期刊
INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS
卷 96, 期 5, 页码 883-919出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207160.2018.1463438
关键词
Ant colony optimization; constrained engineering problem; genetic algorithm; hybrid algorithm; particle swarm optimization
资金
- University of Malaya Research Grant [RP022D-13AET]
- Fundamental Research Grant Scheme [FP010-2014A]
- Advanced Shock and Vibration Research (ASVR) Group of University of Malaya, Postgraduate Research Fund [PG009-2015A]
The intention of this hybridization is to further enhance the exploratory and exploitative search capabilities involving simple concepts. The proposed algorithm adopts the combined discrete and continuous probability distribution scheme of ant colony optimization (ACO) to specifically assist genetic algorithm in the aspect of exploratory search. Besides, distinctive crossover and mutation operators are introduced, in which, two types of mutation operators, namely, standard mutation and refined mutation are suggested. In early iterations, standard mutation is utilized collaboratively with the concept of unrepeated tours of ACO to evade local entrapment, while refined mutation is used in later iterations to supplement the exploitative search, which is mainly controlled by particle swarm optimization. The proposed method has been validated in solving test functions and well-known engineering design problems. It exhibits a great global search capability even in the presence of non-linearity, multimodality and constraints, involving a large number of dimensions as well as large search areas.
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