An improved SPEA2 algorithm with adaptive selection of evolutionary operators scheme for multiobjective optimization problems
type: Item
instance of: scholarly article
title: An improved SPEA2 algorithm with adaptive selection of evolutionary operators scheme for multiobjective optimization problems
zbMATH Open document ID: 1400.90274
DOI: 10.1155/2016/8010346
author: Fuqing Zhao
author: Wenchang Lei
author: Yang Liu
author: Chuck Zhang
author: Weimin Ma
published in: Mathematical Problems in Engineering
publication date: 2018-10-12T00:00:00Z
review text: Summary: A fixed evolutionary mechanism is usually adopted in the multiobjective evolutionary algorithms and their operators are static during the evolutionary process, which causes the algorithm not to fully exploit the search space and is easy to trap in local optima. In this paper, a SPEA2 algorithm which is based on adaptive selection evolution operators (AOSPEA) is proposed. The proposed algorithm can adaptively select simulated binary crossover, polynomial mutation, and differential evolution operator during the evolutionary process according to their contribution to the external archive. Meanwhile, the convergence performance of the proposed algorithm is analyzed with Markov chain. Simulation results on the standard benchmark functions reveal that the performance of the proposed algorithm outperforms the other classical multiobjective evolutionary algorithms. Editorial remark: At the request of the authors, this article has been retracted. The article was published without the knowledge or approval of Zhen Chen who graduated from the School of Computer and Communication Technology, Lanzhou University of Technology and wrote this article.
Mathematics Subject Classification ID: 90C29
Mathematics Subject Classification ID: 90C59
zbMATH DE Number: 6953602
describes a project that uses: SPEA2
describes a project that uses: MOEA/D
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2016/8010346
OpenAlex ID: W2557246438
Wikidata QID: Q59141250
cites work: Q4424302
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cites work: Evolutionary Multi-Criterion Optimization
description: scientific article; zbMATH DE number 6953602