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Alrijadjis .
Politeknik Elektronika Negeri Surabaya
Computer Science & IT

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Modified Particle Swarm Optimization using Nonlinear Decreased Inertia Weight Alrijadjis .; Shenglin Mu; Shota Nakashima; Kanya Tanaka
EMITTER International Journal of Engineering Technology Vol 3 No 2 (2015)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (179.162 KB) | DOI: 10.24003/emitter.v3i2.42

Abstract

Particle Swarm Optimization (PSO) has demonstrated great performance in various optimization problems. However, PSO has weaknesses, namely premature convergence and easy to get stuck or fall into local optima for complex multimodal problems. One of the causes of these weaknesses is unbalance between exploration and exploitation ability in PSO. This paper proposes a Modified Particle Swarm Optimization (MPSO) using nonlinearly decreased inertia weight called MPSO-NDW to improve the balance. The key idea of the proposed method is to control the period and decreasing rate of exploration-exploitation ability. The investigation with three famous benchmark functions shows that the accuracy, success rate, and convergence speed of the proposed MPSO-NDW is better than the common used PSO with linearly decreased inertia weight or called PSO-LDWKeywords: particle swarm optimization (PSO), premature convergence, local optima, exploration ability, exploitation ability.
Co-Authors Kanya Tanaka Shenglin Mu Shota Nakashima