CHEN Guoyu and LI Junhua, “A Research Mode Based Evolutionary Algorithm for Many-Objective Optimization,” Chinese Journal of Electronics, vol. 28, no. 4, pp. 764-772, 2019, doi: 10.1049/cje.2019.05.003
Citation: CHEN Guoyu and LI Junhua, “A Research Mode Based Evolutionary Algorithm for Many-Objective Optimization,” Chinese Journal of Electronics, vol. 28, no. 4, pp. 764-772, 2019, doi: 10.1049/cje.2019.05.003

A Research Mode Based Evolutionary Algorithm for Many-Objective Optimization

doi: 10.1049/cje.2019.05.003
Funds:  This work is supported by the National Natural Science Foundation of China (No.61440049, No.61866025) and the Jiangxi Provincial Natural Science Foundation (No.20161BAB202038, No.20181BCB24008).
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  • Corresponding author: LI Junhua (corresponding author) was born in Jiangxi Province, China, in 1974. He received Ph.D. degree from Nanjing University of Aeronautics and Astronautics, China, in 2009. He is currently the professor in Nanchang Hangkong University. His research interests include evolutionary computation and intelligent control. (Email:jhlee126@126.com)
  • Received Date: 2018-05-21
  • Rev Recd Date: 2018-11-03
  • Publish Date: 2019-07-10
  • The development of algorithms to solve Many-objective optimization problems (MaOPs) has attracted significant research interest in recent years. Solving various types of Pareto front (PF) is a daunting challenge for evolutionary algorithm. A Research mode based evolutionary algorithm (RMEA) is proposed for many-objective optimization. The archive in the RMEA is used to store non-dominated solutions that can reflect the shape of the PF to guide the reference vector adaptation. Information concerning the population is collected, once the number of non-dominated solutions reaches its limit after many generations without exceeding a given threshold, RMEA introduces a research mode that generates more reference vectors to search through the solutions. The proposed algorithm showed competitive performance with four state-of-the-art evolutionary algorithms in a large number of experiments.
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