Adaptive Cuckoo Search Based on Ranking of Search Point

被引：0

作者：

Miyake, Yuki ^{[1
]}

Tamura, Kenichi ^{[1
]}

Tsuchiya, Junichi ^{[1
]}

Yasuda, Keiichiro ^{[1
]}

机构：

[1] Tokyo Metropolitan Univ, Dept Elect & Elect Engn, 1-1 Minamiosawa, Hachioji, Tokyo 1920397, Japan

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2017年

关键词：

Metaheuristics; Cuckoo Search; Intensification; Diversification; Parameter Adjustment; PARTICLE SWARM OPTIMIZATION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recently, the development of high-performance metaheuristics has become an important subject. In this study, an adaptive Cuckoo Search based on ranking of search point is proposed. This study aims to improve the performance of Cuckoo Search by adjusting the parameter beta to allow search points with good evaluation value to search nearby and those with poor evaluation value to search far away. Finally, the performance of the proposed method is evaluated by numerical experiments.

引用

页码：1788 / 1792

页数：5

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