摘要
Many-objective optimization problems take challenges to multi-objective evolutionary algorithms.A number of nondominated solutions in population cause a difficult selection towards the Pareto front.To tackle this issue,a series of indicatorbased multi-objective evolutionary algorithms(MOEAs)have been proposed to guide the evolution progress and shown promising performance.This paper proposes an indicator-based manyobjective evolutionary algorithm calledε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA),which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effectiveε-indicator as a fitness assignment scheme to press the population towards the Pareto front.Compared with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives,the experimental results show thatε-MaOSFLA outperforms the competitors.
Many-objective optimization problems take challenges to multi-objective evolutionary algorithms. A number of non-dominated solutions in population cause a difficult selection towards the Pareto front. To tackle this issue, a series of indicatorbased multi-objective evolutionary algorithms(MOEAs) have been proposed to guide the evolution progress and shown promising performance. This paper proposes an indicator-based manyobjective evolutionary algorithm called ε-indicator-based shuffled frog leaping algorithm(ε-MaOSFLA), which adopts the shuffled frog leaping algorithm as an evolutionary strategy and a simple and effective ε-indicator as a fitness assignment scheme to press the population towards the Pareto front. Compared with four stateof-the-art MOEAs on several standard test problems with up to 50 objectives, the experimental results show that ε-MaOSFLA outperforms the competitors.
基金
supported by the Shenzhen Innovation Technology Program(JCYJ20160422112909302)
作者简介
Corresponding author:WANG Na,was born in 1977.She received her B.S.degree in electronic engineering from Dalian Maritime University in 1998.She later took a successive postgraduate and doctoral programs of study and was conferred a Ph.D.degree on signal and information processing by Dalian Maritime University in 2003.Now she is a professor of College of Electronics and Information Engineering at Shenzhen University.Her main research interests include intelligent computing,machine learning and pattern recognition.E-mail:wangna@szu.edu.cn;SU Yuchao,was born in 1992.He received his B.S.degree from Minnan Normal University,Zhangzhou,China in 2016.He is pursuing his M.S.degree in Shenzhen University,China.His research interests include single-,multi-,and many-objective optimization,and artificial neural network.E-mail:yuchaosu@126.com;LI Xia,was born in 1968.She received her B.S.and M.S.degrees in electronic engineering and signal and information processing from Xidian University in 1989 and 1992 respectively.She was later conferred a Ph.D.degree in Department of Information Engineering by the Chinese University of Hong Kong in 1997.She is a director of Shenzhen Key Laboratory of Advanced Communication and Information Processing.Her research interests include intelligent computing and its applications,image processing and pattern recognition.E-mail:lixia@szu.edu.cn;CHEN Xiaohong,was born in 1977.She received her B.S.and M.S.degrees in electronic engineering and signal and information processing from Liaoning University in 2003 and 2006 respectively.She later was conferred a Ph.D.degree on signal and information processing by Shenzhen University in 2015.Her main research interests include intelligent computing,machine learning and pattern recognition.E-mail:chenxh@szu.edu.cn;LIU Dui,was born in 1995.He received his B.S.degree from the College of Electronics and Information Engineering of Shenzhen University in 2017,where he is currently pursuing his M.S.degree.His main research interests include intelligent computing and machine learning.E-mail:liud@szu.edu.cn。
