改进的布谷鸟搜索,更好的搜索能力,解决CEC2017基准问题

Rohit Salgotra, Urvinder Singh, S. Saha
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引用次数: 23

摘要

布谷鸟搜索是一种自然启发的进化算法,用于解决现实世界的优化问题。它的灵感来自杜鹃的幼虫寄生。它具有很强的竞争力,并已被用于解决科学和工程领域的许多问题。过去曾提出过许多改进方案以提高其性能。本文提出了CS的改进版本CVnew,其中提出了三个修改。第一个改进是引入两个新的搜索方程来改进全局搜索,第二个改进是引入四个搜索方程来改进局部搜索。作为第三个改进,通过指数降低切换概率来增加全局和局部搜索之间的平衡。该算法已用于求解CEC 2017的单目标实参数问题。数值结果表明,CVnew与SaDE、JADE、SHADE和MVMO相比,具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improved Cuckoo Search with Better Search Capabilities for Solving CEC2017 Benchmark Problems
Cuckoo Search is a nature inspired evolutionary algorithm to solve real-world optimization problems. It is inspired from the brood parasitism of cuckoos. It is highly competitive and has been used to solve number of problems in the field of science and engineering. A number of modifications have been proposed to enhance its performance in the past. This paper presents an improved version of CS namely CVnew in which three modifications are proposed. The first modification is the introduction of two new search equations to improve the global search while the second one deals with the incorporation of four search equations to improve the local search. As a third modification, a balance between global and local search has been increased by exponentially decreasing the switch probability. The proposed algorithm has been applied to solve single objective real-parameter problems of CEC 2017. The numerical results prove the better performance of CVnew in comparison with SaDE, JADE, SHADE and MVMO.
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