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A MODIFIED PARTICLE SWARM OPTIMIZATION WITH RANDOM ACTIVATION FOR INCREASING EXPLORATION

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Alrijadjis Alrijadjis

Abstract

Particle Swarm Optimization (PSO) is a popular optimization technique which is
inspired by the social behavior of birds flocking or fishes schooling for finding food.
It is a new metaheuristic search algorithm developed by Eberhart and Kennedy in
1995. However, the standard PSO has a shortcoming, i.e., premature convergence
and easy to get stack or fall into local optimum. Inertia weight is an important
parameter in PSO, which significantly affect the performance of PSO. There are
many variations of inertia weight strategies have been proposed in order to overcome
the shortcoming. In this paper, a new modified PSO with random activation to
increase exploration ability, help trapped particles for jumping-out from local
optimum and avoid premature convergence is proposed. In the proposed method, an
inertia weight is decreased linearly until half of iteration, and then a random number
for an inertia weight is applied until the end of iteration. To emphasis the role of this
new inertia weight adjustment, the modified PSO paradigm is named Modified PSO
with random activation (MPSO-RA). The experiments with three famous benchmark
functions show that the accuracy and success rate of the proposed MPSO-RA increase
of 43.23% and 32.95% compared with the standard PSO.

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How to Cite
ALRIJADJIS, Alrijadjis. A MODIFIED PARTICLE SWARM OPTIMIZATION WITH RANDOM ACTIVATION FOR INCREASING EXPLORATION. Jurnal Ilmiah Kursor, [S.l.], v. 8, n. 1, p. 33-40, dec. 2016. ISSN 2301-6914. Available at: <http://kursorjournal.org/index.php/kursor/article/view/72>. Date accessed: 19 aug. 2019. doi: https://doi.org/10.28961/kursor.v8i1.72.
Keywords
Particle Swarm Optimization; inertia weight; premature convergence; local optimum; random activation
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