Charged system search and particle swarm optimization hybridized for optimal design of engineering structures

被引:0
作者
Kaveh, A. [1 ]
Nasrollahi, A. [1 ]
机构
[1] Iran Univ Sci & Technol, Ctr Excellence Fundamental Studies Struct Engn, Tehran, Iran
基金
美国国家科学基金会;
关键词
Hybrid metaheuristic algorithm; Charged system search; Particle swarm optimization; Optimal design; Engineering structures;
D O I
暂无
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
In this paper, a new Hybrid Charged System Search and Particle Swarm Optimization, HCSSPSO, is presented. Although Particle Swarm Optimization (PSO) has many advantages, including directional search, it has also some disadvantages resulting in slow convergence rate and low performance. On the other hand, the Charged System Search (CSS) is a robust optimization algorithm which has been successfully utilized in. many structural optimization problems. In this study, the goal is to incorporate the positive features of the PSO in CSS and make it more capable of solving optimization problems. The hybrid CSS and PSO is named HCSSPRO, and it uses the positive features of the PSO to further improve the CSS. In order to show the higher performance of the HCSSPSO, it is implemented and applied to some engineering problems. These structures are benchmark examples which are optimized by many other methods and are suitable for comparison. Results of the present algorithm show its better performance and higher convergence rate for the problem studied. (C) 2014 Sharif University of Technology. All rights reserved.
引用
收藏
页码:295 / 305
页数:11
相关论文
共 32 条
[1]  
[Anonymous], ASIAN J CIVIL ENG
[2]  
[Anonymous], 1994, Journal of mechanical design, DOI DOI 10.1115/1.2919393
[3]  
Arora J., 2004, INTRO OPTIMUM DESIGN
[4]  
BELEGUNDU AD, 1982, THESIS U IOWA IOWA
[5]   Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems [J].
Coelho, Leandro dos Santos .
EXPERT SYSTEMS WITH APPLICATIONS, 2010, 37 (02) :1676-1683
[6]   Constraint-handling in genetic algorithms through the use of dominance-based tournament selection [J].
Coello, CAC ;
Montes, EM .
ADVANCED ENGINEERING INFORMATICS, 2002, 16 (03) :193-203
[7]   Use of a self-adaptive penalty approach for engineering optimization problems [J].
Coello, CAC .
COMPUTERS IN INDUSTRY, 2000, 41 (02) :113-127
[8]  
Deb K., 1997, Evolutionary Algorithms in Engineering Applications, P497, DOI [10.1007/978-3-662-03423-127, DOI 10.1007/978-3-662-03423-127, https://doi.org/10.1007/978-3-662-03423-1_27]
[9]  
Eberhart R., 1995, MHS 95 P 6 INT S MIC
[10]   Optimal design of planar and space structures with genetic algorithms [J].
Erbatur, F ;
Hasançebi, O ;
Tütüncü, I ;
Kiliç, H .
COMPUTERS & STRUCTURES, 2000, 75 (02) :209-224