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A Reference-Inspired Evolutionary Algorithm with Subregion Decomposition for Many-Objective Optimization

  • Xiaogang Fu
  • Jianyong SunEmail author
  • Qingfu Zhang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 650)

Abstract

In this paper, we propose a reference-inspired multiobjective evolutionary algorithm for many-objective optimisation. The main idea is (1) to summarise information inspired by a set of randomly generated reference points in the objective space to strengthen the selection pressure towards the Pareto front; and (2) to decompose the objective space into subregions for diversity management and offspring recombination. We showed that the mutual relationship between the objective vectors and the reference points provides not only a fine selection pressure, but also a balanced convergence-diversity information. The decomposition of the objective space into subregions is able to preserve the Pareto front’s diversity. A restricted stable match strategy is proposed to choose appropriate parent solutions from solution sets constructed at the subregions for high-quality offspring generation. Controlled experiments conducted on a commonly-used benchmark test suite have shown the effectiveness and competitiveness of the proposed algorithm in comparison with several state-of-the-art many-objective evolutionary algorithms.

Keywords

Many-objective optimization Reference-inspired Domain decomposition 

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of Electronic EngineeringShanghai Dianji UniversityShanghaiChina
  2. 2.School of Mathematics and StatisticsXi’an Jiaotong UniversityXi’anChina
  3. 3.Department of Computer ScienceCity University of Hong KongHong KongHong Kong

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