Abstract
Particle Swarm Optimization (PSO) is one of the evolutionary computation techniques based on the social behaviors of birds flocking or fish schooling, biologically inspired computational search and optimization method. Since first introduced by Kennedy and Eberhart [7] in 1995, several variants of the original PSO have been developed to improve speed of convergence, improve the quality of solutions found, avoid getting trapped in the local optima and so on. This paper is focused on performing a comparison of different PSO variants such as full model, only cognitive, only social, weight inertia, and constriction factor. We are using a set of 4 mathematical functions to validate our approach. These functions are widely used in this field of study.
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References
Angeline, P.J.: Evolutionary Optimization versus Particle Swarm Optimization: Philosophy and Performance Differences. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 601–610. Springer, Heidelberg (1998)
Brits, R., Engelbrecht, A.P., van den Bergh, F.: A Niching Particle Swarm Optimizer. In: Proceedings of the Fourth Asia-Pacific Conference on Simulated Evolution and Learning, pp. 692–696 (2002)
Carlisle, A., Dozier, G.: Adapting Particle Swarm Optimization to Dynamic Environments. PhD thesis. Auburn University (2002)
Cristian, T.I.: The particle swarm optimization algorithm: convergence analysis and parameter selection. Information Processing Letters 85(6), 317–325 (2003)
Clerc, M.: The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 3, pp. 1951–1957 (July 1999)
Clerc, M., Kennedy, J.: The Particle Swarm-Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Eberhart, R.C., Kennedy, J.: A New Optimizer using Particle Swarm Theory. In: Procedings of the Sixth International Symposium on MicroMachine and Human Science, pp. 39–43 (1995)
Eberhart, R.C., Shi, Y.: Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 84–88 (July 2000)
Eberhart, R.C., Shi, Y.: Particle Swarm Optimization: Developments, Applications and Resources. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 27–30. IEEE Press (May 2001)
Kennedy, J.: The behaviour of particles. Evol. Progr. VII, 581–587 (1998)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Joint Conference on Neuronal Networks, pp. 1942–1948. IEEE Press (1995)
Kennedy, J., Spears, W.: Matching Algorithms to problems: An Experimental Test of the Particle Swarm and Some Genetic Algorithms on the Multimodal Problem Generator. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 78–83. IEEE Press (May 1998)
Ribeiro, P.F., Kyle Schlansker, W.: A Hybrid Particle Swarm and Neuronal Network Approach for Reactive Power Control. IEEE (2006)
Russell, C., Eberthart, Hu, X.: Human Tremor Analysus Using Particle Swarm Optoimization. Purdue Shool of Engineering and Technology, Indiana University Purdue University Indianapolis, Indianapolis (1999)
Salerno, J.: Using the Particle Swarm Optimization Technique to Train a Recurrent Neural Model. In: In Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, pp. 45–49. IEEE Press (November 1997)
Shi, Y.H., Eberhart, R.C.: Fuzzy adaptive particle swarm optimization. In: IEEE Int. Conf. on Evolutionary Computation, pp. 101–106 (2001)
Shi, Y.H., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73 (May 1998)
Shi, Y.H., Eberhart, R.C.: Parameter Selection in Particle Swarm Optimization. In: Porto, V.W., Waagen, D. (eds.) EP 1998. LNCS, vol. 1447, pp. 591–600. Springer, Heidelberg (1998)
Tasgetiren, M.F., Liang, Y.C., Sevkli, M., Gencyilmaz, G.: A Particle Swarm Optimization Algorithm for Makespan and Total Flowtime Minimization in the Permutation Flowshop Sequencing Problem. European Journal of Operational Research 177, 1930–1947 (2007)
Valdez, F., Melin, P., Castillo, O.: Evolutionary method combining particle swarm optimization and genetic algorithms using fuzzy logic for decision making. In: Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 2114–2119 (2009)
Valdez, F., Melin, P., Castillo, O.: An improved evolutionary method with fuzzy logic for combining Particle Swarm Optimization and Genetic Algorithms. Appl. Soft Comput. 11(2), 2625–2632 (2011)
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Vazquez, J.C., Valdez, F., Melin, P. (2013). Comparative Study of Particle Swarm Optimization Variants in Complex Mathematics Functions. In: Castillo, O., Melin, P., Kacprzyk, J. (eds) Recent Advances on Hybrid Intelligent Systems. Studies in Computational Intelligence, vol 451. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33021-6_18
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DOI: https://doi.org/10.1007/978-3-642-33021-6_18
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