Parameter tuning for enhancing performance of a variant of particle swarm optimization algorithm
Abstract
There is dependably an extraordinary requirement for new types of algorithms in the population-based improvement algorithm. These algorithms improve the execution of the current algorithm. Parameter change approach assumes an essential job in improving the execution of the PSO algorithm. A new algorithm called particle acceleration-based particle swarm optimization (PA-PSO) has been proposed. In this algorithm a particle acceleration parameter is tuned. This algorithm significantly improves the performance of the PSO–time varying acceleration coefficients (PSO-TVAC) algorithm. This algorithm reduces the time varying weight of inertia and the nonlinear acceleration coefficients in the equation of the PSO-TVAC velocity vector in each iteration. Particle movements in the n-dimensional search space are governed by the kinetics of the second motion equation. Experiments demonstrate that the proposed PA-PSO algorithm outperforms the existing PSO-TVAC algorithm on five well-known reference test functions. The algorithm possesses adequate control over the local as well as global optimums.
Keywords
Global optima; Local optima; Particle swarm optimization; Premature convergence; Stagnation
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PDFDOI: http://doi.org/10.11591/ijeecs.v36.i2.pp1253-1260
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Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).