Dual-Strategy Differential Evolution with Affinity Propagation Clustering for Multimodal Optimization Problems

Zi-Jia WANG, Zhi-Hui ZHAN, Ying LIN, Wei-Jie YU, Hua-Qiang YUAN, Tian-Long GU, Sam KWONG, Jun ZHANG

Research output: Journal PublicationsJournal Article (refereed)peer-review

137 Citations (Scopus)

Abstract

Multimodal optimization problem (MMOP), which targets at searching for multiple optimal solutions simultaneously, is one of the most challenging problems for optimization. There are two general goals for solving MMOPs. One is to maintain population diversity so as to locate global optima as many as possible, while the other is to increase the accuracy of the solutions found. To achieve these two goals, a novel dual-strategy differential evolution (DSDE) with affinity propagation clustering (APC) is proposed in this paper. The novelties and advantages of DSDE include the following three aspects. First, a dual-strategy mutation scheme is designed to balance exploration and exploitation in generating offspring. Second, an adaptive selection mechanism based on APC is proposed to choose diverse individuals from different optimal regions for locating as many peaks as possible. Third, an archive technique is applied to detect and protect stagnated and converged individuals. These individuals are stored in the archive to preserve the found promising solutions and are reinitialized for exploring more new areas. The experimental results show that the proposed DSDE algorithm is better than or at least comparable to the state-of-theart multimodal algorithms when evaluated on the benchmark problems from CEC2013, in terms of locating more global optima, obtaining higher accuracy solution, and converging with faster speed.
Original languageEnglish
Pages (from-to)894-908
JournalIEEE Transactions on Evolutionary Computation
Volume22
Issue number6
Early online date17 Nov 2017
DOIs
Publication statusPublished - Dec 2018
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundations of China under Grant 61772207, Grant 61402545, and Grant 61332002, in part by the Natural Science Foundations of Guangdong Province for Distinguished Young Scholars under Grant 2014A030306038, in part by the Project for Pearl River New Star in Science and Technology under Grant 201506010047, in part by the GDUPS (2016), in part by the Fundamental Research Funds for the Central Universities, and in part by the Hong Kong RGC General Research Fund under Grant 9042038 and Grant CityU 11205314.

Keywords

  • Affinity propagation clustering (APC)
  • archive technique
  • differential evolution (DE)
  • dual-strategy differential evolution (DSDE)
  • multimodal optimization problems (MMOPs)

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