Abstract
Particle Swarm Optimization (PSO) is a stochastic computation technique aimed at finding the optimal solution to a problem. It is a population based technique inspired by the behavior of a flock of birds or school of fish, developed by Dr. Eberhart and Dr. Kennedy in 1995. The original algorithm suffers from drawbacks like premature convergence at local optimum solution (optima), and high computational cost with little robustness in case of multi-modal problems (problems involving multiple optima). This paper introduces a concept aimed at increasing the diversity (exploration of the search space) portrayed by these particles. The algorithm implements a form of teleportation by which particles are randomly re-initialized in the search space once their behavior becomes predictable. Two approaches to the implementation of this idea shall be described and discussed here. The predictability is modeled using a hyper-sphere of variable radius, centered at the best known solution.
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References
Eberhart, R. C., Shi, Y.: Particle Swarm Optimization: Developments, Applications and Resources. In: Proc. of the IEEE Cong. on Evol. Comp., vol. 1, pp. 81–86 (2001).
Poli, R.: An Analysis of Publications on Particle Swarm Optimization Applications. Technical Report CSM-469, Department of Computer Science, University of Essex, Colchester, Essex, UK (2007).
Shi, Y., Eberhart, R. C.: Empirical Study of Particle Swarm Optimization. In: Proc. of the IEEE Cong. on Evol. Comp. (CEC 1999), vol. 3, pp. 1945-1950 (1999).
Hu, X.: Particle Swarm Optimization. http://www.swarmintelligence.org/index.php (2006).
Schutte, J. F.: The Particle Swarm Optimization Algorithm. EGM 6365 - Structural Optimization, Fall 2005, Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL (2005).
Sedighizadeh, D., Masehian, E.: Particle Swarm Optimization: Methods, Taxonomy and Applications. In: International Journal of Computer Theory and Engineering, vol. 1, pp. 1793-8201 (2009).
Xie, X.-F., Zhang, W.-J., Yang, Z. L.: Adaptive Particle Swarm Optimization on Indi-vidual Level. In: In Proc. of the 6th Int. Conf. on Signal Processing. vol. 2, pp. 1215-1218 (2002).
Lam, H. T., Nikolaevna, P. N., Quan, N. T. M.: A Heuristic Particle Swarm Optimization. In: Proc. of the 9th Annual Conf. on Genetic and evol. comp. (2007).
Shen, X., Li, Y., Yang, J., Yu, L.: A Heuristic Particle Swarm Optimization for Cut-ting Stock Problem Based on Cutting Pattern. In: Lecture Notes in Computer Science, vol. 4490, pp. 1175-1178 (2007).
Xinchao, Z.: A Perturbed Particle Swarm Algorithm for Numerical Optimization. In: Applied Soft Computing, vol. 10, pp. 119-124 (2010).
Zhang, X., Hu, W., Li, W., Qu, W., Maybank, S.: Multi-Object Tracking via Species Based Particle Swarm Optimization. In: IEEE 12th Int. Conf. on Computer Vision Workshops, ICCV Workshops, pp. 1105-1112 (2009).
Li, X.: Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization. In: Proc. Genetic Evol. Comput. Conf., pp. 105–116 (2004).
Sugathan, P. N.: Particle Swarm Optimization & Differential Evolution. http://ewh.ieee.org/cmte/cis/mtsc/ieeecis/tutorial2007/CEC2007/P_N_Suganthan.pdf (2007).
Liang, J. J., Qin, A. K., Suganthan, P. N., Baskar, S.: Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions. In: IEEE Trans. Evol. Comput., vol. 10, pp. 281–295 (2006).
Zhao, S. Z., Liang, J. J., Suganthan, P. N., Tasgetiren, M. F.: Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search for Large Scale Global Optimization. In: Proc. of IEEE Cong. on Evol. Comp., pp.3845-3852 (2008).
Zhao, S. Z., Suganthana, P. N., Pan, Q.-K., Tasgetiren, M. F.: Dynamic Multi -Swarm Particle Swarm Optimizer with Harmony Search. In: Expert Systems with Applica-tion, vol. 38, pp. 3735-3742 (2011).
Mendes, R., Kennedy, J., Neves, J.: The Fully Informed Particle Swarm: Simpler, Maybe Better. In: IEEE Trans. Evol. Comput., vol. 8, pp. 204–210 (2004).
Molga, M., Smutnicki, C.: Test Functions for Optimization Needs. http://www.zsd.ict.pwr.wroc.pl/files/docs/functions.pdf (2005).
Katebi, S.D.: Function Optimization Using GA, ES and EP. http://pasargad.cse.shirazu.ac.ir/~mhaji/ec2/EC_OPT/Project1.htm (2005).
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Budhraja, K.K., Singh, A., Dubey, G., Khosla, A. (2013). Exploration Enhanced Particle Swarm Optimization using Guided Re-Initialization. In: Bansal, J., Singh, P., Deep, K., Pant, M., Nagar, A. (eds) Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Advances in Intelligent Systems and Computing, vol 201. Springer, India. https://doi.org/10.1007/978-81-322-1038-2_34
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DOI: https://doi.org/10.1007/978-81-322-1038-2_34
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