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All Journal Jurnal Ilmiah Kursor EMITTER International Journal of Engineering Technology
Shenglin Mu
Hiroshima National College of Maritime Technology
Computer Science & IT Decision Sciences, Operations Research & Management

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A MODIFIED PARTICLE SWARM OPTIMIZATION WITH RANDOM ACTIVATION FOR INCREASING EXPLORATION Alrijadjis Alrijadjis; Shenglin Mu; Kanya Tanaka; Shota Nakashima
Jurnal Ilmiah Kursor Vol 8 No 1 (2015)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i1.72

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.
PID Controller Design of Nonlinear System using a New Modified Particle Swarm Optimization with Time-Varying Constriction Coefficient Alrijadjis .; Shenglin Mu; Shota Nakashima; Kanya Tanaka
EMITTER International Journal of Engineering Technology Vol 2 No 2 (2014)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (133.449 KB) | DOI: 10.24003/emitter.v2i2.28

Abstract

The proportional integral derivative (PID) controllers have been widely used in most process control systems for a long time. However, it is a very important problem how to choose PID parameters, because these parameters give a great influence on the control performance. Especially, it is difficult to tune these parameters for nonlinear systems. In this paper, a new modified particle swarm optimization (PSO) is presented to search for optimal PID parameters for such system. The proposed algorithm is to modify constriction coefficient which is nonlinearly decreased time-varying for improving the final accuracy and the convergence speed of PSO. To validate the control performance of the proposed method, a typical nonlinear system control, a continuous stirred tank reactor (CSTR) process, is illustrated. The results testify that a new modified PSO algorithm can perform well in the nonlinear PID control system design in term of lesser overshoot, rise-time, settling-time, IAE and ISE.Keywords: PID controller, Particle Swarm Optimization (PSO),constriction factor, nonlinear system.
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 Alrijadjis . Alrijadjis . Alrijadjis Alrijadjis Kanya Tanaka Shota Nakashima