Local Binary Pattern-Based Adaptive Differential Evolution for Multimodal Optimization Problems

Hong ZHAO, Zhi-Hui ZHAN, Ying LIN, Xiaofeng CHEN, Xiao-Nan LUO, Jie ZHANG, Sam KWONG, Jun ZHANG

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

105 Citations (Scopus)


The multimodal optimization problem (MMOP) requires the algorithm to find multiple global optima of the problem simultaneously. In order to solve MMOP efficiently, a novel differential evolution (DE) algorithm based on the local binary pattern (LBP) is proposed in this paper. The LBP makes use of the neighbors' information for extracting relevant pattern information, so as to identify the multiple regions of interests, which is similar to finding multiple peaks in MMOP. Inspired by the principle of LBP, this paper proposes an LBP-based adaptive DE (LBPADE) algorithm. It enables the LBP operator to form multiple niches, and further to locate multiple peak regions in MMOP. Moreover, based on the LBP niching information, we develop a niching and global interaction (NGI) mutation strategy and an adaptive parameter strategy (APS) to fully search the niching areas and maintain multiple peak regions. The proposed NGI mutation strategy incorporates information from both the niching and the global areas for effective exploration, while APS adjusts the parameters of each individual based on its own LBP information and guides the individual to the promising direction. The proposed LBPADE algorithm is evaluated on the extensive MMOPs test functions. The experimental results show that LBPADE outperforms or at least remains competitive with some state-of-the-art algorithms.
Original languageEnglish
Pages (from-to)3343-3357
JournalIEEE Transactions on Cybernetics
Issue number7
Early online date8 Aug 2019
Publication statusPublished - Jul 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the Outstanding Youth Science Foundation under Grant 61822602, in part by the National Natural Science Foundation of China under Grant 61772207 and Grant 61873097, in part by the Natural Science Foundations of Guangdong Province for Distinguished Young Scholars under Grant 2014A030306038, in part by the Guangdong Natural Science Foundation Research Team under Grant 2018B030312003, in part by the Guangdong–Hong Kong Joint Innovation Platform under Grant 2018B050502006, and in part by the Hong Kong GRF-RGC General Research Fund 9042489 under Grant CityU 11206317.


  • Adaptive differential evolution (DE)
  • DE
  • local binary pattern (LBP) strategy
  • multimodal optimization problems (MMOPs)


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