A dynamic neighborhood balancing-based multi-objective particle swarm optimization for multi-modal problems

•The purpose of this study is to solve multi-modal multi-objective problems.•Using the adaptive parameter adjustment strategy to extend the search space.•The dynamic neighborhood forming strategy can exchange the information between particles in time.•The mutation operator is embedded to make the pa...

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Bibliographic Details
Published in:Expert systems with applications Vol. 205; p. 117713
Main Authors: Gu, Qinghua, Wang, Qian, Chen, Lu, Li, Xiaoguang, Li, Xuexian
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.11.2022
Subjects:
Adaptive parameters
Dynamic neighborhood
Multi-modal multi-objective problem
Mutation operator
Particle swarm optimization
Dynamic neighborhood
Mutation operator
Particle swarm optimization
Multi-modal multi-objective problem
Adaptive parameters
ISSN:0957-4174, 1873-6793
Online Access:Get full text
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Summary:•The purpose of this study is to solve multi-modal multi-objective problems.•Using the adaptive parameter adjustment strategy to extend the search space.•The dynamic neighborhood forming strategy can exchange the information between particles in time.•The mutation operator is embedded to make the particle jump out of the local optimum. To solve the multi-modal multi-objective optimization problems which may have two or more Pareto-optimal solutions with the same fitness value, a new multi-objective particle swarm optimizer with a dynamic neighborhood balancing mechanism (DNB-MOPSO) is proposed in this paper. First, an adaptive parameter adjustment strategy is developed to balance the local and global search, which takes the difference among niches into consideration. Second, according to evolutionary states, a mutation operator is alternatively utilized to construct new solutions for escaping from the local optima. Then, combined with current niching methods, the dynamic neighborhood reform strategy of non-overlapping regions is properly implemented, which can enhance the exploration and keep the population diversity in the decision space. To validate the effectiveness of the proposed algorithm, DNB-MOPSO is compared with the other five popular multi-objective optimization algorithms. It is also applied to solve a real-world problem. The experimental results show the superiority of the proposed algorithm, especially in locating more optimal solutions in the decision space while obtaining the well-distributed Pareto fronts.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2022.117713