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

共 50 条

[41] Unified Particle Swarm Optimization for solving constrained engineering optimization problems
Parsopoulos, KE
Vrahatis, MN
ADVANCES IN NATURAL COMPUTATION, PT 3, PROCEEDINGS, 2005, 3612 : 582 - 591
[42] A Novel Chaos Glowworm Swarm Optimization Algorithm for Optimization Functions
Huang, Kai
Zhou, Yong Quan
BIO-INSPIRED COMPUTING AND APPLICATIONS, 2012, 6840 : 426 - 434
[43] A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems
Yong Wang
Zixing Cai
Frontiers of Computer Science in China, 2009, 3 : 38 - 52
[44] A hybrid multi-swarm particle swarm optimization to solve constrained optimization problems
Wang, Yong
Cai, Zixing
FRONTIERS OF COMPUTER SCIENCE IN CHINA, 2009, 3 (01): : 38 - 52
[45] An adaptive switchover hybrid particle swarm optimization algorithm with local search strategy for constrained optimization problems
Liu, Zhao
Qin, Zhiwei
Zhu, Ping
Li, Han
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 95
[46] Hybrid Immune Clonal Particle Swarm Optimization Multi-Objective Algorithm for Constrained Optimization Problems
Pei, Shengyu
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2017, 31 (01)
[47] Glowworm swarm optimization algorithm with improved movement pattern
He, Lifang
Tong, Xiong
Huang, Songwei
Wang, Qingping
2013 6TH INTERNATIONAL CONFERENCE ON INTELLIGENT NETWORKS AND INTELLIGENT SYSTEMS (ICINIS), 2013, : 43 - 46
[48] A glowworm swarm optimization algorithm based on metropolis criterion
Zhao, Guangwei
Zhou, Yongquan
Luo, Qifang
Wang, Yingju
International Journal of Advancements in Computing Technology, 2012, 4 (03) : 149 - 155
[49] Hybrid charged system search and particle swarm optimization for engineering design problems
Kaveh, A.
Talatahari, S.
ENGINEERING COMPUTATIONS, 2011, 28 (3-4) : 423 - 440
[50] Hybrid Particle Swarm Optimization with Sine Cosine Algorithm and Nelder–Mead Simplex for Solving Engineering Design Problems
Hussam N. Fakhouri
Amjad Hudaib
Azzam Sleit
Arabian Journal for Science and Engineering, 2020, 45 : 3091 - 3109

← 1 2 3 4 5 →