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A parallel multiple reference point approach for multi-objective optimization

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第2期205卷 390-400页

作者： Figueira, J. R. Liefooghe, A. Talbi, E. -G. Wierzbicki, A. P. Univ Lille 1 LIFL CNRS INRIA Lille Nord Europe F-59650 Villeneuve Dascq France King Saud Univ Riyadh Saudi Arabia Inst Natl Telecommun PL-04894 Warsaw Poland Univ Paris 09 LAMSADE CNRS UMR 7024 F-75775 Paris France Univ Tecn Lisboa Inst Super Tecn CEG Ctr Management Studies P-2780990 Porto Salvo Portugal

This paper presents a multiple reference point approach for multi-objective optimization problems of discrete and combinatorial nature. When approximating the Pareto Frontier, multiple reference points can be used instead of traditional techniques. These multiple reference points can easily be implemented in a parallel algorithmic framework. The reference points can be uniformly distributed within a region that covers the Pareto Frontier. An evolutionary algorithm is based on an achievement scalarizing function that does not impose any restrictions with respect to the location of the reference points in the objective space. Computational experiments are performed on a bi-objective flow-shop scheduling problem. Results, quality measures as well as a statistical analysis are reported in the paper. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Multiple objective programming Parallel computing Multiple reference point approach evolutionary computations Bi-objective flow-shop scheduling

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A decomposition-based multi-objective evolutionary algorithm with quality indicator

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SWARM AND evolutionary COMPUTATION 2018年 39卷 339-355页

作者： Luo, Jianping Yang, Yun Li, Xia Liu, Qiqi Chen, Minrong Gao, Kaizhou Shenzhen Univ Coll Informat Engn Shenzhen 518060 Peoples R China Shenzhen Key Lab Media Secur Shenzhen 518060 Peoples R China South China Normal Univ Guangzhou 510631 Guangdong Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

The issue of integrating preference information into multi-objective optimization is considered, and a multi objective framework based on decomposition and preference information, called indicator-based MOEA/D (IBMOEA/D), is presented in this study to handle the multi-objective optimization problems more effectively. The proposed algorithm uses a decomposition-based strategy for evolving its working population, where each individual represents a subproblem, and utilizes a binary quality indicator-based selection for maintaining the external population. Information obtained from the quality improvement of individuals is used to determine which subproblem should be invested at each generation by a power law distribution probability. Thus, the indicator-based selection and the decomposition strategy can complement each other. Through the experimental tests on seven many-objective optimization problems and one discrete combinatorial optimization problem, the proposed algorithm is revealed to perform better than several state-of-the-art multi-objective evolutionary algorithms. The effectiveness of the proposed algorithm is also analyzed in detail.

关键词： evolutionary computations Multi-objective optimization Algorithm diversity Decomposition Indicator-based

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Opposition-based learning for competitive hub location: A bi-objective biogeography-based optimization algorithm

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KNOWLEDGE-BASED SYSTEMS 2017年 128卷 1-19页

作者： Niknamfar, Amir Hossein Niaki, Seyed Taghi Akhavan Niaki, Seyed Armin Akhavan Islamic Azad Univ Qazvin Branch Young Researchers & Elite Club Qazvin Iran Sharif Univ Technol Dept Ind Engn POB 11155-9414 Azadi Ave Tehran *** Iran West Virginia Univ Dept Stat Morgantown WV 26506 USA

This paper introduces a new hub-and-center transportation network problem for a new company competing against an operating company. The new company intends to locate p hubs and assign the center nodes to the located hubs in order to form origin-destination pairs. It desires not only to maximize the total captured flow in the market but also aims to minimize the total transportation cost. Three competition rules are established between the companies which must be abided. According to the competition rules, the new company can capture the full percentage of the traffic in each origin-destination pair if its transportation cost for each route is significantly less than of the competitor. If its transportation cost for each route is not significantly less than one of the competitors, only a certain percentage of the traffic can be captured. A bi-objective optimization model is proposed for the hub location, problem on hand under a competitive environment. As the problem is shown to be NP-hard, a novel meta-heuristic algorithm called multi-objective biogeography-based optimization is developed. As there is no benchmark in the literature, a popular non-dominated sorting algorithm is utilized to validate the results obtained. Moreover, to enhance the performance of the proposed Pareto-based algorithms, this paper intends to develop a binary opposition-based learning as a diversity mechanism for both algorithms. The algorithms are tuned to solve the problem, based on which their performances are compared, ranked, and analyzed statistically. Finally, the applicability of the proposed approach and the solution methodologies are demonstrated in three steps. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Competitive hub location evolutionary computations Binary opposition-based learning Multi-objective biogeography-based optimization Non-dominated sorting genetic algorithm

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Backtracking search optimization heuristics for nonlinear Hammerstein controlled auto regressive auto regressive systems

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ISA TRANSACTIONS 2019年 91卷 99-113页

作者： Mehmood, Ammara Chaudhary, Naveed Ishtiaq Zameer, Aneela Raja, Muhammad Asif Zahoor Pakistan Inst Engn & Appl Sci Dept Elect Engn Islamabad Pakistan Int Islamic Univ Dept Elect Engn Islamabad Pakistan Pakistan Inst Engn & Appl Sci Dept Comp & Informat Sci Islamabad Pakistan COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock Pakistan

In this work, novel application of evolutionary computational heuristics is presented for parameter identification problem of nonlinear Hammerstein controlled auto regressive auto regressive (NHCARAR) systems through global search competency of backtracking search algorithm (BSA), differential evolution (DE) and genetic algorithms (GAs). The mean squared error metric is used for the fitness function of NHCARAR system based on difference between actual and approximated design variables. Optimization of the cost function is conducted with BSA for NHCARAR model by varying degrees of freedom and noise variances. To verify and validate the worth of the presented scheme, comparative studies are carried out with its counterparts DE and GAs through statistical observations by means of weight deviation factor, root of mean squared error, and Thiel's inequality coefficient as well as complexity measures. (C) 2019 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Parameter estimation Hammerstein systems Backtracking search optimization Differential evolution Genetic algorithms evolutionary computations

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Designing RBF Networks Using the Agent-Based Population Learning Algorithm

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NEW GENERATION COMPUTING 2014年第3-4期32卷 331-351页

作者： Czarnowski, Ireneusz Jedrzejowicz, Piotr Gdynia Maritime Univ Dept Informat Syst PL-81225 Gdynia Poland

Radial Basis Function Neural Networks (RBFNs) are nowadays quite popular due to their ability to discover and approximate complex nonlinear dependencies within the data under analysis. Performance of the RBF network depends on numerous factors related to its initialization and training. The paper proposes an approach to the radial basis function networks design, where initial parameters of the network, output weights and parameters of the transfer function are set using the proposed agent-based population learning algorithm (PLA). The algorithm is validated experimentally. Advantages and main features of the PLA-based RBF designs are discussed basing on results of the computational experiment.

关键词： Neural network RBF networks evolutionary computations Population learning algorithm Multi-agent systems

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evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2010年第3期204卷 533-544页

作者： Syberfeldt, Anna Ng, Amos John, Robert I. Moore, Philip Univ Skovde Ctr Intelligent Automat SE-54148 Skovde Sweden De Montfort Univ Ctr Computat Intelligence Leicester LE1 9BH Leics England De Montfort Univ Ctr Mechatron Res Leicester LE1 9BH Leics England

Many real-world optimisation problems approached by evolutionary algorithms are subject to noise. When noise is present, the evolutionary selection process may become unstable and the convergence of the optimisation adversely affected. In this paper, we present a new technique that efficiently deals with noise in multi-objective optimisation. This technique aims at preventing the propagation of inferior solutions in the evolutionary selection due to noisy objective values. This is done by using an iterative resampling procedure that reduces the noise until the likelihood of selecting the correct solution reaches a given confidence level. To achieve an efficient utilisation of resources, the number of samples used per solution varies based on the amount of noise in the present area of the search space. The proposed algorithm is evaluated on the ZDT benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of engine component manufacturing in aviation industry, while the second real-world problem concerns the optimisation of a camshaft machining line in automotive industry. The results from the optimisations indicate that the proposed technique is successful in reducing noise, and it competes successfully with other noise handling techniques. (C) 2009 Elsevier B.V. All rights reserved.

关键词： evolutionary computations Multi-objective optimisation Noise Simulation

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evolutionary search for difficult problem instances to support the design of job shop dispatching rules

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2011年第1期212卷 22-32页

作者： Branke, Juergen Pickardt, Christoph W. Univ Warwick Warwick Business Sch Coventry CV4 7AL W Midlands England

Dispatching rules are simple scheduling heuristics that are widely applied in industrial practice. Their popularity can be attributed to their ability to flexibly react to shop floor disruptions that are prevalent in many real-world manufacturing environments. However, it is a challenging and time-consuming task to design local, decentralised dispatching rules that result in a good global performance of a complex shop. An evolutionary algorithm is developed to generate job shop problem instances for which an examined dispatching rule fails to achieve a good solution due to a single suboptimal decision. These instances can be easily analysed to reveal limitations of that rule which helps with the design of better rules. The method is applied to a job shop problem from the literature, resulting in new best dispatching rules for the mean flow time measure. (C) 2011 Elsevier B.V. All rights reserved.

关键词： evolutionary computations Heuristics Scheduling Metaheuristics Distributed decision making

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Two genetic algorithms for solving the uncapacitated single allocation p-hub median problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2007年第1期182卷 15-28页

作者： Kratica, Jozef Stanimirovic, Zorica Tokic, Dusan Filipovic, Vladimir Serbian Acad Arts & Sci Inst Math Belgrade 11001 Serbia Univ Belgrade Fac Math Belgrade 11000 Serbia

This paper deals with the Uncapacitated Single Allocation p-Hub Median Problem (USApHMP). Two genetic algorithm (GA) approaches are proposed for solving this NP-hard problem. New encoding schemes are implemented with appropriate objective functions. Both approaches keep the feasibility of individuals by using specific representation and modified genetic operators. The numerical experiments were carried out on the standard ORLIB hub data set. Both methods proved to be robust and efficient in solving USApHMP with up to 200 nodes and 20 hubs. The second GA approach achieves all previously known optimal solutions and achieves the best-known solutions on large-scale instances. (c) 2006 Elsevier B.V. All rights reserved.

关键词： genetic algorithms evolutionary computations location p-hub median problem single allocation

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A Multi-swarm Competitive Algorithm Based on Dynamic Task Allocation Particle Swarm Optimization

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第12期43卷 8255-8274页

作者： Zhang, Lingjie Sun, Jianbo Guo, Chen Zhang, Hui Dalian Maritime Univ Inst Marine Engn Dalian 116026 Peoples R China Dalian Maritime Univ Inst Informat Sci & Technol Dalian 116026 Peoples R China

This paper presents a novel multi-swarm competitive algorithm based on the dynamic task allocation particle swarm optimization (MSCPSO-DTA) to solve global optimization problems. The MSCPSO-DTA consists of three main steps. Firstly, we propose a multi-swarm competitive (MSC) schedule to help the stagnant sub-swarm to jump out of local optima. When a certain condition is matched, the stagnant sub-swarm will be reinitialized with a chaotic strategy to begin a new search process in a different part of the search space. Secondly, in order to serve the MSC strategy which will reinitialize the stagnant sub-swarms and need the inertia weight to be increased correspondingly, a novel adaptive inertia weight strategy is proposed based on the maximum distance between the particles. Thirdly, a modified dynamic task allocation (DTA) strategy is adopted to make a better control of the balance between exploitation and exploration;specifically, with the help of the cumulative distribution function of normal distribution, the swarm is automatically divided into two labors to serve different tasks according to the position diversity. A set of benchmark functions are employed to test the performance of the proposed algorithm, as well as the contribution of each strategy employed in improving the performance of the algorithms. Experimental results demonstrate that, compared with other seven well-established optimization algorithms, the MSCPSO-DTA significantly performs the greatest improvement in terms of searching reliability, searching efficiency, convergence speed, and searching accuracy on most of the problems.

关键词： evolutionary computations Multi-swarm competitive schedule Particle swarm optimization Adaptive inertia weight Dynamic task allocation

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SOLVING VARIATIONAL PROBLEMS VIA evolutionary ALGORITHM

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INTERNATIONAL JOURNAL OF MODERN PHYSICS C 2013年第3期24卷 1350009-1-1350009-13页

作者： Korda, V. Yu Berezovsky, S. V. Molev, A. S. Klepikov, V. F. Korda, L. P. Natl Acad Sci Ukraine Inst Electrophys & Radiat Technol UA-61002 Kharkov Ukraine Natl Acad Sci Ukraine NSC Kharkov Inst Phys & Technol UA-61108 Kharkov Ukraine

We present the evolutionary algorithm that evolves the population of numerical solutions of the variational problem. The evolved solutions are model-independent, smooth, can have a predefined shape (if needed), and satisfy boundary or any other additional conditions (if imposed). To exemplify the performance of the proposed algorithm, we show how to solve the variational problem of searching for the spatially modulated distribution of the field of order parameter that gives a minimum to the Landau-type thermodynamic potential in the theory of ferroelectrics with incommensurate phases.

关键词： Variational problem evolutionary computations proper ferroelectrics incommensurate phase

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