Abstract
Particle Swarm Optimization (PSO) technique has proved its ability to deal with very complicated optimization and search problems. Several variants of the original algorithm have been proposed. This paper proposes a novel hybrid PSO – evolutionary algorithm for solving the well known geometrical place problems. Finding the geometrical place could be sometimes a hard task. In almost all situations the geometrical place consists more than one single point. The performance of the newly proposed PSO algorithm is compared with evolutionary algorithms. The main advantage of the PSO technique is its speed of convergence. Also, we propose a hybrid algorithm, combining PSO and evolutionary algorithms. The hybrid combination is able to detect the geometrical place very fast for which the evolutionary algorithms required more time and the conventional PSO approach even failed to find the real geometrical place.
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References
Bergh, F.D., Engelbrecht, A.: A Cooperative Approach to Particle Swarm Optimization. IEEE Transaction on Evolutionary Computation 8(3), 225–239 (2004)
Clerc, M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 1999), pp. 1951–1957 (1999)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39–43 (1995)
Eberhart, R.C., Simpson, P.K., Dobbins, R.W.: Computational Intelligence PC Tools. Academic Press Professional, Boston (1996)
Eberhart, R.C., Shi, Y.: Particle swarm optimization: developments, applications and resources. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2001), Seoul, Korea (2001)
Grosan, C.: Solving geometrical place problems by using Evolutionary Algorithms. In: Kaaniche, M. (ed.) World Computer Congress, Student Forum, Toulouse, France, pp. 365–375 (2004)
Hu, X., Shi, Y., Eberhart, R.C.: Recent Advences in Particle Swarm. In: Congress on evolutionary Computation, Portland, Oregon, June 19-23, pp. 90–97 (2004)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, Perth, Australia, vol. IV, pp. 1942–1948. IEEE Service Center, Piscataway (1995)
Kennedy, J.: Minds and cultures:Particle swarm implications. Socially Intelligent Agents: Papers from the 1997 AAAI Fall Symposium. Technical Report FS-97-02, Menlo Park, CA: AAAI Press, pp. 67–72 (1997)
Kennedy, J.: The Behavior of Particles. In: 7th Annual Conference on Evolutionary Programming, San Diego, USA (1998)
Krohling, R.A., Hoffmann, F., Coelho, L.S.: Co-evolutionary Particle Swarm Optimization for Min-Max Problems using Gaussian Distribution. In: Proceedings of the Congress on Evolutionary Computation CEC 2004, vol. 1, pp. 959–964. IEEE Press, Los Alamitos (2004)
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimization. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1945–1950. IEEE Service Center, Piscataway (1999)
Shi, Y., Eberhart, R.C.: Parameter selection in particle swarm optimization. In: Proceedings of the 1998 Annual Conference on Evolutionary Computation (1998)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of the IEEE Congress on Evolutionary Computation CEC 1998, Piscataway, NJ, pp. 69–73 (1998)
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Grosan, C., Abraham, A., Han, S., Gelbukh, A. (2005). Hybrid Particle Swarm – Evolutionary Algorithm for Search and Optimization. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_63
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DOI: https://doi.org/10.1007/11579427_63
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