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Retracted

At the request of the authors, the article titled “An Improved SPEA2 Algorithm with Adaptive Selection of Evolutionary Operators Scheme for Multiobjective Optimization Problems” [1] has been retracted.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.

View the full Retraction here.

References

  1. F. Zhao, W. Lei, W. Ma, Y. Liu, and C. Zhang, “An improved SPEA2 algorithm with adaptive selection of evolutionary operators scheme for multiobjective optimization problems,” Mathematical Problems in Engineering, vol. 2016, Article ID 8010346, 20 pages, 2016.
Mathematical Problems in Engineering
Volume 2016, Article ID 8010346, 20 pages
http://dx.doi.org/10.1155/2016/8010346
Research Article

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

1School of Computer and Communication Technology, Lanzhou University of Technology, Lanzhou 730050, China
2School of Economics and Management, Tongji University, Shanghai 200092, China
3H. Milton Stewart School of Industrial & Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA

Received 6 May 2016; Revised 26 July 2016; Accepted 28 August 2016

Academic Editor: Alfredo G. Hernández-Diaz

Copyright © 2016 Fuqing Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.