Abstract
Concept of the particle swarms emerged from a simulation of the collective behavior of social creatures and gradually evolved into a powerful global optimization technique, now well-known as the Particle Swarm Optimization (PSO). A vast amount of analytical studies on various aspects of the PSO dynamics like stability, convergence, explorative power, sampling distribution and so on can be found in the literature. The boundary of the swarm is still as a challenging research interest. The upper boundary restricts the swarm members within a sub-region of the whole search space. Higher the upper boundary, higher is the diversity. This paper investigates mainly the diversity of the swarm in terms of the upper boundary of the swarm.
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References
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proc. IEEE Int. Conf. Neural Netw. (ICNN)., vol. 4, pp. 942–1948 (November 1995)
Venter, G., Sobieski, J.S.: Particle swarm optimization. AIAA Journal 41(8), 1583–1589 (2003)
Bratton, D., Kennedy, J.: Defining a standard for particle swarm optimization. In: IEEE Swarm Intelligence Symposium (2007)
Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. John Wiley & Sons (2006)
Clerc, M.: Particle Swarm Optimization. ISTE Publications (2008)
Hendtlass, T.: The particle swarm algorithm. In: Computational Intelligence: A Compendium, pp. 1029–1062 (2008)
Ozcan, E., Mohan, C.K.: Analysis of a simple particle swarm optimization system. In: Proc. Intell. Eng. Syst. Through Artificial. Neural Netw. (ANNIE), vol. 8, pp. 253–258 (October 1998)
Ozcan, E., Mohan, C.: Particle swarm optimization: surfing the waves. In: Proc. Congr. Evol. Comput., pp. 1939–1944. IEEE Service Center, Piscataway (1999)
Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. on Evolutionary Computation 6(1), 58–73 (2002)
Fernández-Martinez, J.L., Garcia-Gonzalo, E.: Stochastic stability analysis of the linear continuous and discrete PSO models. IEEE Trans. on Evolutionary Computation 15(3), 405–423 (2011)
Ghosh, S., Kundu, D., Suresh, K., Das, S., Abraham, A., Panigrahi, B.K., Snášel, V.: On some properties of the lbest topology in particle swarm optimization. In: Ninth International Conference on Hybrid Intelligent Systems (HIS, 2009), Shenyang, China, August 12–14 (2009)
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Maity, D., Halder, U. (2012). Convergence and Boundary Estimation of the Particle Dynamics in Generalized Particle Swarm Optimization. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Nanda, P.K. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2012. Lecture Notes in Computer Science, vol 7677. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35380-2_4
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DOI: https://doi.org/10.1007/978-3-642-35380-2_4
Publisher Name: Springer, Berlin, Heidelberg
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