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An Angle-based Many-Objective evolutionary algorithm with Shift-based density estimation and sum of objectives
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AI
DOI：10.1016/j.eswa.2022.118333.png)
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Abstract
En 中文
Due to the curse of dimensionality, the existing evolutionary algorithms have difficulties in balancing convergence and diversity in many-objective problems. To address this shortcoming, this paper proposes an efficient many-objective optimizer named MaOEA-ASS. In the MaOEA-ASS, the angle-based selection strategy is used to obtain solutions with good diversity from the population. In addition, the combination of the shift-based density estimation and the sum of objectives, which uses the iteration information and emphasis the distribution of solutions, is employed to obtain the high-quality solutions approximating the optimal Pareto solutions. The proposed MaOEA-ASS is compared with eight state-of-the-art many-objective optimization algorithms (MaOEAs) on the DTLZ and WFG test suites, and its performance is verified on a practical many-objective problem. The experimental results demonstrate that the proposed MaOEA-ASS has a superior performance over the peer competitors on all considered many-objective problems.
Keywords:
Many-objective optimization
Angle-based selection
Shift-based density estimation
Evolutionary optimization
Journal
IF:
7.5
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2.9W
Citations:
10.2W