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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 8    
Website: http://www.ijser.org
scirp IJSER >> Volume 3,Issue 8,August 2012
Half Mean Particle Swarm Optimization Algorithm
Full Text(PDF, )  PP.990-998  
Narinder Singh, Sharandeep Singh, S.B. Singh and Shelly Arora
: SPSO, HMPSO, global optimization, velocity update equation, pbest (personal best position), gbest (global best position)
This paper introduces a Half Mean Particle Swarm Optimization algorithm (HMPSO) and discusses the results of experimentally comparing the performances of SPSO. This is done by replacing one term of original velocity update equation by one new terms based on the linear combination of pbest and gbest. Its performance is compared with the standard PSO (SPSO) by testing it on a 29 benchmark test problems (15 Scalable and 13 Non-Scalable Problems). Based on the numerical and graphical analyses of results it is shown that the HMPSO outperforms the SPSO (Standard Particle Swarm Optimization), in terms of efficiency, reliability, accuracy and stability.
[1] R. Eberhart and J. Kennedy, ‚A new optimizer using particle swarm theory‛, Proceeding 6th Int. Symp. Micro Machine and Human Science, Nagoya, Japan, pp. 39–43, 1995.

[2] Y. Shi and R. Eberhart, ‚A Combined Particle Swarm Optimizer‛, Proceedings of IEEE World Congress on Computational Intelligence, pp. 69–73, 1998.

[3] V. Tandon, ‚Closing The Gap Between CAD/CAM and Optimized CNC and Milling‛, Master thesis, Purdue School of Engineering and Technology, Indiana University Purdue University Indianapolis, 2000.

[4] Abido M.A., ‚Optimal Power Flow Using Particle Swarm Optimization‛, Electri Power and Energy System, vol. 24, pp. 563–71, 2002.

[5] Jiang Chuanwen, Etorre Bompard, ‚A Hybrid Method Of Chaotic Particle Swarm Optimization and Linear Interior For Reactive Power Optimization‛, Mathematics and Computers in Simulation , vol. 68 , pp. 57–65, 2005.

[6] N. Shigenori, G. Takamu, Y. Toshiku, F. Yoshikazu, ‚A Hybrid Particle Swarm Optimization For Distribution State Estimation‛, IEEE Transactions on Power Systems, vol. 18 , pp. 60– 68, 2003.

[7] R. C. Eberhart and Y. Shi, ‚Comparing Inertia Weights and Constriction Factors In Particle Swarm Optimization‛ In ProceedingCEC, San Diego, CA, pp. 84–88, 2000.

[8] Ioan Cristian Trelea, ‚The Particle Swarm Optimization Algorithm: Convergence Analysis And Parameter Selection‛, Information Processing Letters, vol. 85 , pp. 317–325, 2003.

[9] R. Eberhart and Y. Shi, ‚Comparison between Genetic Algorithms and Particle Swarm Optimization‛, The 7th Annual Conference on Evolutionary Programming, San Diego, USA, 1998.

[10] Narinder Singh and S.B.Singh, ‚ One Half Global Best Position Particle Swarm Optimization‛, International Journal of Scientific & Engineering Research, vol. 2, no. 8,: ISSN 2229-5518, 2011.

[11] Narinder Singh and S.B.Singh,‚Personal Best Position Particle Swarm Optimization‛, Journal of Applied Computer Science & Mathematics, Suceava, vol. 12, no.6, 2012,

[12] Narinder Singh and S.B.Singh, ‚A New Version of Particle Swarm Optimization Algorithm‛, Accepted in Journal of Artificial Intelligence, bio informatics, EBSCO Publishing, USA; Ovid Technologies, USA; ProQuest, USA, 2012

[13] Jun-qing Li et al, ‚An effective hybrid particle swarm optimization algorithm for flexible job-shop scheduling problem‛, MASAUM Journal of Computing , vol.1 no.1, pp. 69-74, 2009.

[14] Jun-qing Li, Quan-ke Pan, Sheng-xian Xie, Bao-xian Jia & Yuting Wang, ‚ A hybrid particle swarm optimization and tabu search algorithm for flexible job-shop scheduling problem ‚,International Journal of Computer Theory and Engineering, vol. 2, no. 2, pp.1793-1801, 2010.

[15] Davoud Sedighizadeh and Ellips Masehian, ‚Particle Swarm Optimization Methods, Taxonomy and Applications‛, International Journal of Computer Theory and Engineering, vol. 1, no. 5, pp. 1793-8201, 2009.

[16] J. Zhang, C. Zhang, T. Chu, and M. Perc, ‚Resolution of the Stochastic Strategy Spatial Prisoner’s Dilemma by Means of Particle Swarm Optimization‛, PloS ONE, www.plosone.org, vol 6, no. 7, 2011.

[17] S. J. Bassi, M. K. Mishra & E. E. Omizegba, ‚Automatic Tuning Of Proportional Integral Derivative (PID) Controller Using Particle Swarm Optimization (PSO) Algorithm‛, International Journal of Artificial Intelligence & Applications (IJAIA), vol.2, no.4, October 2011.

[18] Taoshen LI, Zhigang ZHAO, Zhihui GE, ‚An Adaptive Particle Swarm Optimization Algorithm for Anycast Routing‛ Journal of Computational Information Systems, Available at http://www.Jofcis.com, vol. 7, no. 5 , pp.1559-1566, 2011.

[19] Pinar Civicioglu & Erkan Besdok, ‚A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms‛, Artificial Intelligence Rev DOI 10.1007/s 10462-011-9276-0, 2011.

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