Sudip Mandal

A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants

Full Text (PDF, 943KB), PP.49-56

Views: 0 Downloads: 0

Author(s)

1. Department of Electronics and Communication Engineering, Global Institute of Management and Technology, Krishna Nagar, India-741102

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2017.08.07

Received: 14 Mar. 2016 / Revised: 2 Jul. 2016 / Accepted: 25 Jul. 2017 / Published: 8 Aug. 2017

Index Terms

Metaheuristic, Optimization, Modified Particle Swarm Optimization (MPSO), Inertia Weight, Acceleration Constant

Abstract

Particle Swarm Optimization (PSO) is one of most widely used metaheuristics which is based on collective movement of swarm like birds or fishes. The inertia weight (w) of PSO is normally used for maintaining balance between exploration and exploitation capability. Many strategies for updating the inertia weight during iteration were already proposed by several researchers. In this paper, a Modified Particle Swarm Optimization (MPSO) algorithm based on self-adaptive acceleration constants along with Linear Decreasing Inertia Weight (LDIW) technique is proposed. Here, in spite of using fixed values of acceleration constants, the values are updated themselves during iteration depending on local and global best fitness value respectively. Six different benchmark functions and three others inertia weight strategies were used for validation and comparison with this proposed model. It was observed that proposed MPSO algorithm performed better than others three strategies for most of functions in term of accuracy and convergence although its execution time was larger than others techniques.

Cite This Paper

Sudip Mandal, "A Modified Particle Swarm Optimization Algorithm based on Self-Adaptive Acceleration Constants", International Journal of Modern Education and Computer Science(IJMECS), Vol.9, No.8, pp.49-56, 2017. DOI:10.5815/ijmecs.2017.08.07

Reference

[1]J. Kennedy and R.C. Eberhart, “Particle swarm optimization,” in IEEE International Conference on Neuran Networks, pp. 1942–1948, 1995.
[2]A. Nickabadi, M. M. Ebadzadeh, and R. Safabakhsh, “A novel particle swarm optimization algorithm with adaptive inertia weight,” Applied Soft Computing, vol. 11(4), pp. 3658-3670, 2011.
[3]M. A. Arasomwan and A. O. Adewumi, “On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization,” The Scientific World Journal, vol. 2013, Article ID 860289, pp. 1-12, 2013.
[4]Y. H. Shi and R.C. Eberhart, “A modified particle swarm optimizer,” in IEEE International Conference on Evolutionary Computation, pp. 69–73. 1998.
[5]R. C. Eberhart and Y. H. Shi, “Tracking and optimizing dynamic systems with particle swarms,” in: Congress on Evolutionary Computation, 2001.
[6]R. C. Eberhart and Y. H. Shi, “Comparing inertia weights and constriction factors in particle swarm optimization,” in IEEE Congress on Evolutionary Computation, pp. 84–88, 2000.
[7]Y. H. Shi and R. C. Eberhart, “Experimental study of particle swarm optimization,” in SCI2000 Conference, 2000.
[8]J. Xin, G. Chen, and Y. Hai., “A Particle Swarm Optimizer with Multistage Linearly-Decreasing Inertia Weight,” in IEEE International Joint Conference on Computational Sciences and Optimization (CSO-2009), pp. 505–508, 2009.
[9]A. Nikabadi and M. Ebadzadeh, “Particle swarm optimization algorithms with adaptive Inertia Weight: A survey of the state of the art and a Novel method,” IEEE journal of evolutionary computation, 2008.
[10]R. F. Malik, T. A. Rahman, S. Z. M. Hashim, and R. Ngah, “New Particle Swarm Optimizer with Sigmoid Increasing Inertia Weight,” International Journal of Computer Science and Security, vol. 1(2), pp. 35-44, 2007.
[11]Y. Feng, G. F. Teng, A. X. Wang, and Y.M. Yao., “Chaotic Inertia Weight in Particle Swarm Optimization,” in Second IEEE International Conference on Innovative Computing, Information and Control. ICICIC-07, pp. 475-475, 2007.
[12]K. Kentzoglanakis and M. Poole., “Particle swarm optimization with an oscillating Inertia Weight,” in 11th Annual conference on Genetic and evolutionary computation, pp 1749–1750, 2009.
[13]M. S. Arumugam and M. V. C. Rao, “On the performance of the particle swarm optimization algorithm with various Inertia Weight variants for computing optimal control of a class of hybrid systems,” Discrete Dynamics in Nature and Society, vol. 2006, Article ID 79295, pp. 1-17, 2006.
[14]W. Al-Hassan, M. B. Fayek, and S.I. Shaheen, “PSOSA: An optimized particle swarm technique for solving the urban planning problem,” in The IEEE International Conference on Computer Engineering and Systems, pp 401–405, 2006.
[15]H. R. Li and Y.L. Gao, “Particle Swarm Optimization Algorithm with Exponent Decreasing Inertia Weight and Stochastic Mutation,” in The IEEE Second International Conference on Information and Computing Science, pp. 66–69, 2009.
[16]G. Chen, X. Huang, J. Jia, and Z. Min, “Natural exponential Inertia Weight strategy in particle swarm optimization”, in The IEEE Sixth World Congress on Intelligent Control and Automation (WCICA-2006), pp. 3672–3675, 2006.
[17]Y. Gao, X. An, and J. Liu., “A Particle Swarm Optimization Algorithm with Logarithm Decreasing Inertia Weight and Chaos Mutation”, in IEEE International Conference on Computational Intelligence and Security (CIS’08), pp. 61–65, 2008.
[18]A. Chatterjee and P. Siarry, “Nonlinear inertia weight variation for dynamic adaption in particle swarm optimization,” Computer and Operations Research, vol. 33 pp. 859–871, 2006.
[19]A. Khan, S. Mandal, R. K. Pal, and G. Saha, “Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence,” Scientifica, vol. 2016, Article ID 1060843, pp. 1-14, 2016.
[20]N. C. Bansal, P. K. Singh, M. Saraswat, A. Verma, S. S. Jadon, and A. Abraham, “Inertia Weight strategies in Particle Swarm Optimization,” in Third World Congress on Nature and Biologically Inspired Computing (NaBIC’11), pp. 633-640, 2011.
[21]S. Mirjalili, S.M. Mirjalili, and A.Lewis, “Grey wolf optimizer,” Advances in Engineering Software, vol. 69, pp.46-61, 2014.
[22]M. Dorigo, M. Birattari, and T. Stutzle,. Ant colony optimization. IEEE computational intelligence magazine, 1(4), pp.28-39. 2006.
[23]D. Karaboga, B. Gorkemli, , C. Ozturk, and N. Karaboga, “A comprehensive survey: artificial bee colony (ABC) algorithm and applications,” Artificial Intelligence Review, vol. 42(1), pp.21-57, 2014.
[24]X. S. Yang, “A new metaheuristic bat-inspired algorithm,” Nature inspired cooperative strategies for optimization (NICSO 2010), pp. 65-74, 2010.
[25]X. Yingwei, “Research on SVG DC-Side Voltage Control Based-on PSO Algorithm,” International Journal of Information Technology and Computer Science, vol. 8(10), pp.29-38, 2016. DOI: 10.5815/ijitcs.2016.10.04
[26]F. S. Milani and A. H. Navin, “Multi-Objective Task Scheduling in the Cloud Computing based on the Patrice Swarm Optimization,” International Journal of Information Technology and Computer Science, vol. 7(5), pp.61-66, 2015. DOI: 10.5815/ijitcs.2015.05.09
[27]A. Verma and S. Kaushal, “Cost Minimized PSO based Workflow Scheduling Plan for Cloud Computing”, International Journal of Information Technology and Computer Science, vol. 7(8), pp. 37-43, 2015. DOI: 10.5815/ijitcs.2015.08.06
[28]A. Babazadeh, H. Poorzahedy and S. Nikoosokhan, “Application of particle swarm optimization to transportation network design problem,” Journal of King Saud University-Science, vol. 23(3), pp.293-300, 2011.

International Journal of Modern Education and Computer Science (IJMECS)