Modified particle swarm optimization with novel modulated inertia for velocity update

Abdul Hadi Hamdan, Fazida Hanim Hashim, Abdullah Zawawi Mohamed, Wan Mimi Diyana Wan Zaki, Aini Hussain

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Particle swarm optimization (PSO) is a population-based stochastic search algorithm for searching the optimal regions from multidimensional space, inspired by the social behaviour of some animal species. However, it has its limitations such as being trapped into a local optima and having a slow rate of convergence. In this paper, a new method of creating a combination of a developed Accelerated PSO and a new modulated inertia coefficient for the velocity update has been proposed. Random term based on particle neighbourhood has been added in the position update formula, inspired by the Artificial Bee Colony (ABC) algorithm. To verify the proposed modified PSO, experiments were conducted on several benchmark optimization problems. The results show that the proposed algorithm is superior in comparison with standard PSO and accelerated PSO algorithms.

Original languageEnglish
Pages (from-to)1855-1860
Number of pages6
JournalInternational Journal of Engineering and Technology
Volume8
Issue number4
DOIs
Publication statusPublished - 1 Sep 2016

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Particle swarm optimization (PSO)
Animals
Experiments

Keywords

  • Global best
  • Modulated inertia
  • Particle swarm optimization
  • Velocity update

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Modified particle swarm optimization with novel modulated inertia for velocity update. / Hamdan, Abdul Hadi; Hashim, Fazida Hanim; Mohamed, Abdullah Zawawi; Wan Zaki, Wan Mimi Diyana; Hussain, Aini.

In: International Journal of Engineering and Technology, Vol. 8, No. 4, 01.09.2016, p. 1855-1860.

Research output: Contribution to journalArticle

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