A hybrid biogeography-based optimization algorithm to solve high-dimensional optimization problems and real-world engineering problems

According to our extensive investigation, Biogeography-based optimization (BBO) and its variants have not been applied to solve high-dimensional optimization problems. To make a breakthrough in this field, a new BBO variant with hybrid migration operator and feedback differential evolution mechanism...

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Bibliographic Details
Published in:Applied soft computing Vol. 144; p. 110514
Main Authors: Zhang, Ziyu, Gao, Yuelin, Liu, Yingchun, Zuo, Wenlu
Format: Journal Article
Language:English
Published: Elsevier B.V 01.09.2023
Subjects:
Biogeography-based optimization
Engineering problems
Feedback differential evolution
High-dimensional optimization
Hybrid migration
Feedback differential evolution
Hybrid migration
Engineering problems
Biogeography-based optimization
High-dimensional optimization
ISSN:1568-4946, 1872-9681
Online Access:Get full text
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Summary:According to our extensive investigation, Biogeography-based optimization (BBO) and its variants have not been applied to solve high-dimensional optimization problems. To make a breakthrough in this field, a new BBO variant with hybrid migration operator and feedback differential evolution mechanism, HFBBO, is proposed. Firstly, the example learning method is used to ensure the inferior solutions cannot destroy the superior solutions. Secondly, the hybrid migration operator is presented to balance the exploration and exploitation. It enables the algorithm to switch freely between local search and global search. Finally, the feedback differential evolution mechanism is designed to replace the random mutation operator. HFBBO can select the mutation mode intelligently by this mechanism to avoid getting stuck in local optima. Meanwhile, the Markov model is established to prove the convergence of HFBBO, and the complexity is also discussed. A series experiments are carried out on 24 benchmark functions, CEC2017 test suite and 12 real-world engineering problems. The results of the Wilcoxon’s rank-sum test and Friedman’s test show that HFBBO has better competitiveness and stability than the 27 compared algorithms. Furtherly, the performance of HFBBO is compared on 1000, 2000, 5000 and 10000 dimensions, respectively. Experimental results show that this method can effectively solve high-dimensional optimization problems. [Display omitted] •HFBBO can self-regulate mutation mode through the feedback mechanism.•The damage of the inferior solutions to the superior solutions is avoided.•The exploration and exploitation of the population can reach an equilibrium state.•The performance of HFBBO is tested on 1000, 2000, 5000 and 10000 dimensions.•27 algorithms proposed in recent years are used to compare with HFBBO.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2023.110514