基于分解的多目标布谷鸟搜索算法

Liang Chen, Wenyan Gan, Hongwei Li, Xin Xu, Lin Cao, Yufang Feng
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引用次数: 4

摘要

布谷鸟搜索(CS)算法的简单和成功启发了研究者将这些技术应用于多目标优化领域。本文研究了基于分解方法的CS在求解多目标优化问题中的应用。提出了一种新的基于分解的多目标CS算法MOCS/D。该算法将CS独特的lsamvy飞行技术和改进的多项式变异技术融合到基于分解的多目标进化算法(MOEA/D)中。在测试实例上与MOEA/D-SBX和MOEA/D-DE进行了比较。实验结果表明,在大多数选定的测试实例上,该算法优于比较算法。结果表明,该方法是求解多目标优化问题的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multi-objective Cuckoo search Algorithm Based on Decomposition
The simplicity and success of cuckoo search (CS) algorithm has inspired researchers to apply these techniques to the multi-objective optimization field. The paper studies the application of CS for solving multi-objective optimization problems (MOPs) based on decomposition methods. A new decomposition-based multi-objective CS algorithm is proposed, called MOCS/D. The proposed algorithm integrates the unique Lévy flights technique of CS and improved polynomial mutation into multi-objective evolutionary algorithm based on Decomposition (MOEA/D). Our proposed approach is compared with MOEA/D-SBX and MOEA/D-DE on the test instances. The experimental results show that it outperforms the compared algorithms on most of the selected test instances. It demonstrates that the proposed approach is a competitive candidate for multi-objective optimization problems.
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