Abstract
To design an effective multi-objective optimization evolutionary algorithms (MOEA), we need to address the following issues: 1) the sensitivity to the shape of true Pareto front (PF) on decomposition-based MOEAs; 2) the loss of diversity due to paying so much attention to the convergence on domination-based MOEAs; 3) the curse of dimensionality for many-objective optimization problems on grid-based MOEAs. This paper proposes an MOEA based on space partitioning (MOEA-SP) to address the above issues. In MOEA-SP, subspaces, partitioned by a k-dimensional tree (kd-tree), are sorted according to a bi-indicator criterion defined in this paper. Subspace-oriented and Max-Min selection methods are introduced to increase selection pressure and maintain diversity, respectively. Experimental studies show that MOEA-SP outperforms several compared algorithms on a set of benchmarks.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant No. 61673355, in part by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) under Grant (CUG170603, CUGGC02).
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Wu, X., Li, C., Zeng, S., Yang, S. (2020). A Novel Multi-objective Evolutionary Algorithm Based on Space Partitioning. In: Li, K., Li, W., Wang, H., Liu, Y. (eds) Artificial Intelligence Algorithms and Applications. ISICA 2019. Communications in Computer and Information Science, vol 1205. Springer, Singapore. https://doi.org/10.1007/978-981-15-5577-0_10
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DOI: https://doi.org/10.1007/978-981-15-5577-0_10
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