Multimodal Multi-objective Differential Evolution Algorithm Based on Two-stage Search

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Multimodal Multi-objective Differential Evolution Algorithm Based on Two-stage Search

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[1]WANG Shenwen,ZHANG Jiaxing,CHU Xiaokai,et al.Multimodal Multi-objective Differential Evolution Algorithm Based on Two-stage Search[J].Journal of Zhengzhou University (Engineering Science),2021,42(01):9-14.[doi:10.13705/j.issn.1671-6833.2021.01.002]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 42卷 Number of periods: 2021 01 Page number: 9-14 Column: Public date: 2021-03-14

Title:: Multimodal Multi-objective Differential Evolution Algorithm Based on Two-stage Search

Author(s):: WANG Shenwen1; 2; ZHANG Jiaxing1; 2; CHU Xiaokai1; 2; LIU Hong3; WANG Hui4; 1.School of Information Engineering, Hebei GEO University, Shijiazhuang 050031, China; 2.Laboratory of Artificial Intelligence and Machine Learning, Hebei GEO University, Shijiazhuang 050031, China; 3.Tel Terminal Laboratory, China Academy of Information and Communication, Beijing 100191, China; 4.School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, China

Keywords:: multimodal multi-objective optimization; differential evolution; two-stage search; elite variation; partition search

CLC:: TP301

DOI:: 10.13705/j.issn.1671-6833.2021.01.002

Abstract:: In multimodal multi-objective optimization problem, the same position of Pareto front often corresponded to multiple Pareto optimal solutions in decision space. However, the existing multi-objective optimization algorithms could only obtain one of the Pareto optimal solutions. Therefore, in this paper, a two-stage search multimodal multi-objective differential evolution algorithm was proposed, which divided the optimization process into two stages: elite search and partition search. In the elite search stage, elite mutation strategy was used to generate high-quality individuals to ensure the search accuracy and efficiency of the population. In the stage of partition search, the decision space was divided into several subspaces, and the detected population was used to explore each subspace in depth, so as to reduce the complexity of the problem and to improve the expansion and uniformity of the population in the decision space. The performance of the algorithm was compared with five classical algorithms NSGAII、MO_Ring_PSO_SCD、DN-NSGAII、Omni-Optimizer、MMODE on 18 multimodal and multi-objective optimization test functions, such as MMF1. Experimental results showed that there were 16 test functions in the performance index of Pareto approximation (PSP) of the proposed algorithm, which were better than the other five comparison algorithms.

References:: [1] 公茂果,焦李成,杨咚咚,等.进化多目标优化算法研究[J].软件学报,2009, 20(2): 271-289.
[2] 闫李,李超,柴旭朝,等.基于多学习多目标鸽群优化的动态环境经济调度[J].郑州大学学报(工学版), 2019, 40 (4): 8-14.
[3] YUE C T, QU B Y, LIANG J. A multi-objective particle swarm optimizer using ring topology for solving multimodal multi-objective problems[J]. IEEE tran-sactions on evolutionary computation,2017, 22(5): 805-817.
[4] LIANG J, XU W W, YUE C T, et al. Multimodal multi-objective optimization with differential evolution[J]. Swarm and evolutionary computation, 2019, 44:1028-1059.
[5] WANG Y, YANG Z L, GOU Y J, et al. A novel multi-objective competitive swarm optimization algorithm for multi-modal multi objective problems[C]//IEEE Congress on Evolutionary Computation (CEC). New York: IEEE, 2019:271-278.
[6] DEB K, TIWARI S. Omni-optimizer: a procedure for single and multi-objective optimization[C]// Evolutionary Multi-criterion Optimization, Third Inter-national Conference. Berlin: Springer, 2005: 47-61.
[7] LIANG J J, YUE C T, QU B Y. Multimodal multi-objective optimization: a preliminary study[C]//IEEE Congress on Evolutionary Computation (CEC). New York:IEEE,2016: 2454-2461.
[8] LIU Y, ISHIBUCHI H, NOJIMA Y, et al. A double-niched evolutionary algorithm and its behavior on polygon-based problems[C]// International Conference on Parallel Problem Solving from Nature. Berlin:Springer, 2018: 262-273.
[9] SHIR O M, PREUSS M, NAUJOKS B, et al. Enhancing decision space diversity in evolutionary multiobjective algorithms[C]// International Conference on Evolutionary Multi-Criterion Optimization. Berlin: Springer, 2009:95-109.
[10] FAN Q Q, YAN X F. Solving multimodal multi-objective problems through zoning search[J]. IEEE transactions on systems, man, and cybernetics: systems, 2019:1-12.
[11] QU B Y, LI Y, LIANG J, et al. A self-organized speciation based multi-objective particle swarm optimizer for multimodal multi-objective problems[J]. Applied soft computing, 2019, 86: 105886.
[12] ZHANG W Z, LI G Q, ZHANG W W, et al. A cluster based PSO with leader updating mechanism and ring-topology for multimodal multi-objective optimization[J]. Swarm and evolutionary computation, 2019,50: 100569.
[13] HU Y, WANG J, LIANG J, et al. A self-organizing multimodal multi-objective pigeon-inspired optimization algorithm[J]. Science China information sciences,2019,62(7)：69-85.
[14] 汪慎文,丁立新,张文生,等.差分进化算法研究进展[J].武汉大学学报(理学版),2014, 60(4):283-292.
[15] FAN Q Q, LI N, ZHANG Y L, et al. Zoning search using a hyper-heuristic algorithm[J]. Science China information sciences,2019,62(9): 189-191.
[16] DEB K, AGRAWAL S, PRATAP A, et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II[C]// International Conference on Parallel Problem Solving from Nature. Berlin: Springer, 2000:849-858.

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Last Update: 2021-03-15