A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm

被引:1522
作者
Askarzadeh, Alireza [1 ]
机构
[1] Grad Univ Adv Technol, Inst Sci & High Technol & Environm Sci, Dept Energy Management & Optimizat, Kerman, Iran
关键词
Metaheuristic optimization; Crow search algorithm; Constrained engineering optimization; PARTICLE SWARM OPTIMIZATION; DIFFERENTIAL EVOLUTION;
D O I
10.1016/j.compstruc.2016.03.001
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper proposes a novel metaheuristic optimizer, named crow search algorithm (CSA), based on the intelligent behavior of crows. CSA is a population-based technique which works based on this idea that crows store their excess food in hiding places and retrieve it when the food is needed. CSA is applied to optimize six constrained engineering design problems which have different natures of objective functions, constraints and decision variables. The results obtained by CSA are compared with the results of various algorithms. Simulation results reveal that using CSA may lead to finding promising results compared to the other algorithms. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1 / 12
页数:12
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共 30 条
[1]   Artificial bee colony algorithm for large-scale problems and engineering design optimization [J].
Akay, Bahriye ;
Karaboga, Dervis .
JOURNAL OF INTELLIGENT MANUFACTURING, 2012, 23 (04) :1001-1014
[2]   Metaheuristics in combinatorial optimization: Overview and conceptual comparison [J].
Blum, C ;
Roli, A .
ACM COMPUTING SURVEYS, 2003, 35 (03) :268-308
[3]   Corvid cognition [J].
Clayton, N ;
Emery, N .
CURRENT BIOLOGY, 2005, 15 (03) :R80-R81
[4]   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
[5]   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
[6]   Use of a self-adaptive penalty approach for engineering optimization problems [J].
Coello, CAC .
COMPUTERS IN INDUSTRY, 2000, 41 (02) :113-127
[7]   A new heuristic optimization algorithm: Harmony search [J].
Geem, ZW ;
Kim, JH ;
Loganathan, GV .
SIMULATION, 2001, 76 (02) :60-68
[8]   FUTURE PATHS FOR INTEGER PROGRAMMING AND LINKS TO ARTIFICIAL-INTELLIGENCE [J].
GLOVER, F .
COMPUTERS & OPERATIONS RESEARCH, 1986, 13 (05) :533-549
[9]   A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization [J].
He, Qie ;
Wang, Ling .
APPLIED MATHEMATICS AND COMPUTATION, 2007, 186 (02) :1407-1422
[10]   An effective co-evolutionary particle swarm optimization for constrained engineering design problems [J].
He, Qie ;
Wang, Ling .
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2007, 20 (01) :89-99