A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants

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Sudip Mandal 1,*

1. Department of Electronics and Communication Engineering, Global Institute of Management and Technology, Krishna Nagar, India-741102

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2017.08.07

Received: 14 Mar. 2016 / Revised: 2 Jul. 2016 / Accepted: 25 Jul. 2017 / Published: 8 Aug. 2017

Index Terms

Metaheuristic, Optimization, Modified Particle Swarm Optimization (MPSO), Inertia Weight, Acceleration Constant


Particle Swarm Optimization (PSO) is one of most widely used metaheuristics which is based on collective movement of swarm like birds or fishes. The inertia weight (w) of PSO is normally used for maintaining balance between exploration and exploitation capability. Many strategies for updating the inertia weight during iteration were already proposed by several researchers. In this paper, a Modified Particle Swarm Optimization (MPSO) algorithm based on self-adaptive acceleration constants along with Linear Decreasing Inertia Weight (LDIW) technique is proposed. Here, in spite of using fixed values of acceleration constants, the values are updated themselves during iteration depending on local and global best fitness value respectively. Six different benchmark functions and three others inertia weight strategies were used for validation and comparison with this proposed model. It was observed that proposed MPSO algorithm performed better than others three strategies for most of functions in term of accuracy and convergence although its execution time was larger than others techniques.

Cite This Paper

Sudip Mandal, "A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.8, pp.49-56, 2017. DOI:10.5815/ijmecs.2017.08.07


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