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A universal strengthened searching module for multi-objective optimization based on variable properties
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DOI：10.1016/j.asoc.2020.106199.png)
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Numerous algorithms have been introduced in solving multi-/many-objective optimization problems. However, some rigorous elite definitions may result in the side-effect that new solutions are hard to be admitted by the elite archive, which will restrain the searching ability. In this paper, a universal strengthened searching (SS) module is proposed as an accessorial searching procedure to improve the performance of existing algorithms. By concentrating part of the computational resources, this strategy can enhance the convergence and diversity searching strengths in separate areas. These areas are determined by classifying decision variables according to a dynamic spread-based procedure. Moreover, incorporating SS with existing algorithms can improve the overall performance of original algorithms while persisting their inherent advantages. In this paper, the structure of an SS-embedded algorithm is illustrated and the comparison experiments have been established among several couples of algorithms. The results demonstrate that the algorithms with the SS module have better overall performance compared to the original algorithms towards different problem properties. Meanwhile, the proposed strategy can accelerate the searching procedure for the time-consuming algorithms. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Multi-objective
Variable clustering
Strengthened searching
Evolutionary algorithm
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期刊
IF:
6.6
论文数:
1.4W
被引数:
4.8W
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