstrate that the proposed PPBIL-PSO method provides the best balance between processing time, voltage profiles and reduction of power losses.
Tsai, Wen-Ping; Chang, Fi-John; Chang, Li-Chiu; Herricks, Edwin E.
2015-11-01
Flow regime is the key driver of the riverine ecology. This study proposes a novel hybrid methodology based on artificial intelligence (AI) techniques for quantifying riverine ecosystems requirements and delivering suitable flow regimes that sustain river and floodplain ecology through optimizing reservoir operation. This approach addresses issues to better fit riverine ecosystem requirements with existing human demands. We first explored and characterized the relationship between flow regimes and fish communities through a hybrid artificial neural network (ANN). Then the non-dominated sorting genetic algorithm II (NSGA-II) was established for river flow management over the Shihmen Reservoir in northern Taiwan. The ecosystem requirement took the form of maximizing fish diversity, which could be estimated by the hybrid ANN. The human requirement was to provide a higher satisfaction degree of water supply. The results demonstrated that the proposed methodology could offer a number of diversified alternative strategies for reservoir operation and improve reservoir operational strategies producing downstream flows that could meet both human and ecosystem needs. Applications that make this methodology attractive to water resources managers benefit from the wide spread of Pareto-front (optimal) solutions allowing decision makers to easily determine the best compromise through the trade-off between reservoir operational strategies for human and ecosystem needs.
Directory of Open Access Journals (Sweden)
G. Esteve-Asensio
2009-01-01
Full Text Available We propose and compare three novel heuristics for the calculation of the optimal cell radius in mobile networks based on Wideband Code Division Multiple Access (WCDMA technology. The proposed heuristics solve the problem of the load assignment and cellular radius calculation. We have tested our approaches with experiments in multiservices scenarios showing that the proposed heuristics maximize the cell radius, providing the optimum load factor assignment. The main application of these algorithms is strategic planning studies, where an estimation of the number of Nodes B of the mobile operator, at a national level, is required for economic analysis. In this case due to the large number of different scenarios considered (cities, towns, and open areas other methods than simulation need to be considered. As far as we know, there is no other similar method in the literature and therefore these heuristics may represent a novelty in strategic network planning studies. The proposed heuristics are implemented in a strategic planning software tool and an example of their application for a case in Spain is presented. The proposed heuristics are used for telecommunications regulatory studies in several countries.
Energy Technology Data Exchange (ETDEWEB)
Castillo M, J.A. [ININ, 52045 Ocoyoacac, Estado de Mexico (Mexico)
2003-07-01
The basic elements of the Tabu search technique are presented, putting emphasis in the qualities that it has in comparison with the traditional methods of optimization known as in descending pass. Later on some modifications are sketched that have been implemented in the technique along the time, so that this it is but robust. Finally they are given to know some areas where this technique has been applied, obtaining successful results. (Author)
Directory of Open Access Journals (Sweden)
Maryam Ashouri
2017-07-01
Full Text Available Vehicle routing problem (VRP is a Nondeterministic Polynomial Hard combinatorial optimization problem to serve the consumers from central depots and returned back to the originated depots with given vehicles. Furthermore, two of the most important extensions of the VRPs are the open vehicle routing problem (OVRP and VRP with simultaneous pickup and delivery (VRPSPD. In OVRP, the vehicles have not return to the depot after last visit and in VRPSPD, customers require simultaneous delivery and pick-up service. The aim of this paper is to present a combined effective ant colony optimization (CEACO which includes sweep and several local search algorithms which is different with common ant colony optimization (ACO. An extensive numerical experiment is performed on benchmark problem instances addressed in the literature. The computational result shows that suggested CEACO approach not only presented a very satisfying scalability, but also was competitive with other meta-heuristic algorithms in the literature for solving VRP, OVRP and VRPSPD problems. Keywords: Meta-heuristic algorithms, Vehicle Routing Problem, Open Vehicle Routing Problem, Simultaneously Pickup and Delivery, Ant Colony Optimization.
Directory of Open Access Journals (Sweden)
B. Thamaraikannan
2014-01-01
Full Text Available This paper studies in detail the background and implementation of a teaching-learning based optimization (TLBO algorithm with differential operator for optimization task of a few mechanical components, which are essential for most of the mechanical engineering applications. Like most of the other heuristic techniques, TLBO is also a population-based method and uses a population of solutions to proceed to the global solution. A differential operator is incorporated into the TLBO for effective search of better solutions. To validate the effectiveness of the proposed method, three typical optimization problems are considered in this research: firstly, to optimize the weight in a belt-pulley drive, secondly, to optimize the volume in a closed coil helical spring, and finally to optimize the weight in a hollow shaft. have been demonstrated. Simulation result on the optimization (mechanical components problems reveals the ability of the proposed methodology to find better optimal solutions compared to other optimization algorithms.
Dual-phase helical CT using bolus triggering technique: optimization of transition time
International Nuclear Information System (INIS)
Choi, Young Ho; Kim, Tae Kyoung; Park, Byung Kwan; Koh, Young Hwan; Han, Joon Koo; Choi, Byung Ihn
1999-01-01
To optimize the transition time between the triggering point in monitoring scanning and the initiation of diagnostic hepatic arterial phase (HAP) scanning in hepatic spiral CT, using a bolus triggering technique. One hundred consecutive patients with focal hepatic lesion were included in this study. Patients were randomized into two groups. Transition times of 7 and 11 seconds were used in group 1 and 2, respectively. In all patients, bolus triggered HAP spiral CT was obtained using a semi-automatic bolus tracking program after the injection of 120mL of non-ionic contrast media at a rate of 3mL/sec. When aortic enhancement reached 90 HU, diagnostic HAP scanning began after a given transition time. From images of group 1 and group 2, the degree of parenchymal enhancement of the liver and tumor-to-liver attenuation difference were measured. Also, for qualitative analysis, conspicuity of the hepatic artery and hypervascular tumor was scored and analyzed. Hepatic parenchymal enhancement on HAP was 12.07 + /-6.44 HU in group 1 and 16.03 + /-5.80 HU in group 2 (p .05). In the evaluation of conspicuity of hepatic artery, there was no statistically significant difference between the two groups (p > .05). The conspicuity of hypervascular tumors in group 2 was higher than in group 1 (p < .05). HAP spiral CT using a bolus triggering technique with a transition time of 11 seconds provides better HAP images than when the transition time is 7 seconds
Directory of Open Access Journals (Sweden)
Cuong D. Tran
2015-05-01
Full Text Available It is well recognised that zinc deficiency is a major global public health issue, particularly in young children in low-income countries with diarrhoea and environmental enteropathy. Zinc supplementation is regarded as a powerful tool to correct zinc deficiency as well as to treat a variety of physiologic and pathologic conditions. However, the dose and frequency of its use as well as the choice of zinc salt are not clearly defined regardless of whether it is used to treat a disease or correct a nutritional deficiency. We discuss the application of zinc stable isotope tracer techniques to assess zinc physiology, metabolism and homeostasis and how these can address knowledge gaps in zinc supplementation pharmacokinetics. This may help to resolve optimal dose, frequency, length of administration, timing of delivery to food intake and choice of zinc compound. It appears that long-term preventive supplementation can be administered much less frequently than daily but more research needs to be undertaken to better understand how best to intervene with zinc in children at risk of zinc deficiency. Stable isotope techniques, linked with saturation response and compartmental modelling, also have the potential to assist in the continued search for simple markers of zinc status in health, malnutrition and disease.
Directory of Open Access Journals (Sweden)
Juan Antonio Castro Flores
Full Text Available ABSTRACT Mesial temporal sclerosis creates a focal epileptic syndrome that usually requires surgical resection of mesial temporal structures. Objective: To describe a novel operative technique for treatment of temporal lobe epilepsy and its clinical results. Methods: Prospective case-series at a single institution, performed by a single surgeon, from 2006 to 2012. A total of 120 patients were submitted to minimally-invasive keyhole transtemporal amygdalohippocampectomy. Results: Of the patients, 55% were male, and 85% had a right-sided disease. The first 70 surgeries had a mean surgical time of 2.51 hours, and the last 50 surgeries had a mean surgical time of 1.62 hours. There was 3.3% morbidity, and 5% mild temporal muscle atrophy. There was no visual field impairment. On the Engel Outcome Scale at the two-year follow-up, 71% of the patients were Class I, 21% were Class II, and 6% were Class III. Conclusion: This novel technique is feasible and reproducible, with optimal clinical results.
Optimization of Fluorescent Silicon Nano material Production Using Peroxide/ Acid/ Salt Technique
International Nuclear Information System (INIS)
Abuhassan, L.H.
2009-01-01
Silicon nano material was prepared using the peroxide/ acid/ salt technique in which an aqueous silicon-based salt solution was added to H 2 O 2 / HF etchants. In order to optimize the experimental conditions for silicon nano material production, the amount of nano material produced was studied as a function of the volume of the silicon salt solution used in the synthesis. A set of samples was prepared using: 0, 5, 10, 15, and 20 ml of an aqueous 1 mg/ L metasilicate solution. The area under the corresponding peaks in the infrared (ir) absorption spectra was used as a qualitative indicator to the amount of the nano material present. The results indicated that using 10 ml of the metasilicate solution produced the highest amount of nano material. Furthermore, the results demonstrated that the peroxide/ acid/ salt technique results in the enhancement of the production yield of silicon nano material at a reduced power demand and with a higher material to void ratio. A model in which the silicon salt forms a secondary source of silicon nano material is proposed. The auxiliary nano material is deposited into the porous network causing an increase in the amount of nano material produced and a reduction in the voids present. Thus a reduction in the resistance of the porous layer, and consequently reduction in the power required, are expected. (author)
A New Technique of Removing Blind Spots to Optimize Wireless Coverage in Indoor Area
Directory of Open Access Journals (Sweden)
A. W. Reza
2013-01-01
Full Text Available Blind spots (or bad sampling points in indoor areas are the positions where no signal exists (or the signal is too weak and the existence of a receiver within the blind spot decelerates the performance of the communication system. Therefore, it is one of the fundamental requirements to eliminate the blind spots from the indoor area and obtain the maximum coverage while designing the wireless networks. In this regard, this paper combines ray-tracing (RT, genetic algorithm (GA, depth first search (DFS, and branch-and-bound method as a new technique that guarantees the removal of blind spots and subsequently determines the optimal wireless coverage using minimum number of transmitters. The proposed system outperforms the existing techniques in terms of algorithmic complexity and demonstrates that the computation time can be reduced as high as 99% and 75%, respectively, as compared to existing algorithms. Moreover, in terms of experimental analysis, the coverage prediction successfully reaches 99% and, thus, the proposed coverage model effectively guarantees the removal of blind spots.
Tran, Cuong D.; Gopalsamy, Geetha L.; Mortimer, Elissa K.; Young, Graeme P.
2015-01-01
It is well recognised that zinc deficiency is a major global public health issue, particularly in young children in low-income countries with diarrhoea and environmental enteropathy. Zinc supplementation is regarded as a powerful tool to correct zinc deficiency as well as to treat a variety of physiologic and pathologic conditions. However, the dose and frequency of its use as well as the choice of zinc salt are not clearly defined regardless of whether it is used to treat a disease or correct a nutritional deficiency. We discuss the application of zinc stable isotope tracer techniques to assess zinc physiology, metabolism and homeostasis and how these can address knowledge gaps in zinc supplementation pharmacokinetics. This may help to resolve optimal dose, frequency, length of administration, timing of delivery to food intake and choice of zinc compound. It appears that long-term preventive supplementation can be administered much less frequently than daily but more research needs to be undertaken to better understand how best to intervene with zinc in children at risk of zinc deficiency. Stable isotope techniques, linked with saturation response and compartmental modelling, also have the potential to assist in the continued search for simple markers of zinc status in health, malnutrition and disease. PMID:26035248
Partner Selection Optimization Model of Agricultural Enterprises in Supply Chain
Feipeng Guo; Qibei Lu
2013-01-01
With more and more importance of correctly selecting partners in supply chain of agricultural enterprises, a large number of partner evaluation techniques are widely used in the field of agricultural science research. This study established a partner selection model to optimize the issue of agricultural supply chain partner selection. Firstly, it constructed a comprehensive evaluation index system after analyzing the real characteristics of agricultural supply chain. Secondly, a heuristic met...
A heuristic and hybrid method for the tank allocation problem in maritime bulk shipping
DEFF Research Database (Denmark)
Vilhelmsen, Charlotte; Larsen, Jesper; Lusby, Richard Martin
2016-01-01
In bulk shipping, ships often have multiple tanks and carry multiple inhomogeneous products at a time. When operating such ships it is therefore a major challenge to decide how to best allocate cargoes to available tanks while taking into account tank capacity, safety restrictions, ship stability...... finding a feasible solution. We have developed a heuristic that can efficiently find feasible cargo allocations. Computational results show that it can solve 99 % of the considered instances within 0.4 s and all of them if allowed longer time. We have also modified an optimality based method from...... the literature. The heuristic is much faster than this modified method on the vast majority of considered instances. However, the heuristic struggles on two instances which are relatively quickly solved by the modified optimality based method. These two methods therefore complement each other nicely and so, we...
A Heuristic and Hybrid Method for the Tank Allocation Problem in Maritime Bulk Shipping
DEFF Research Database (Denmark)
Vilhelmsen, Charlotte; Larsen, Jesper; Lusby, Richard Martin
In bulk shipping, ships often have multiple tanks and carry multiple inhomogeneous products at a time. When operating such ships it is therefore a major challenge to decide how to best allocate cargoes to available tanks while taking into account tank capacity, safety restrictions, ship stability...... finding a feasible solution. We have developed a heuristic that can efficiently find feasible cargo allocations. Computational results show that it can solve 99% of the considered instances within 0.4 seconds and all of them if allowed longer time. We have also modified an optimality based method from...... the literature. The heuristic is much faster than this modified method on the vast majority of considered instances. However, the heuristic struggles on two instances which are relatively quickly solved by the modified optimality based method. These two methods therefore complement each other nicely and so, we...
The Effect of Incentive Structure on Heuristic Decision Making: The Proportion Heuristic
Robert Oxoby
2007-01-01
When making judgments, individuals often utilize heuristics to interpret information. We report on a series of experiments designed to test the ways in which incentive mechanisms influence the use of a particular heuristic in decision-making. Specifically, we demonstrate how information regarding the number of available practice problems influences the behaviors of individuals preparing for an exam (the proportion heuristic). More importantly the extent to which this information influences be...
Anatomy-based transmission factors for technique optimization in portable chest x-ray
Liptak, Christopher L.; Tovey, Deborah; Segars, William P.; Dong, Frank D.; Li, Xiang
2015-03-01
Portable x-ray examinations often account for a large percentage of all radiographic examinations. Currently, portable examinations do not employ automatic exposure control (AEC). To aid in the design of a size-specific technique chart, acrylic slabs of various thicknesses are often used to estimate x-ray transmission for patients of various body thicknesses. This approach, while simple, does not account for patient anatomy, tissue heterogeneity, and the attenuation properties of the human body. To better account for these factors, in this work, we determined x-ray transmission factors using computational patient models that are anatomically realistic. A Monte Carlo program was developed to model a portable x-ray system. Detailed modeling was done of the x-ray spectrum, detector positioning, collimation, and source-to-detector distance. Simulations were performed using 18 computational patient models from the extended cardiac-torso (XCAT) family (9 males, 9 females; age range: 2-58 years; weight range: 12-117 kg). The ratio of air kerma at the detector with and without a patient model was calculated as the transmission factor. Our study showed that the transmission factor decreased exponentially with increasing patient thickness. For the range of patient thicknesses examined (12-28 cm), the transmission factor ranged from approximately 21% to 1.9% when the air kerma used in the calculation represented an average over the entire imaging field of view. The transmission factor ranged from approximately 21% to 3.6% when the air kerma used in the calculation represented the average signals from two discrete AEC cells behind the lung fields. These exponential relationships may be used to optimize imaging techniques for patients of various body thicknesses to aid in the design of clinical technique charts.
Hermawati, Setia; Lawson, Glyn
2016-09-01
Heuristics evaluation is frequently employed to evaluate usability. While general heuristics are suitable to evaluate most user interfaces, there is still a need to establish heuristics for specific domains to ensure that their specific usability issues are identified. This paper presents a comprehensive review of 70 studies related to usability heuristics for specific domains. The aim of this paper is to review the processes that were applied to establish heuristics in specific domains and identify gaps in order to provide recommendations for future research and area of improvements. The most urgent issue found is the deficiency of validation effort following heuristics proposition and the lack of robustness and rigour of validation method adopted. Whether domain specific heuristics perform better or worse than general ones is inconclusive due to lack of validation quality and clarity on how to assess the effectiveness of heuristics for specific domains. The lack of validation quality also affects effort in improving existing heuristics for specific domain as their weaknesses are not addressed. Copyright © 2016 Elsevier Ltd. All rights reserved.
A New Approach to Tuning Heuristic Parameters of Genetic Algorithms
Czech Academy of Sciences Publication Activity Database
Holeňa, Martin
2006-01-01
Roč. 3, č. 3 (2006), s. 562-569 ISSN 1790-0832. [AIKED'06. WSEAS International Conference on Artificial Intelligence , Knowledge Engineering and Data Bases. Madrid, 15.02.2006-17.02.2006] R&D Projects: GA ČR(CZ) GA201/05/0325; GA ČR(CZ) GA201/05/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary optimization * genetic algorithms * heuristic parameters * parameter tuning * artificial neural networks * convergence speed * population diversity Subject RIV: IN - Informatics, Computer Science
A NEW HEURISTIC ALGORITHM FOR MULTIPLE TRAVELING SALESMAN PROBLEM
Directory of Open Access Journals (Sweden)
F. NURIYEVA
2017-06-01
Full Text Available The Multiple Traveling Salesman Problem (mTSP is a combinatorial optimization problem in NP-hard class. The mTSP aims to acquire the minimum cost for traveling a given set of cities by assigning each of them to a different salesman in order to create m number of tours. This paper presents a new heuristic algorithm based on the shortest path algorithm to find a solution for the mTSP. The proposed method has been programmed in C language and its performance analysis has been carried out on the library instances. The computational results show the efficiency of this method.
A heuristic forecasting model for stock decision
Zhang, D.; Jiang, Q.; Li, X.
2005-01-01
This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural network prediction. Results are compared with a benchmark buy-and-hold strategy. ...
Psychology into economics: fast and frugal heuristics
Schilirò, Daniele
2015-01-01
The present essay focuses on the fast and frugal heuristics program set forth by Gerd Gigerenzer and his fellows. In particular it examines the contribution of Gigerenzer and Goldstein (1996) ‘Reasoning the Fast and Frugal Way: Models of Bounded Rationality’. This essay, following the theoretical propositions and the empirical evidence of Gigerenzer and Goldstein, points out that simple cognitive mechanisms such as fast and frugal heuristics can be capable of successful performance in real wo...
Arational heuristic model of economic decision making
Grandori, Anna
2010-01-01
The article discuss the limits of both the rational actor and the behavioral paradigms in explaining and guiding innovative decision making and outlines a model of economic decision making that in the course of being 'heuristic' (research and discovery oriented) is also 'rational' (in the broad sense of following correct reasoning and scientific methods, non 'biasing'). The model specifies a set of 'rational heuristics' for innovative decision making, for the various sub-processes of problem ...
Heuristic thinking makes a chemist smart.
Graulich, Nicole; Hopf, Henning; Schreiner, Peter R
2010-05-01
We focus on the virtually neglected use of heuristic principles in understanding and teaching of organic chemistry. As human thinking is not comparable to computer systems employing factual knowledge and algorithms--people rarely make decisions through careful considerations of every possible event and its probability, risks or usefulness--research in science and teaching must include psychological aspects of the human decision making processes. Intuitive analogical and associative reasoning and the ability to categorize unexpected findings typically demonstrated by experienced chemists should be made accessible to young learners through heuristic concepts. The psychology of cognition defines heuristics as strategies that guide human problem-solving and deciding procedures, for example with patterns, analogies, or prototypes. Since research in the field of artificial intelligence and current studies in the psychology of cognition have provided evidence for the usefulness of heuristics in discovery, the status of heuristics has grown into something useful and teachable. In this tutorial review, we present a heuristic analysis of a familiar fundamental process in organic chemistry--the cyclic six-electron case, and we show that this approach leads to a more conceptual insight in understanding, as well as in teaching and learning.
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Karthivashan G
2016-07-01
Full Text Available Govindarajan Karthivashan,1 Mas Jaffri Masarudin,2 Aminu Umar Kura,1 Faridah Abas,3,4 Sharida Fakurazi1,5 1Laboratory of Vaccines and Immunotherapeutics, Institute of Bioscience, 2Department of Cell and Molecular Biology, Faculty of Biotechnology and Biomolecular Sciences, 3Department of Food Science, Faculty of Food Science and Technology, 4Laboratory of Natural Products, Institute of Bioscience, 5Department of Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, Serdang, Selangor, Malaysia Abstract: This study involves adaptation of bulk or sequential technique to load multiple flavonoids in a single phytosome, which can be termed as “flavonosome”. Three widely established and therapeutically valuable flavonoids, such as quercetin (Q, kaempferol (K, and apigenin (A, were quantified in the ethyl acetate fraction of Moringa oleifera leaves extract and were commercially obtained and incorporated in a single flavonosome (QKA–phosphatidylcholine through four different methods of synthesis – bulk (M1 and serialized (M2 co-sonication and bulk (M3 and sequential (M4 co-loading. The study also established an optimal formulation method based on screening the synthesized flavonosomes with respect to their size, charge, polydispersity index, morphology, drug–carrier interaction, antioxidant potential through in vitro 1,1-diphenyl-2-picrylhydrazyl kinetics, and cytotoxicity evaluation against human hepatoma cell line (HepaRG. Furthermore, entrapment and loading efficiency of flavonoids in the optimal flavonosome have been identified. Among the four synthesis methods, sequential loading technique has been optimized as the best method for the synthesis of QKA–phosphatidylcholine flavonosome, which revealed an average diameter of 375.93±33.61 nm, with a zeta potential of -39.07±3.55 mV, and the entrapment efficiency was >98% for all the flavonoids, whereas the drug-loading capacity of Q, K, and A was 31.63%±0
Energy Technology Data Exchange (ETDEWEB)
Shah, Chirag [Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI (United States); Vicini, Frank A., E-mail: fvicini@beaumont.edu [Department of Radiation Oncology, William Beaumont Hospital, Royal Oak, MI (United States)
2011-11-15
As more women survive breast cancer, long-term toxicities affecting their quality of life, such as lymphedema (LE) of the arm, gain importance. Although numerous studies have attempted to determine incidence rates, identify optimal diagnostic tests, enumerate efficacious treatment strategies and outline risk reduction guidelines for breast cancer-related lymphedema (BCRL), few groups have consistently agreed on any of these issues. As a result, standardized recommendations are still lacking. This review will summarize the latest data addressing all of these concerns in order to provide patients and health care providers with optimal, contemporary recommendations. Published incidence rates for BCRL vary substantially with a range of 2-65% based on surgical technique, axillary sampling method, radiation therapy fields treated, and the use of chemotherapy. Newer clinical assessment tools can potentially identify BCRL in patients with subclinical disease with prospective data suggesting that early diagnosis and management with noninvasive therapy can lead to excellent outcomes. Multiple therapies exist with treatments defined by the severity of BCRL present. Currently, the standard of care for BCRL in patients with significant LE is complex decongestive physiotherapy (CDP). Contemporary data also suggest that a multidisciplinary approach to the management of BCRL should begin prior to definitive treatment for breast cancer employing patient-specific surgical, radiation therapy, and chemotherapy paradigms that limit risks. Further, prospective clinical assessments before and after treatment should be employed to diagnose subclinical disease. In those patients who require aggressive locoregional management, prophylactic therapies and the use of CDP can help reduce the long-term sequelae of BCRL.
International Nuclear Information System (INIS)
Shah, Chirag; Vicini, Frank A.
2011-01-01
As more women survive breast cancer, long-term toxicities affecting their quality of life, such as lymphedema (LE) of the arm, gain importance. Although numerous studies have attempted to determine incidence rates, identify optimal diagnostic tests, enumerate efficacious treatment strategies and outline risk reduction guidelines for breast cancer–related lymphedema (BCRL), few groups have consistently agreed on any of these issues. As a result, standardized recommendations are still lacking. This review will summarize the latest data addressing all of these concerns in order to provide patients and health care providers with optimal, contemporary recommendations. Published incidence rates for BCRL vary substantially with a range of 2–65% based on surgical technique, axillary sampling method, radiation therapy fields treated, and the use of chemotherapy. Newer clinical assessment tools can potentially identify BCRL in patients with subclinical disease with prospective data suggesting that early diagnosis and management with noninvasive therapy can lead to excellent outcomes. Multiple therapies exist with treatments defined by the severity of BCRL present. Currently, the standard of care for BCRL in patients with significant LE is complex decongestive physiotherapy (CDP). Contemporary data also suggest that a multidisciplinary approach to the management of BCRL should begin prior to definitive treatment for breast cancer employing patient-specific surgical, radiation therapy, and chemotherapy paradigms that limit risks. Further, prospective clinical assessments before and after treatment should be employed to diagnose subclinical disease. In those patients who require aggressive locoregional management, prophylactic therapies and the use of CDP can help reduce the long-term sequelae of BCRL.
Multidisciplinary Optimization of Tilt Rotor Blades Using Comprehensive Composite Modeling Technique
Chattopadhyay, Aditi; McCarthy, Thomas R.; Rajadas, John N.
1997-01-01
An optimization procedure is developed for addressing the design of composite tilt rotor blades. A comprehensive technique, based on a higher-order laminate theory, is developed for the analysis of the thick composite load-carrying sections, modeled as box beams, in the blade. The theory, which is based on a refined displacement field, is a three-dimensional model which approximates the elasticity solution so that the beam cross-sectional properties are not reduced to one-dimensional beam parameters. Both inplane and out-of-plane warping are included automatically in the formulation. The model can accurately capture the transverse shear stresses through the thickness of each wall while satisfying stress free boundary conditions on the inner and outer surfaces of the beam. The aerodynamic loads on the blade are calculated using the classical blade element momentum theory. Analytical expressions for the lift and drag are obtained based on the blade planform with corrections for the high lift capability of rotor blades. The aerodynamic analysis is coupled with the structural model to formulate the complete coupled equations of motion for aeroelastic analyses. Finally, a multidisciplinary optimization procedure is developed to improve the aerodynamic, structural and aeroelastic performance of the tilt rotor aircraft. The objective functions include the figure of merit in hover and the high speed cruise propulsive efficiency. Structural, aerodynamic and aeroelastic stability criteria are imposed as constraints on the problem. The Kreisselmeier-Steinhauser function is used to formulate the multiobjective function problem. The search direction is determined by the Broyden-Fletcher-Goldfarb-Shanno algorithm. The optimum results are compared with the baseline values and show significant improvements in the overall performance of the tilt rotor blade.
Becerra, Sandra C; Roy, Daniel C; Sanchez, Carlos J; Christy, Robert J; Burmeister, David M
2016-04-12
Bacterial infections are a common clinical problem in both acute and chronic wounds. With growing concerns over antibiotic resistance, treatment of bacterial infections should only occur after positive diagnosis. Currently, diagnosis is delayed due to lengthy culturing methods which may also fail to identify the presence of bacteria. While newer costly bacterial identification methods are being explored, a simple and inexpensive diagnostic tool would aid in immediate and accurate treatments for bacterial infections. Histologically, hematoxylin and eosin (H&E) and Gram stains have been employed, but are far from optimal when analyzing tissue samples due to non-specific staining. The goal of the current study was to develop a modification of the Gram stain that enhances the contrast between bacteria and host tissue. A modified Gram stain was developed and tested as an alternative to Gram stain that improves the contrast between Gram positive bacteria, Gram negative bacteria and host tissue. Initially, clinically relevant strains of Pseudomonas aeruginosa and Staphylococcus aureus were visualized in vitro and in biopsies of infected, porcine burns using routine Gram stain, and immunohistochemistry techniques involving bacterial strain-specific fluorescent antibodies as validation tools. H&E and Gram stain of serial biopsy sections were then compared to a modification of the Gram stain incorporating a counterstain that highlights collagen found in tissue. The modified Gram stain clearly identified both Gram positive and Gram negative bacteria, and when compared to H&E or Gram stain alone provided excellent contrast between bacteria and non-viable burn eschar. Moreover, when applied to surgical biopsies from patients that underwent burn debridement this technique was able to clearly detect bacterial morphology within host tissue. We describe a modification of the Gram stain that provides improved contrast of Gram positive and Gram negative microorganisms within host
A Modularity Degree Based Heuristic Community Detection Algorithm
Directory of Open Access Journals (Sweden)
Dongming Chen
2014-01-01
Full Text Available A community in a complex network can be seen as a subgroup of nodes that are densely connected. Discovery of community structures is a basic problem of research and can be used in various areas, such as biology, computer science, and sociology. Existing community detection methods usually try to expand or collapse the nodes partitions in order to optimize a given quality function. These optimization function based methods share the same drawback of inefficiency. Here we propose a heuristic algorithm (MDBH algorithm based on network structure which employs modularity degree as a measure function. Experiments on both synthetic benchmarks and real-world networks show that our algorithm gives competitive accuracy with previous modularity optimization methods, even though it has less computational complexity. Furthermore, due to the use of modularity degree, our algorithm naturally improves the resolution limit in community detection.
International Nuclear Information System (INIS)
Stieler, Florian; Yan, Hui; Lohr, Frank; Wenz, Frederik; Yin, Fang-Fang
2009-01-01
Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT) is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI) guided system was developed and examined. The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS). Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be 'translated' to a set of 'if-then rules' for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS), was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints). The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 ± 0.02%) and membership functions (3.9%), thus suggesting that the 'behavior' of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. The study demonstrated a feasible way
Directory of Open Access Journals (Sweden)
Wenz Frederik
2009-09-01
Full Text Available Abstract Background Parameter optimization in the process of inverse treatment planning for intensity modulated radiation therapy (IMRT is mainly conducted by human planners in order to create a plan with the desired dose distribution. To automate this tedious process, an artificial intelligence (AI guided system was developed and examined. Methods The AI system can automatically accomplish the optimization process based on prior knowledge operated by several fuzzy inference systems (FIS. Prior knowledge, which was collected from human planners during their routine trial-and-error process of inverse planning, has first to be "translated" to a set of "if-then rules" for driving the FISs. To minimize subjective error which could be costly during this knowledge acquisition process, it is necessary to find a quantitative method to automatically accomplish this task. A well-developed machine learning technique, based on an adaptive neuro fuzzy inference system (ANFIS, was introduced in this study. Based on this approach, prior knowledge of a fuzzy inference system can be quickly collected from observation data (clinically used constraints. The learning capability and the accuracy of such a system were analyzed by generating multiple FIS from data collected from an AI system with known settings and rules. Results Multiple analyses showed good agreements of FIS and ANFIS according to rules (error of the output values of ANFIS based on the training data from FIS of 7.77 ± 0.02% and membership functions (3.9%, thus suggesting that the "behavior" of an FIS can be propagated to another, based on this process. The initial experimental results on a clinical case showed that ANFIS is an effective way to build FIS from practical data, and analysis of ANFIS and FIS with clinical cases showed good planning results provided by ANFIS. OAR volumes encompassed by characteristic percentages of isodoses were reduced by a mean of between 0 and 28%. Conclusion The
Selection of the optimal radiotherapy technique for locally advanced hepatocellular carcinoma
International Nuclear Information System (INIS)
Lee, Ik-Jae; Seong, Jinsil; Koom, Woong-Sub; Kim, Yong-Bae; Jeon, Byeong-Chul; Kim, Joo-Ho; Han, Kwang-Hyub
2011-01-01
Various techniques are available for radiotherapy of hepatocellular carcinoma, including three-dimensional conformal radiotherapy, linac-based intensity-modulated radiotherapy and helical tomotherapy. The purpose of this study was to determine the optimal radiotherapy technique for hepatocellular carcinoma. Between 2006 and 2007, 12 patients underwent helical tomotherapy for locally advanced hepatocellular carcinoma. Helical tomotherapy computerized radiotherapy planning was compared with the best computerized radiotherapy planning for three-dimensional conformal radiotherapy and linac-based intensity-modulated radiotherapy for the delivery of 60 Gy in 30 fractions. Tumor coverage was assessed by conformity index, radical dose homogeneity index and moderated dose homogeneity index. Computerized radiotherapy planning was also compared according to the tumor location. Tumor coverage was shown to be significantly superior with helical tomotherapy as assessed by conformity index and moderated dose homogeneity index (P=0.002 and 0.03, respectively). Helical tomotherapy showed significantly lower irradiated liver volume at 40, 50 and 60 Gy (V40, V50 and V60, P=0.04, 0.03 and 0.01, respectively). On the contrary, the dose-volume of three-dimensional conformal radiotherapy at V20 was significantly smaller than those of linac-based intensity-modulated radiotherapy and helical tomotherapy in the remaining liver (P=0.03). Linac-based intensity-modulated radiotherapy showed better sparing of the stomach compared with helical tomotherapy in the case of separated lesions in both lobes (12.3 vs. 24.6 Gy). Helical tomotherapy showed the high dose-volume exposure to the left kidney due to helical delivery in the right lobe lesion. Helical tomotherapy achieved the best tumor coverage of the remaining normal liver. However, helical tomotherapy showed much exposure to the remaining liver at the lower dose region and left kidney. (author)
Directory of Open Access Journals (Sweden)
Wilmar Hernandez
2007-01-01
Full Text Available In this paper a survey on recent applications of optimal signal processing techniques to improve the performance of mechanical sensors is made. Here, a comparison between classical filters and optimal filters for automotive sensors is made, and the current state of the art of the application of robust and optimal control and signal processing techniques to the design of the intelligent (or smart sensors that todayÃ¢Â€Â™s cars need is presented through several experimental results that show that the fusion of intelligent sensors and optimal signal processing techniques is the clear way to go. However, the switch between the traditional methods of designing automotive sensors and the new ones cannot be done overnight because there are some open research issues that have to be solved. This paper draws attention to one of the open research issues and tries to arouse researcherÃ¢Â€Â™s interest in the fusion of intelligent sensors and optimal signal processing techniques.