A novel global Harmony Search method based on Ant Colony Optimisation algorithm

Allouani Fouad*, Djamel Boukhetala, Fares Boudjema, Kai Zenger, Xiao-zhi Gao

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)215-238
Number of pages24
JournalJOURNAL OF EXPERIMENTAL AND THEORETICAL ARTIFICIAL INTELLIGENCE
Volume28
Issue number1-2
DOIs
Publication statusPublished - 3 Mar 2016
MoE publication typeA1 Journal article-refereed

Keywords

  • Ant Colony Optimisation
  • benchmark function
  • engineering optimisation problems
  • Harmony Search
  • hybrid optimisation methods

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