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A multi-resolution grid-based bacterial foraging optimization algorithm for multi-objective optimization problems

SWARM AND EVOLUTIONARY COMPUTATION 2022年 72卷

作者： Ji, Junzhong Weng, Yannan Yang, Cuicui Wu, Tongxuan Beijing Univ Technol Beijing Artificial Intelligence Inst Fac Informat Technol Beijing Key Lab Multimedia & Intelligent Software Beijing Peoples R China

In recent years, bacterial foraging optimization (BFO) has been used to solve multiobjective optimization problems (MOPs). However, BFO has not fully developed its potentials on MOPs for the reason of lacking of in-depth research on the optimization mechanisms and the diversity maintenance strategies. To solve it, this paper develops a multi-resolution grid-based BFO algorithm (called as MRBFO). MRBFO redesigns four tailored optimization mechanisms for MOPs including chemotaxis, conjugation, reproduction, and elimination and dispersal to search optimal nondominated solutions. Moreover, MRBFO defines a multi-resolution grid strategy to produce well-distributed diverse nondominated solutions. The performance of MRBFO is comprehensively evaluated by comparing it with several state-of-the-art algorithms on many benchmark test problems. The empirical results have sufficiently verified the advantages of MRBFO.

关键词： multiobjective optimization problems Bacterial foraging optimization Diversity maintenance Multi-resolution grid

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Assessing the Performance of Interactive multiobjective optimization Methods: A Survey

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ACM COMPUTING SURVEYS 2021年第4期54卷 85-85页

作者： Afsar, Bekir Miettinen, Kaisa Ruiz, Francisco Univ Jyvaskyla Fac Informat Technol POB 35 Agora FI-40014 Jyvaskyla Finland Univ Malaga Dept Appl Econ Math Fac Comercio & Gest Campus Teatinos Malaga 29071 Spain

Interactive methods are useful decision-making tools for multiobjective optimization problems, because they allow a decision-maker to provide her/his preference information iteratively in a comfortable way at the same time as (s)he learns about all different aspects of the problem. A wide variety of interactive methods is nowadays available, and they differ from each other in both technical aspects and type of preference information employed. Therefore, assessing the performance of interactive methods can help users to choose the most appropriate one for a given problem. This is a challenging task, which has been tackled from different perspectives in the published literature. We present a bibliographic survey of papers where interactive multiobjective optimization methods have been assessed (either individually or compared to other methods). Besides other features, we collect information about the type of decision-maker involved (utility or value functions, artificial or human decision-maker), the type of preference information provided, and aspects of interactive methods that were somehow measured. Based on the survey and on our own experiences, we identify a series of desirable properties of interactive methods that we believe should be assessed.

关键词： Interactive methods multiobjective optimization problems performance assessment decision-makers preference information

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Balancing Convergence and Diversity in multiobjective Immune Algorithm 12

Balancing Convergence and Diversity in Multiobjective Immune...

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12th International Conference on Advanced Computational Intelligence (ICACI)

作者： Li, Lingjie Lin, Wu Lin, Qiuzhen Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781728142487

Recently, multiobjective immune algorithms (MOIAs) become popular, which are designed for multiobjective optimization problems (MOPs). However, most existing MOIAs put more attention on maintaining diversity as the used clonal selection strategy will allocate more cloning for the sparse areas, which may hamper the convergence to speed to the optimal Pare-to front, especially for some complicated MOPs. To alleviate the phenomenon mentioned above, we propose a dynamic mechanism into traditional MOIAs in this paper, aiming to balance convergence and diversity, called BCD-MOIA. First, MOP will be decomposed into several single subproblems by decomposition method, and then these subproblems will be optimized simultaneously. Second, we propose a novel measure metric instead of the crowding distance to assign the clone number for each solution, which includes two main parts. The first part focuses on the diversity performance, i.e., the perpendicular distance between solution and its associated weight vectors. The second part uses the aggregated function values quantified by the decomposition method, which is more efficient for accelerating the convergence speed and maintaining diversity as well. Moreover, a dynamic mechanism is performed during the whole evolutionary process, focusing on diversity and convergence at different stages. By this way, our proposed algorithm can tradeoff the performance on convergence and diversity dynamically. The effectiveness of our proposed algorithm BCD-MOIA is validated by comparing with three competitive MOIAs and three multi-objective evolutionary algorithms for tackling two sets of complicated problems.

关键词： immune algorithm multiobjective optimization problems convergence and diversity

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A note on approximate Karush-Kuhn-Tucker conditions in locally Lipschitz multiobjective optimization

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optimization LETTERS 2019年第1期13卷 163-174页

作者： Nguyen Van Tuyen Yao, Jen-Chih Wen, Ching-Feng Univ Elect Sci & Technol China Sch Math Sci Chengdu Sichuan Peoples R China Hanoi Pedag Univ 2 Dept Math Phuc Yen Vinh Phuc Vietnam China Med Univ Ctr Gen Educ Taichung 40402 Taiwan Kaohsiung Med Univ Ctr Fundamental Sci Kaohsiung 80708 Taiwan Kaohsiung Med Univ Res Ctr Nonlinear Anal & Optimizat Kaohsiung 80708 Taiwan Kaohsiung Med Univ Hosp Dept Med Res Kaohsiung 80708 Taiwan

In the recent paper of Giorgi et al. (J Optim Theory Appl 171:70-89, 2016), the authors introduced the so-called approximate Karush-Kuhn-Tucker (AKKT) condition for smooth multiobjective optimization problems and obtained some AKKT-type necessary optimality conditions and sufficient optimality conditions for weak efficient solutions of such a problem. In this note, we extend these optimality conditions to locally Lipschitz multiobjective optimization problems using Mordukhovich subdifferentials. Furthermore, we prove that, under some suitable additional conditions, an AKKT condition is also a KKT one.

关键词： Approximate optimality conditions multiobjective optimization problems Locally Lipschitz functions Mordukhovich subdifferential

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multiobjective environmental adaptation method for solving environmental/economic dispatch problem

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EVOLUTIONARY INTELLIGENCE 2019年第2期12卷 305-319页

作者： Singh, Tribhuvan Mishra, Krishn Kumar Ranvijay Motilal Nehru Natl Inst Technol Allahabad Comp Sci & Engn Dept Allahabad Uttar Pradesh India

Environmental adaptation method is one of the evolutionary algorithms for solving single objective optimization problems. Although the algorithm converges very fast and produces diversified solutions, there are three weaknesses in it. In this paper, first we have given the solutions to resolve these weaknesses and then we have extended the modified method to deal with multiple conflicting objectives simultaneously. A permutation-based multiobjective environmental adaptation method (pMOEAM) has been suggested to solve the environmental/economic dispatch (EED) problem of the power system. In this paper, total generation cost and environmental emission have been taken as two objectives that need to be minimized simultaneously while meeting the load demand under equality and inequality constraints. Three test systems are considered to evaluate the performance of the proposed algorithm. The performance of the suggested algorithm is compared against five multiobjective algorithms. Extensive experimental results demonstrated that the pMOEAM method can obtain effective and feasible solutions for EED problem.

关键词： Environmental/economic dispatch problem Environmental adaptation method multiobjective evolutionary algorithms multiobjective optimization problems

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A multiobjective Test Suite with Hexagon Pareto Fronts and Various Feasible Regions

A Multiobjective Test Suite with Hexagon Pareto Fronts and V...

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Matsumoto, Takashi Masuyama, Naoki Nojima, Yusuke Ishibuchi, Hisao Osaka Prefecture Univ Grad Sch Engn Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan Southern Univ Sci & Technol Shenzhen Key Lab Computat Intelligence Univ Key Lab Evolving Intelligent Syst Guangdong Dept Comp Sci & Engn Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781728121536

The performance of a multiobjective Evolutionary Algorithm (MOEA) for many-objective optimization is often evaluated by multiobjective scalable test problems like DTLZ and WFG problems. This is because the scalable test problems are quite useful for an MOEA analysis. However, the scalable test problems do not have enough diversity of the shapes of the Pareto front and the feasible region to evaluate the capability of MOEAs. Previous studies showed that these shapes have a great impact on the performance of MOEAs. Thus, MOEAs should be evaluated on more test problems with different shapes of the Pareto front and the feasible region. In this study, the shapes of the Pareto front in the existing scalable test problems are examined from some viewpoints such as the distribution of optimal or worst solutions for each objective and the degree of the correspondence with the distribution of the weight vectors. The shape of the feasible region is also examined from the viewpoint of the spread of an initial population and the existence of dominance resistant solutions. According to the observations, we propose new shapes of the Pareto front and the feasible region to design a new scalable test suite. Experimental results show that the proposed test suite has totally different properties from the existing test problems.

关键词： multiobjective optimization problems scalable test suites many-objective optimization evolutionary multiobjective optimization algorithms

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Bare-Bones multiobjective Particle Swarm optimization Based on Parallel Cell Balanceable Fitness Estimation

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IEEE ACCESS 2018年 6卷 32493-32506页

作者： Qiao, Junfei Zhou, Hongbiao Yang, Cuili Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

The convergence and diversity of the Pareto optimal solutions is of great importance for multiobjective evolutionary algorithms. Based on parallel cell balanceable fitness estimation (PCBFE), a novel bare-bones multiobjective particle swarm optimization (NBBMOPSO) algorithm is proposed in this paper. First, the PCBFE strategy, which is based on the parallel cell mapping approach, is developed to retain the balance between the proximity and the diversity. After that, the PCB1-E strategy is adopted to maintain external archive and update leaders. Second, an adaptive update strategy for crossover probability is designed to repair the weakness of particle search. Finally, an elitism learning strategy is performed to exchange useful information among solutions in the external archive, which can enhance the capability of dropping out of the local Pareto front. To demonstrate the merits of NBBMOPSO for multiobjective optimization, Zitzler-Deb-Thiele (ZDT) and Deb-Thiele-Laumanns-Zitzler (DTLZ) test suits are examined with comparisons against the other seven state-of-the-art competitors. Experimental results show that the proposed NBBMOPSO outperforms all the other methods in terms of the chosen performance metrics.

关键词： multiobjective optimization problems bare-bones particle swarm optimization parallel cell balanceable fitness estimation adaptive crossover probability elitism learning strateg

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Performance Comparison of multiobjective Evolutionary Algorithms on problems with Partially Different Properties from Popular Test Suites

Performance Comparison of Multiobjective Evolutionary Algori...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Matsumoto, Takashi Masuyama, Naoki Nojima, Yusuke Ishibuchi, Hisao Osaka Prefecture Univ Dept Comp Sci & Intelligent Syst Sakai Osaka 5998531 Japan Southern Univ Sci & Technol SUSTech Dept Comp Sci & Engn Shenzhen Peoples R China

ISBN: (纸本)9781538666500

A multiobjective Evolutionary Algorithm (MOEA) is one of the effective approaches for solving multiobjective optimization problems (MOPs). The performance of MOEAs is evaluated mainly by scalable MOP test suites where the number of objectives can be arbitrarily specified. However, the number of scalable MOP test suites is quite limited and their properties are similar. Thus, there is a risk that the current research on MOEAs is specialized for some properties (i.e., a shape of feasible regions, a shape of the Pareto front, and a distance function) of existing scalable MOP test suites. In this paper, we focus on the above properties of two popular MOP test suites (i.e., DTLZ and WFG). Based on DTLZ and WFG, we create 12 MOPs which have partially different properties from those of DTLZ and WFG. Computational experiments show that the search performance of the state-of-the-art MOEAs strongly depends on three properties.

关键词： multiobjective optimization problems many-objective optimization scalable test suites evolutionary multiobjective optimization algorithms

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A decomposition-based binary ACO algorithm for the multiobjective UBQP

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NEUROCOMPUTING 2017年 246卷 58-68页

作者： Zangari, Murilo Pozo, Aurora Santana, Roberto Mendiburu, Alexander Univ Fed Parana UFPR Comp Sci Dept PO 19081 BR-81531970 Curitiba Parana Brazil Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Intelligent Syst Grp Paseo Manuel Lardizabal 1 San Sebastian 20080 Guipuzcoa Spain Univ Basque Country UPV EHU Dept Comp Architecture & Technol Intelligent Syst Grp Paseo Manuel Lardizabal 1 San Sebastian 20080 Guipuzcoa Spain

The multiobjective unconstrained binary quadratic programming (mUBQP) is a combinatorial optimization problem which is able to represent several multiobjective optimization problems (MOPs). The problem can be characterized by the number of variables, the number of objectives and the objective correlation strength. multiobjective evolutionary algorithms (MOEAs) are known as an efficient technique for solving MOPs. Moreover, several recent studies have shown the effectiveness of the MOEA/D framework applied to different MOPs. Previously, we have presented a preliminary study on an algorithm based on MOEA/D framework and the bio-inspired metaheuristic called binary ant colony optimization (BACO). The metaheuristic uses a positive feedback mechanism according to the best solutions found so far to update a probabilistic model which maintains the learned information. This paper presents the improved MOEA/D-BACO framework for solving the mUBQP. The components (i) mutation-like effect, and (ii) diversity preserving method are incorporated into the framework to enhance its search ability avoiding the premature convergence of the model and consequently maintaining a more diverse population of solutions. Experimental studies were conducted on a set of mUBQP instances. The results have shown that the proposed MOEA/D-BACO has outperformed MOEA/D, which uses genetic operators, in most of the test instances.. Moreover, the algorithm has produced competitive results in comparison to the best approximated Pareto fronts from the literature. (C) 2017 Elsevier B.V. All rights reserved.

关键词： multiobjective optimization problems Probabilistic modeling Binary Ant Colony optimization MOEA/D mUBQP

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Preference clustering-based mediating group decision-making (PCM-GDM) method for infrastructure asset management

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 83卷 206-214页

作者： Yoon, Yoojung Hastak, Makarand Cho, Kyuman West Virginia Univ Dept Civil & Environm Engn Morgantown WV 26506 USA Purdue Univ Div Construct Engn & Management W Lafayette IN 47907 USA Chosun Univ Dept Architectural Engn 309 Pilmun Daero Gwangju 61452 South Korea

The problem-solving decision-making process often requires involvement of a group of individuals who have differing interests and conflicting multiple evaluation criteria. Therefore, the greatest concern in multiobjective group decision-making problems is how to arrive at a best decision that is agreeable to all the members of the group. Many previous studies focused on handling this concern based on decision rules, such as the consensus or ranking selection approaches. Although many contributions to the literature were made by past studies on this issue, disagreement remains on finding an effective way to address the subjectivity issue in group decision-making. This paper introduces a new approach called the preference clustering-based mediating group decision-making (PCM-GDM) method for minimizing the subjectivity issue. The PCM-GDM method basically employs two concepts: (1) clustering the preferences of the group members in a decision and (2) utilizing a mediating agent as a final decision-making tool. The new approach was applied to a case study of sample concrete bridge decks in the state of Indiana. The results of this study confirm that the proposed approach can significantly improve the multicriteria group decision-making results by providing a way to exclude biased judgments by decision-makers that can interfere with the development of one best alternative. The proposed approach advanced the reliability of the conventional decision-making knowledge, which is dependent on a consensus or the ranking of approaches by human experts to reach one solution. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Group decision-making multiobjective optimization problems Mediating agent Infrastructure asset management

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