Abstract
Many-objective optimization problems (MaOPs) refer to multi-objective optimization problems (MOPs) containing large number of objectives, typically more than four. The complexity of MOPs grows rapidly in size with the number of objectives, making the problem quickly intractable. Though various many-objective evolutionary algorithms (MaOEAs) have been proposed to solve MaOPs, it is difficult to balance the convergence and population diversity. In order to address the above issues, this paper proposes a many-objective evolutionary algorithm based on dominance and objective space decomposition (called DDMaOEA), in which fast non-dominated sorting cooperates with objective space decomposition to maintain the population diversity and convergence during the environment selection. To verify the performance of DDMaOEA, 13 benchmark problems with 3, 5, 8, and 10 objectives are used. Experimental results show that DDMaOEA achieves better performance when compared with five other Many-objective optimization algorithms.
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Acknowledgment
This work was supported by National Natural Science Foundation of China (No. 62166027), and Jiangxi Provincial Natural Science Foundation (Nos. 20212ACB21 2004, 20212BAB202023, and 20212BAB202022).
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Wei, Z. et al. (2022). Many-Objective Evolutionary Algorithm Based on Dominance and Objective Space Decomposition. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1638. Springer, Singapore. https://doi.org/10.1007/978-981-19-6135-9_16
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DOI: https://doi.org/10.1007/978-981-19-6135-9_16
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