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
收藏
页码：379 / 388
页数：10
相关论文
共 50 条
  • [1] Hybrid Artificial Glowworm Swarm Optimization Algorithm for Solving Constrained Engineering Problem
    Luo, Qifang
    Zhang, Junli
    ADVANCED RESEARCH ON INDUSTRY, INFORMATION SYSTEMS AND MATERIAL ENGINEERING, PTS 1-7, 2011, 204-210 : 823 - 827
  • [2] A hybrid particle swarm optimization for constrained engineering design problems
    Zhou, Zhigang
    Journal of Information and Computational Science, 2010, 7 (14): : 2977 - 2984
  • [3] A hybrid glowworm swarm optimization algorithm to solve constrained multimodal functions optimization
    Zhou, Yongquan
    Zhou, Guo
    Zhang, Junli
    OPTIMIZATION, 2015, 64 (04) : 1057 - 1080
  • [4] A Hybrid Co-evolutionary Particle Swarm Optimization Algorithm for Solving Constrained Engineering Design Problems
    Zhou, Yongquan
    Pei, Shengyu
    JOURNAL OF COMPUTERS, 2010, 5 (06) : 965 - 972
  • [5] Solving constrained optimization problems with a hybrid particle swarm optimization algorithm
    Cecilia Cagnina, Leticia
    Cecilia Esquivel, Susana
    Coello Coello, Carlos A.
    ENGINEERING OPTIMIZATION, 2011, 43 (08) : 843 - 866
  • [6] A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems
    Zhu, Hao
    Hu, Yumei
    Zhu, Weidong
    ADVANCES IN MECHANICAL ENGINEERING, 2019, 11 (03)
  • [7] Good Lattice Swarm Algorithm for Constrained Engineering Design Optimization
    Su, Shoubao
    Wang, Jiwen
    Fan, Wangkang
    Yin, Xibing
    2007 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-15, 2007, : 6421 - +
  • [8] A Hybrid Glowworm Swarm Optimization Algorithm for Solving Matrix Eigenvalues
    Yang, Yan
    Zhou, Yongquan
    INFORMATION-AN INTERNATIONAL INTERDISCIPLINARY JOURNAL, 2011, 14 (03): : 999 - 1004
  • [9] Artificial Glowworm Swarm Optimization Algorithm for Solving Multi-objective Constrained Optimization
    Luo, Qifang
    Gong, Qiaoqiao
    Zhou, Yongquan
    ADVANCES IN MANUFACTURING TECHNOLOGY, PTS 1-4, 2012, 220-223 : 2393 - 2397
  • [10] AN IMPROVED GLOWWORM SWARM OPTIMIZATION ALGORITHM
    Hao, Ya-Yun
    Zhang, Guo-Li
    Xiong, Bo
    PROCEEDINGS OF 2018 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1, 2018, : 155 - 160
12345