Abstract
Recently, Particle Swarm Optimization (PSO), gained vast attention and applied to variety of engineering optimization problems because of its simplicity and efficiency. The performance of the PSO algorithm can be further improved by hybrid techniques. There are numerous hybrid PSO algorithms published in the literature where researchers combine the benefits of PSO with other heuristic algorithms. In this paper, we propose a cooperative line search particle swarm optimization (CLS-PSO) algorithm by integrating local line search technique and standard PSO (S-PSO). The performance of the proposed hybrid algorithm, examined through six typical nonlinear optimization problems, is reported. Our experimental results show that CLS-PSO outperforms S-PSO.
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References
Banks, A., Vincent, J., Anyakoha, C.: A Review of Particle Swarm Optimization. Part I: Background and Development. Natural Computing 6(4), 46–484 (2007)
Banks, A., Vincent, J., Anyakoha, C.: Review of Particle Swarm Optimization. Part II: Hybridization, Combinatorial, Multicriteria and Constrained Optimization, and Indicative Applications. Natural Computing 7(1), 109–124 (2008)
Li, Y., Chen, X.: Mobile Robot Navigation Using Particle Swarm Optimization and Adaptive NN. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 628–631. Springer, Heidelberg (2005)
Omran, M., Salman, A., Engelbrecht, A.P.: Image Classification Using Particle Swarm Optimization. In: 4th Asia-Pacific Conference on Simulated Evolution and Learning, pp. 370–374 (2002)
Xia, W.J., Wu, Z.M.: A Hybrid Particle Swarm Optimization Approach for the Job-Shop Scheduling Problem. International Journal of Advanced Manufacturing Technology 29, 360–366 (2006)
Asselmayer, T., Ebeling, W., Rose, H.: Evolutionary Strategies of Optimization. Phys. Rev. E 56(1), 1171–1180 (1997)
Whittey, D.: A Genetic Algorithm Tutorial. Statistical Computation 4(2), 65–85 (1994)
Dorigo, M., Maniezzo, V., Colorni, A.: Ant System: Optimization by A Colony of Cooperating Agents. IEEE transactions on Systems, Man and Cybernetics-part B 26, 29–41 (1996)
Wang, L.: Intelligent Optimization Algorithms with Application. Tsinghua University and Springer Press, Beijing (2001)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: IEEE Int. Conf. on Neural Networks, pp. 1942–1948. IEEE Press, Piscataway (1995)
Yang, G., Chen, D., Zhou, G.: A New Hybrid Algorithm of Particle Swarm Optimization. In: Huang, D.S., Li, K., Irwin, G.W. (eds.) ICIC 2006. LNCS (LNBI), vol. 4115, pp. 50–60. Springer, Heidelberg (2006)
Zhang, Q., Li, C., Liu, Y., Kang, L.: Fast Multi-swarm Optimization with Cauchy Mutation and Crossover Operation. In: Yang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 344–352. Springer, Heidelberg (2007)
Chen, J., Zheng, Q., Yu, L., Jiang, L.: Particle Swarm Optimization with Local Search. In: IEEE Int. Conf. on Neural Networks and Brain, China, Beijing, pp. 481–484 (2005)
Das, S., Koduru, P., Min, G., Cochran, M., Wareing, A., Welch, S.M., Babin, B.R.: Adding Local Search to Particle Swarm Optimization. In: IEEE Congress on EC, pp. 428–433 (2005)
Wang, J., Zhou, Y.: Quantum-Behaved Particle Swarm Optimization with Generalized Local Search Operator for Global Optimization. In: Huang, D.-S., Heutte, L., Loog, M. (eds.) ICIC 2007. LNCS, vol. 4682, pp. 851–860. Springer, Heidelberg (2007)
Liang, X., Xu, C., Qian, J.: A Trust Region-type Method for Solving Monotone Variational Inequality. Journal of Computational Mathematics 18(1), 13–14 (2000)
Liang, X., Xu, C., Hu, J.: A Potential Reduction Algorithm for Monotone Variational Inequality Problems. Systems Science and Mathematical Sciences 13(1), 59–66 (2000)
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Liang, X., Li, X., Ercan, M.F. (2009). A PSO – Line Search Hybrid Algorithm. In: Gervasi, O., Taniar, D., Murgante, B., Laganà, A., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2009. ICCSA 2009. Lecture Notes in Computer Science, vol 5593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02457-3_47
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DOI: https://doi.org/10.1007/978-3-642-02457-3_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02456-6
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