A novel global Harmony Search method based on Ant Colony Optimisation algorithm. Issue 1 (3rd March 2016)

• Record Type: Journal Article
• Authors: Fouad, Allouani; Boukhetala, Djamel; Boudjema, Fares; Zenger, Kai; Gao, Xiao-Zhi
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 28:Issue 1/2(2016)
• Page Start: 215
• Page End: 238
• Publication Date: 2016-03-03
• Subjects: Harmony Search -- Ant Colony Optimisation -- hybrid optimisation methods -- benchmark function -- engineering optimisation problems; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2015.1020570
• Languages: English
• ISSNs: 0952-813X

Abstract

Abstract : The Global-best Harmony Search (GHS) is a stochastic optimisation algorithm recently developed, which hybridises the Harmony Search (HS) method with the concept of swarm intelligence in the particle swarm optimisation (PSO) to enhance its performance. In this article, a new optimisation algorithm called GHSACO is developed by incorporating the GHS with the Ant Colony Optimisation algorithm (ACO). Our method introduces a novel improvisation process, which is different from that of the GHS in the following aspects. (i) A modified harmony memory (HM) representation and conception. (ii) The use of a global random switching mechanism to monitor the choice between the ACO and GHS. (iii) An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The proposed GHSACO algorithm has been applied to various benchmark functions and constrained optimisation problems. Simulation results demonstrate that it can find significantly better solutions when compared with the original HS and some of its variants.