Local binary pattern-based adaptive differential evolution for multimodal optimization problems

Zhao, Hong, Zhan, Zhi-Hui ORCID: 0000-0003-0862-0514, Lin, Ying ORCID: 0000-0003-4141-1490, Chen, Xiaofeng ORCID: 0000-0001-5858-5070, Luo, X ORCID: 0000-0002-0751-5045, Zhang, Jie, Kwong, Sam ORCID: 0000-0001-7484-7261 and Zhang, Jun ORCID: 0000-0001-7835-9871 (2019) Local binary pattern-based adaptive differential evolution for multimodal optimization problems. IEEE Transactions on Cybernetics, 50 (7). pp. 3343-3357. ISSN 2168-2267


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.

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Item type Article
URI https://vuir.vu.edu.au/id/eprint/45250
DOI 10.1109/TCYB.2019.2927780
Official URL https://ieeexplore.ieee.org/document/8792370
Subjects Current > FOR (2020) Classification > 4602 Artificial intelligence
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords differential evolution, intelligence algorithm, problem solving, optimisation algorithm
Citations in Scopus 79 - View on Scopus
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