An Improved SPEA2 Algorithm with Adaptive Selection of Evolutionary Operators Scheme for Multiobjective Optimization Problems

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...

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Bibliographic Details
Published in:Mathematical problems in engineering 2016-01, Vol.2016 (2016), p.1-20
Main Authors: Liu, Yang, Ma, Weimin, Lei, Wenchang, Zhao, Fuqing, Zhang, Chuck
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Archives & records
Colleges & universities
Computer simulation
Elitism
Evolutionary algorithms
Evolutionary computation
Fuzzy sets
Genetic algorithms
Markov chains
Mathematical analysis
Mathematical problems
Mathematical programming
Multiple objective
Multiple objective analysis
Mutation
Objectives
Operators
Operators (mathematics)
Optimization
Pareto optimum
Polynomials
Population
Researchers
Science
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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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2016/8010346