# [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] 点击复制 两阶段搜索的多模态多目标差分进化算法() 分享到： var jiathis_config = { data_track_clickback: true };

42

2021年01期

9-14

2021-03-14

## 文章信息/Info

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

Author(s):
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:

TP301
DOI:
10.13705/j.issn.1671-6833.2021.01.002

A

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.

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