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

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

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

8 Sitaatiot (Scopus)

Abstrakti

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.

AlkuperäiskieliEnglanti
Sivut215-238
Sivumäärä24
JulkaisuJOURNAL OF EXPERIMENTAL AND THEORETICAL ARTIFICIAL INTELLIGENCE
Vuosikerta28
Numero1-2
DOI - pysyväislinkit
TilaJulkaistu - 3 maaliskuuta 2016
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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