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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.
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Authors and Affiliations
School of Electronic Engineering, Shanghai Dianji University, PuDong, Shanghai, 201306, China
Xiaogang Fu
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049, China
Jianyong Sun
Department of Computer Science, City University of Hong Kong, Hong Kong, Hong Kong
Qingfu Zhang
- Xiaogang Fu
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- Qingfu Zhang
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Correspondence toJianyong Sun.
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Editors and Affiliations
Xiamen University, Xiamen Shi, Fujian, China
Fei Chao
Cardiff University, Cardiff, United Kingdom
Steven Schockaert
Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
Qingfu Zhang
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Fu, X., Sun, J., Zhang, Q. (2018). A Reference-Inspired Evolutionary Algorithm with Subregion Decomposition for Many-Objective Optimization. In: Chao, F., Schockaert, S., Zhang, Q. (eds) Advances in Computational Intelligence Systems. UKCI 2017. Advances in Intelligent Systems and Computing, vol 650. Springer, Cham. https://doi.org/10.1007/978-3-319-66939-7_12
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