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An improved NSGA-III algorithm based on elimination operator for many-objective optimization

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Abstract

Due to the low selection pressure of the Pareto-dominance relation and the ineffectivity of diversity maintenance schemes in the environmental selection, the classical Pareto-dominance based multi-objective evolutionary algorithms (MOEAs) fail to handle many-objective optimization problems. The recently presented non-dominated sorting genetic algorithm III (NSGA-III) employs the uniformly distributed reference points to significantly promote population diversity, but the convergence based on the Pareto-dominance relation could still be enhanced. For this purpose, an improved NSGA-III algorithm based on elimination operator (NSGA-III-EO) is proposed. In the proposed algorithm, the elimination operator first identifies the reference point with maximum niche count and then employs the penalty-based boundary intersection distance to rank the individuals associated with it. To this end, the selection scheme is used to remove the worse individuals rather than to select the superior individuals. The proposed NSGA-III-EO is tested on a number of well-known benchmark problems with up to fifteen objectives and shows the competitive performance compared with five state-of-the-art MOEAs. Additionally, it is also tested on constrained problems having a large number of objectives and shows good performance.

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Notes

  1. The code of NSGA-III-EO is available at https://www.researchgate.net/profile/Chao_Wang207/publications?sorting=recentlyAdded.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No. 61175126) and the International S&T Cooperation Program of China (Grant No. 2015DFG12150).

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Authors and Affiliations

  1. College of Information And Communication Engineering, Harbin Engineering University, Harbin, 150000, People’s Republic of China

    Xiaojun Bi & Chao Wang

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  1. Xiaojun Bi
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Correspondence to Chao Wang.

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Bi, X., Wang, C. An improved NSGA-III algorithm based on elimination operator for many-objective optimization. Memetic Comp. 9, 361–383 (2017). https://doi.org/10.1007/s12293-017-0240-7

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