Abstract
Evolutionary computation has become an important problem solving methodology among the set of search and optimization techniques. Recently, more and more different evolutionary techniques have been developed, especially hybrid evolutionary algorithms. This paper proposes an island based hybrid evolutionary algorithm (IHEA) for optimization, which is based on Particle swarm optimization (PSO), Fast Evolutionary Programming (FEP), and Estimation of Distribution Algorithm (EDA). Within IHEA, an island model is designed to cooperatively search for the global optima in search space. By combining the strengths of the three component algorithms, IHEA greatly improves the optimization performance of the three basic algorithms. Experimental results demonstrate that IHEA outperforms all the three component algorithms on the test problems.
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References
Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1(1), 67–82 (1997)
Zmuda, M.A., Rizki, M.M., Tamburino, L.A.: Hybrid evolutionary learning for synthesizing multi-class pattern recognition systems. Applied Soft Computing 2(4), 269–282 (2003)
Wang, L.: A hybrid genetic algorithm-neural network strategy for simulation optimization. Applied Mathematics and Computation 170(2), 1329–1343 (2005)
Shi, X.H., Liang, Y.C., Lee, H.P., Lu, C., Wang, L.M.: An improved GA and a novel PSO-GA-based hybrid algorithm. Information Processing Letters 93(5), 255–261 (2005)
Grimaldi, E.A., Grimacia, F., Mussetta, M., Pirinoli, P., Zich, R.E.: A new hybrid genetical C swarm algorithm for electromagnetic optimization. In: Proc. of Int. Conf. on Computational Electromagnetics and its Applications, pp. 157–160 (2004)
Li, C., Liu, Y., Kang, L., Zhou, A.: A Fast Particle Swarm Optimization Algorithm with Cauchy Mutation and Natural Selection Strategy. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 334–343. Springer, Heidelberg (2007)
Wang, H., Liu, Y., Li, C., Zeng, S.: A Hybrid Particle Swarm Algorithm with Cauchy Mutation. In: Proc.of the 2007 IEEE Swarm Intelligence Symposium (2007)
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proc. of the 6th Int. Symp. on Micro Machine and Human Science, pp. 39–43 (1995)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. of the 1995 IEEE Int. Conf. on Neural Networks, pp. 1942–1948 (1995)
Shi, Y., Eberhart, R.C.: A Modified Particle Swarm Optimizer. In: Proc. of the IEEE Int. Conf. on Evol. Comput., pp. 69–73 (1998)
Clerc, M., Kennedy, J.: The Particle Swarm: Explosion, Stability and Convergence in a Multi-Dimensional Complex Space. IEEE Trans. on Evol. Comput. 6(1), 58–73 (2002)
Yao, X., Liu, Y.: Fast evolutionary programming. In: Proc. of the 5th Annual Conference on Evolutionary Programming (EP 1996), pp. 451–460 (1996)
Bäck, T., Schwefel, H.-P.: An overview of evolutionary algorithms for parameter optimization. Evol. Comput. 1(1), 1–23 (1993)
Fogel, D.B.: An introduction to simulated evolutionary optimization. IEEE Trans. Neural Networks 5(1), 3–14 (1994)
Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. on Evol. Comput. 3(1), 82–102 (1999)
Zhang, B.-T.: A Bayesian framework for evolutionary computation. In: Proc. of the 1999 Congress on Evol. Comput, pp. 722–728 (1999)
Larrañaga, P., Lozano, J.A.: Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer Academic Publishers, Dordrecht (2001)
van den Bergh, F.: An Analysis of Particle Swarm Optimizers. PhD thesis, Department of Computer Science, University of Pretoria, South Africa (2002)
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Li, C., Yang, S. (2008). An Island Based Hybrid Evolutionary Algorithm for Optimization. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_19
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DOI: https://doi.org/10.1007/978-3-540-89694-4_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89693-7
Online ISBN: 978-3-540-89694-4
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