Paper Info
Title
<p>A two-archive model based evolutionary algorithm for multimodal multi-objective optimization problems</p>
Abstract
Multimodal multi-objective optimization (MMO) can offer more elegant solutions and provide diverse decisions to decision-makers in real world optimization problems. Many multimodal evolutionary mechanisms have been proposed to explore and exploit two solution spaces (i.e. decision space and objective space) in recent years. However, most existing methods only use single evolutionary operator to generate offsprings and ignore the advantage of using hybrid evolutionary algorithm. Moreover, it is still a great challenge to balance the effectiveness and efficiency simultaneously in the evolutionary process of MMO. In view of this, an efficient Two-Archive model based multimodal evolutionary algorithm is proposed in this paper. Two parallel offspring generation mechanisms based on competitive particle swarm optimizer and differential evolution are applied to expand two solution spaces with different evolutionary requirements. Moreover, niching local search scheme and reverse vector mutation strategy play roles in achieving better convergence and diversity. Finally, 22 MMO test problems are used to validate the superiority of the proposed method by comparing it with 5 state-of-the-art MMO algorithms. The proposed method is also expanded to solve 9 feature selection problems for validating the effectiveness of the proposed method on real world applications. (C)& nbsp;2022 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2022
10.1016/j.asoc.2022.108606
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Multimodal multi-objective optimization, Two-Archive, Evolutionary algorithm, Particle swarm optimizer, Differential evolution
Journal
119
ISSN
Citations 
PageRank 
1568-4946
0
0.34
References 
Authors
0
7
Name
Order
Citations
PageRank
Yi Hu1102.13
Jie Wang203.72
Jing J. Liang32073107.92
Yanli Wang461.75
Usman Ashraf500.34
Caitong Yue6237.41
Kunjie Yu762.09