A Communication Strategy for Paralleling Grey Wolf Optimizer

被引：2

作者：

Pan, Tien-Szu ^{[1
]}

Dao, Thi-Kien ^{[1
]}

Trong-The Nguyen ^{[1
]}

Chu, Shu-Chuan ^{[2
]}

机构：

[1] Natl Kaohsiung Univ Appl Sci, Dept Elect Engn, Kaohsiung, Taiwan

[2] Flinders Univ S Australia, Sch Comp Sci Engn & Math, Adelaide, SA, Australia

来源：

GENETIC AND EVOLUTIONARY COMPUTING, VOL II | 2016年 / 388卷

关键词：

Grey wolf optimizer; Parallel grey wolf optimizer; Numerical optimization;

D O I：

10.1007/978-3-319-23207-2_25

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, a communication strategy for the parallelized Grey Wolf Optimizer is proposed for solving numerical optimization problems. In this proposed method, the population wolves are split into several independent groups based on the original structure of the Grey Wolf Optimizer (GWO), and the proposed communication strategy provides the information flow for the wolves to communicate in different groups. Four benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental results, the proposed communicational strategy increases the speed and accuracy of the GWO on finding the best solution is up to 75% and 45% respectively in comparison with original method.

引用

页码：253 / 262

页数：10

共 15 条

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[2]

Chang JF, 2005, J INF SCI ENG, V21, P809

[3]

Chu S.-C., 2006, INT J INNOV COMPUT I, V3, P8

[4] Ant colony system with communication strategies [J].