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

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...

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Vydáno v:International journal of computer mathematics Ročník 96; číslo 5; s. 883 - 919
Hlavní autoři: Tam, Jun Hui, Ong, Zhi Chao, Ismail, Zubaidah, Ang, Bee Chin, Khoo, Shin Yee
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 04.05.2019
Taylor & Francis Ltd
Témata:
65Kxx mathematical programming
80M50 optimization
90C26 non-convex programming
90C27 combinatorial optimization
Algorithms
Ant colony optimization
constrained engineering problem
Crossovers
Design engineering
Entrapment
genetic algorithm
Genetic algorithms
global optimization
hybrid algorithm
Linearity
Mutation
Operators (mathematics)
optimization and variational techniques
Particle swarm optimization
Searching
ISSN:0020-7160, 1029-0265
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Shrnutí: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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content type line 14
ISSN:0020-7160
1029-0265
DOI:10.1080/00207160.2018.1463438