A multi-angle hierarchical differential evolution approach for multimodal optimization problems

Hong, Zhao ORCID: 0000-0001-5288-2656, Chen, Zong-Gan ORCID: 0000-0001-7585-5212, Liu, Dong, Zhan, Zhi-Hui ORCID: 0000-0003-0862-0514 and Zhang, Jun ORCID: 0000-0001-7835-9871 (2020) A multi-angle hierarchical differential evolution approach for multimodal optimization problems. IEEE Access, 8. pp. 178322-178335. ISSN 2169-3536


Multimodal optimization problem (MMOP) is one of the most common problems in engineering practices that requires multiple optimal solutions to be located simultaneously. An efficient algorithm for solving MMOPs should balance the diversity and convergence of the population, so that the global optimal solutions can be located as many as possible. However, most of existing algorithms are easy to be trapped into local peaks and cannot provide high-quality solutions. To better deal with MMOPs, considerations on the solution quality angle and the evolution stage angle are both taken into account in this paper and a multi-angle hierarchical differential evolution (MaHDE) algorithm is proposed. Firstly, a fitness hierarchical mutation (FHM) strategy is designed to balance the exploration and exploitation ability of different individuals. In the FHM strategy, the individuals are divided into two levels (i.e., low/high-level) according to their solution quality in the current niche. Then, the low/high-level individuals are applied to different guiding strategies. Secondly, a directed global search (DGS) strategy is introduced for the low-level individuals in the late evolution stage, which can improve the population diversity and provide these low-level individuals with the opportunity to re-search the global peaks. Thirdly, an elite local search (ELS) strategy is designed for the high-level individuals in the late evolution stage to refine their solution accuracy. Extensive experiments are developed to verify the performance of MaHDE on the widely used MMOPs test functions i.e., CEC’2013. Experimental results show that MaHDE generally outperforms the compared state-of-the-art multimodal algorithms.

Dimensions Badge

Altmetric Badge

Item type Article
URI https://vuir.vu.edu.au/id/eprint/46348
DOI 10.1109/ACCESS.2020.3027559
Official URL https://ieeexplore.ieee.org/document/9208664
Subjects Current > FOR (2020) Classification > 4602 Artificial intelligence
Current > FOR (2020) Classification > 4605 Data management and data science
Current > Division/Research > Institute for Sustainable Industries and Liveable Cities
Keywords directed global search, DSG, algorithms, MMOP, multimodal optimisation problem
Download/View statistics View download statistics for this item

Search Google Scholar

Repository staff login