Abstract
In multi-objective evolutionary optimization, maintaining a good balance between convergence and diversity is particularly crucial to decision makers, especially when tackling problems with complicated Pareto sets. According to the analysis of dominance-based and decomposition-based selection mechanisms in multi-objective evolutionary algorithms, a multi-objective evolutionary algorithm based on the combination of local non-dominated rank and global decomposition is presented. The Gauss distribution model and differential evolution based on history information are employed as evolutionary operators. Various comparative experiments are conducted on 19 unconstraint test MOPs, and our empirical results validate the effectiveness and competitiveness of our proposed algorithm in solving MOPs of different types.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 61074099), Natural Science Foundation of Hebei(No. F2016203249), Innovation Project for Postgraduate of Hebei Province (No. CXZZBS2017049) and the Cultivation Program Project for Leading Talent of innovation team in Colleges and universities of Hebei Province (No. LJRC013). The authors would like to thank the editor and anonymous reviewers for their helpful comments and suggestions to improve the quality of this paper.
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Hu, Z., Yang, J., Cui, H. et al. MOEA3D: a MOEA based on dominance and decomposition with probability distribution model. Soft Comput 23, 1219–1237 (2019). https://doi.org/10.1007/s00500-017-2840-z
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DOI: https://doi.org/10.1007/s00500-017-2840-z