Hybrid Particle Swarm Optimization with Bat Algorithm

被引：25

作者：

Pan, Tien-Szu ^{[1
]}

Dao, Thi-Kien ^{[1
]}

Trong-The Nguyen ^{[2
]}

Chu, Shu-Chuan ^{[3
]}

机构：

[1] Natl Kaohsiung Univ Appl Sci, Dept Elect Engn, Kaohsiung 807, Taiwan

[2] Haiphong Private Univ, Fac Informat Technol, Le Chan, Hai Phong, Vietnam

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

来源：

GENETIC AND EVOLUTIONARY COMPUTING | 2015年 / 329卷

关键词：

Hybrid Particle Swarm Optimization with Bat Algorithm; Particle Swarm Optimization Algorithm; Bat Algorithm Optimizations; Swarm Intelligence;

D O I：

10.1007/978-3-319-12286-1_5

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 hybrid Particle Swarm Optimization (PSO) with Bat Algorithm (BA) is proposed for solving numerical optimization problems. In this work, several worst individuals of particles in PSO will be replaced with the best individuals in BA after running some fixed iterations, and on the contrary, the poorer individuals of BA will be replaced with the finest particles of PSO. The communicating strategy provides the information flow for the particles in PSO to communicate with the bats in BA. Six benchmark functions are used to test the behavior of the convergence, the accuracy, and the speed of the approached method. The results show that the proposed scheme increases the convergence and accuracy more than BA and PSO up to 3% and 47% respectively.

引用

页码：37 / 47

页数：11

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