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

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.

KEYWORDS:

• Ant colony optimization
• constrained engineering problem
• genetic algorithm
• hybrid algorithm
• particle swarm optimization

2010 AMS SUBJECT CLASSIFICATIONS:

• 65Kxx mathematical programming
• optimization and variational techniques
• 80M50 optimization
• 90C26 non-convex programming
• global optimization
• 90C27 combinatorial optimization

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Zhi Chao Ong http://orcid.org/0000-0002-1686-3551

Shin Yee Khoo http://orcid.org/0000-0002-4729-8508

Additional information

Funding

The authors wish to acknowledge the financial support and advice given by 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] and other project collaborators.