Universiti Teknologi Malaysia Institutional Repository

Non-parametric particle swarm optimization for global optimization

Beheshti, Zahra and Shamsuddin, Siti Mariyam (2015) Non-parametric particle swarm optimization for global optimization. Applied Soft Computing Journal, 28 . pp. 345-359. ISSN 1568-4946

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Official URL: http://dx.doi.org/10.1016/j.asoc.2014.12.015

Abstract

In recent years, particle swarm optimization (PSO) has extensively applied in various optimization problems because of its simple structure. Although the PSO may find local optima or exhibit slow convergence speed when solving complex multimodal problems. Also, the algorithm requires setting several parameters, and tuning the parameters is a challenging for some optimization problems. To address these issues, an improved PSO scheme is proposed in this study. The algorithm, called non-parametric particle swarm optimization (NP-PSO) enhances the global exploration and the local exploitation in PSO without tuning any algorithmic parameter. NP-PSO combines local and global topologies with two quadratic interpolation operations to increase the search ability. Nineteen (19) unimodal and multimodal nonlinear benchmark functions are selected to compare the performance of NP-PSO with several well-known PSO algorithms. The experimental results showed that the proposed method considerably enhances the efficiency of PSO algorithm in terms of solution accuracy, convergence speed, global optimality, and algorithm reliability.

Item Type:Article
Uncontrolled Keywords:global and local optimum, non-parametric particle swarm optimization, optimization problems, particle swarm optimization
Subjects:A General Works
ID Code:58652
Deposited By: Haliza Zainal
Deposited On:04 Dec 2016 12:07
Last Modified:08 Mar 2017 13:05

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