Abstract
Like other stochastic algorithms, particle swarm optimization algorithm (PSO) has shown a good performance over global numerical optimization. However, PSO also has a few drawbacks such as premature convergence and low convergence speed, especially on complex problem. In this paper, we present a new approach called AMPSONS in which neighborhood search, diversity mechanism and adaptive mutation were utilized. Experimental results obtained from a test on several benchmark functions showed that the performance of proposed AMPSONS algorithm is superior to five other PSO variants, namely CLPSO, AMPSO, GOPSO, DNLPSO, and DNSPSO, in terms of convergence speed and accuracy.
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References
Kenedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neuron Networks Conference Proceedings, Perth, Australia, pp. 1942–1948 (1995)
Clerc, M., Kennedy, J.: The Particle Swarm-Explosion, Stability, and Convergence in a Multidimensional Complex Space. IEEE Trans on Evol. 6(1), 58–73 (2002)
Liang, J., Qin, A., Suganthan, P., Baskarr, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. Trans on Evol. 10, 281–295 (2006)
Wang, H., Wang, W., Wu, Z.: Particle swarm optimization with adaptive mutation for multimodal optimization. Applied Mathematics and Computation 221, 296–305 (2013)
Wang, H., Wu, Z., Rahnamayan, S., Liu, Y., Ventresca, M.: Enhancing particle swarm optimization using generalized opposition-based learning. Information Sciences 181, 4699–4714 (2011)
Nasir, M., Das, S.: A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization. Information Sciences 209, 16–36 (2012)
Wang, H., Sun, S., Li, C., Rahnamayan, S., Pan, J.: Diversity enhanced particle swarm optimization with neighborhood search. Information Sciences 223, 119–135 (2013)
Tran, D.C., Wu, Z., Wang, H.: A novel enhanced particle swarm optimization method with diversity and neighborhood search. In: IEEE International Conference on Systems, Man, and Cybernetics (SMC 2013), pp. 180–187 (2013)
Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. In: Proceedings of the 1998 Congress on Evolutionary Computation (CEC 1998), pp. 69–73 (1998)
Kennedy, J.: Small Worlds and Mega-Minds: Effects of Neighborhood Topology on Particle Swarm Performance. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1931–1938 (1999)
Stacey, S., Jancic, M., Grundy, I.: Particle swarm optimization with mutation. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 1425–1430 (2003)
Higashi, N., Iba, H.: Particle swarm optimization with Gaussian mutation. In: Proceeding of IEEE Swarm Intelligence Symposium, Indianapolis, pp. 72–79 (2003)
Krohling, R.: Gaussian particle swarm with jumps. In: Proceedings of IEEE Congress on Evolutionary Computation, pp. 1226–1231 (2005)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-TR06, Erciyes University, Engineering Faculty, Comput. Eng. Dep. (2005)
Brest, J., Greiner, S.: Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Trans on Evol. Comput. 10, 646–657 (2006)
Derrac, J.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1, 3–18 (2011)
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Tran, D.C., Wu, Z., Wang, H. (2014). A New Approach of Diversity Enhanced Particle Swarm Optimization with Neighborhood Search and Adaptive Mutation. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_18
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DOI: https://doi.org/10.1007/978-3-319-12640-1_18
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