Chaotic grasshopper optimization algorithm for global optimization

被引：256

作者：

Arora, Sankalap ^{[1
]}

Anand, Priyanka ^{[2
]}

机构：

[1] DAV Univ, Jalandhar, Punjab, India

[2] Lovely Profess Univ, Jalandhar, Punjab, India

来源：

NEURAL COMPUTING & APPLICATIONS | 2019年 / 31卷 / 08期

关键词：

Grasshopper optimization algorithm; Chaotic maps; Global optimization problem; Multimodal function; DIFFERENTIAL EVOLUTION; DESIGN; MAPS;

D O I：

10.1007/s00521-018-3343-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Grasshopper optimization algorithm (GOA) is a new meta-heuristic algorithm inspired by the swarming behavior of grasshoppers. The present study introduces chaos theory into the optimization process of GOA so as to accelerate its global convergence speed. The chaotic maps are employed to balance the exploration and exploitation efficiently and the reduction in repulsion/attraction forces between grasshoppers in the optimization process. The proposed chaotic GOA algorithms are benchmarked on thirteen test functions. The results show that the chaotic maps (especially circle map) are able to significantly boost the performance of GOA.

引用

页码：4385 / 4405

页数：21

共 48 条

[1]

[Anonymous], 1988, SIMULATED ANNEALING

[2]

[Anonymous], 1970, Sel. Tables Math. Stat

[3]

Arora S., 2013, Int. J. Comput. Appl, V69, DOI DOI 10.5120/11826-7528

[4] Node Localization in Wireless Sensor Networks Using Butterfly Optimization Algorithm [J].