A new hybrid GA−ACO−PSO algorithm for solving various engineering design problems

Tam, Jun Hui and Ong, Zhi Chao and Ismail, Zubaidah and Ang, Bee Chin and Khoo, Shin Yee (2019) A new hybrid GA−ACO−PSO algorithm for solving various engineering design problems. International Journal of Computer Mathematics, 96 (5). pp. 883-919. ISSN 0020-7160

Full text not available from this repository.
Official URL: https://doi.org/10.1080/00207160.2018.1463438

Abstract

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. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Item Type: Article
Uncontrolled Keywords: Ant colony optimization; constrained engineering problem; genetic algorithm; hybrid algorithm; particle swarm optimization
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
T Technology > TP Chemical technology
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 06 Jan 2020 03:58
Last Modified: 06 Jan 2020 03:58
URI: http://eprints.um.edu.my/id/eprint/23328

Actions (login required)

View Item View Item