Modified Particle Swarm Optimization using Nonlinear Decreased Inertia Weight

  • Alrijadjis . Politeknik Elektronika Negeri Surabaya
  • Shenglin Mu Hiroshima National College of Maritime Technology
  • Shota Nakashima Yamaguchi University
  • Kanya Tanaka Yamaguchi University


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-LDW

Keywords: particle swarm optimization (PSO), premature convergence, local optima, exploration ability, exploitation ability.


Download data is not yet available.


Kennedy, R.C. Eberhart, Particle Swarm Optimization, Proceeding IEEE International Conference on Neural Networks, pp. 1942-1945, 1995

Y. Shi, R.C. Eberhart, A modified particle swarm optimizer, Proceeding of IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, pp. 69-73, 1998

J. Kennedy, R.C. Eberhart, Y. Shi, Swarm Intelligence, San Fransisco: Morgan Kaufman Pubhisher, 2001

A. Chander, A. Chatterjee, P. Siarry, A new social and momentum component adaptive PSO algorithm for image segmentation, Expert System with application, No. 38, pp. 4998-5004, 2011

C.L. Chen, R.M. Jan, T.Y. Lee, C.H. Chen, A novel particle swarm optimization algorithm solution of economic dispatch with valve point loading, Journal of Marine Science and Technology, Vol. 19, No. 1, pp. 43-51, 2011

H. Zhu, C. Zheng, X. Hu, X. Li, Adaptive PSO using random inertia weight and its application in UAV path planning, Proceeding of SPIE, Vol. 7128, pp. 1-5, 2008

K. Tanaka, Y. Murata, Y. Nishimura, Faridah A. Rahman, M. Oka, A. Uchibori. Variable gain type-PID control using PSO for ultrasonic motor. Journal of the Japan Society of Applied Electromagnetics and Mechanics, Vol. 18, No. 3, 118-123, 2011

I.C. Trelea, The particle swarm optimization algorithm: convergence analysis and parameters setting, Information Processing Letters, No. 85, pp. 317-325, 2003

Y. Shi, R. Eberhart, Empirical study of particle swarm optimization, Proceeding of the IEEE Congress on Evolutionary Computation, pp. 1945- 1950, 1999

How to Cite
., A., Mu, S., Nakashima, S., & Tanaka, K. (2016). Modified Particle Swarm Optimization using Nonlinear Decreased Inertia Weight. EMITTER International Journal of Engineering Technology, 3(2), 18-27.