Open AccessA Research Mode Based Evolutionary Algorithm for Many‐Objective Optimization
Author(s) -
Chen Guoyu,
Li Junhua
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2019.05.003
Subject(s) - computer science , mode (computer interface) , evolutionary algorithm , optimization algorithm , algorithm , mathematical optimization , artificial intelligence , mathematics , operating system
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.