A Hybrid Glowworm Swarm Optimization Algorithm for Constrained Engineering Design Problems

被引：20

作者：

Zhou, Yongquan ^{[1
,2
]}

Zhou, Guo ^{[3
]}

Zhang, Junli ^{[1
]}

机构：

[1] Guangxi Univ Nationalities, Coll Math & Comp Sci, Nanning 530006, Guangxi, Peoples R China

[2] Guangxi Key Lab Hybrid Computat & IC Design Anal, Nanning 530006, Guangxi, Peoples R China

[3] Chinese Acad Sci, Inst Comp Technol, Beijing 100081, Peoples R China

来源：

APPLIED MATHEMATICS & INFORMATION SCIENCES | 2013年 / 7卷 / 01期

基金：

美国国家科学基金会;

关键词：

Glowworm swarm optimization; differential evolution; feasibility rules; simulated annealing; hybrid optimization algorithm; engineering design problems; SIMULATION;

D O I：

10.12785/amis/070147

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, a novel hybrid glowworm swarm optimization (HGSO) algorithm is proposed. Firstly, the presented algorithm embeds predatory behavior of artificial fish swarm algorithm (AFSA) into glowworm swarm optimization (GSO) algorithm and combines the improved GSO with differential evolution (DE) on the basis of a two-population co-evolution mechanism. Secondly, under the guidance of the feasibility rules, the swarm converges towards the feasible region quickly. In addition, to overcome premature convergence, the local search strategy based on simulated annealing (SA) is used and makes the search near the true optimum solution gradually. Finally, the HGSO algorithm is for solving constrained engineering design problems. The results show that HGSO algorithm has faster convergence speed, higher computational precision, and is more effective for solving constrained engineering design problems.

引用

页码：379 / 388

页数：10

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