[1]汪慎文,张佳星,褚晓凯,等.两阶段搜索的多模态多目标差分进化算法[J].郑州大学学报(工学版),2021,42(01):9-14.[doi:10.13705/j.issn.1671-6833.2021.01.002]
　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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两阶段搜索的多模态多目标差分进化算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
42卷
期数:
2021年01期
页码:
9-14
栏目:
出版日期:
2021-03-14

文章信息/Info

Title:
Multimodal Multi-objective Differential Evolution Algorithm Based on Two-stage Search
作者:
汪慎文1; 2; 张佳星1; 2; 褚晓凯1; 2; 刘䫺3; 王晖4
河北地质大学信息工程学院;河北地质大学人工智能与机器学习研究室;中国信息通信研究院泰尔终端实验室;南昌工程学院信息工程学院;

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
关键词:
Keywords:
multimodal multi-objective optimization;  differential evolution;  two-stage search;  elite variation;  partition search
分类号:
TP301
DOI:
10.13705/j.issn.1671-6833.2021.01.002
文献标志码:
A
摘要:
在多模态多目标优化问题中，决策空间的多个Pareto最优解往往对应目标空间中Pareto前沿的同一位置。针对这种问题提出了一种两阶段搜索的多模态多目标差分进化算法，该算法将优化过程分为了精英搜索和分区搜索两个阶段。在精英搜索阶段通过精英变异策略生成高质量个体来保障种群的搜索精度和效率；在分区搜索阶段将决策空间分为若干子空间，利用已探测到的种群对各个子空间进行深度探索，降低问题复杂度的同时提高种群在决策空间的扩展性和均匀性。该算法在18个多模态多目标优化测试函数上与常见的5种算法进行了性能比较。实验结果证明了该算法的有效性。
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.

参考文献/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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更新日期/Last Update: 2021-03-15