Abstract
Dynamic optimization in which global optima and local optima change over time is always a hot research topic. It has been shown that particle swarm optimization works well facing into dynamic environments. From another hands, learning automata is considered as an intelligent tool (agent) which can learn what action is the best one interacting with its environment. The great deluge algorithm is also a search algorithm applied to optimization problems. All these algorithms have their special drawbacks and advantages. In this paper it is examined can the combination of these algorithms results in the better performance dealing with dynamic problems. Indeed a learning automaton is employed per each particle of the swarm to decide whether the corresponding particle updates its velocity (and consequently its position) considering the best global particle, the best local particle or the combination global and local particles. Water level in the deluge algorithm is used in the progress of the algorithm. Experimental results on different dynamic environments modeled by moving peaks benchmark show that the combination of these algorithms outperforms Particle Swarm Optimization (PSO) algorithm, Fast Multi-Swarm Optimization (FMSO) method, a similar particle swarm algorithm for dynamic environments, for all tested environments.
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References
Blackwell, T., Branke, J.: Multi-Swarms, Exclusion, and Anti-Convergence in Dynamic Environments. IEEE Transactions on Evolutionary Computation 10, 459–472 (2006)
Blackwell, T., Branke, J.: Multi-Swarm Optimization in Dynamic Environments. Applications of Evolutionary Computing, 489–500 (2004)
Blackwell, T., Branke, J., Li, X.: Particle Swarms for Dynamic Optimization Problems. Swarm Intelligence, 193–217 (2008)
Branke, J.: Memory Enhanced Evolutionary Algorithms for Changing Optimization Problems. In: 1999 Congress on Evolutionary Computation, Washington D.C., USA, vol. 3, pp. 1875–1882 (1999)
Dueck, G.: New Optimization Heuristics. The Great Deluge Algorithm and the Record-to-Record Travel. Journal of Computational Physics 104, 86–92 (1993)
Hashemi, A.B., Meybodi, M.R.: Cellular PSO: A PSO for Dynamic Environments. Advances in Computation and Intelligence, 422–433 (2009)
Janson, S., Middendorf, M.: A Hierarchical Particle Swarm Optimizer for Dynamic Optimization Problems. Applications of Evolutionary Computing, 513–524 (2004)
Janson, S., Middendorf, M.: A hierarchical particle swarm optimizer for noisy and dynamic environments. Genetic Programming and Evolvable Machines 7, 329–354 (2006)
Kamosi, M., Hashemi, A.B., Meybodi, M.R.: A New Particle Swarm Optimization Algorithm for Dynamic Environments. In: Panigrahi, B.K., Das, S., Suganthan, P.N., Dash, S.S. (eds.) SEMCCO 2010. LNCS, vol. 6466, pp. 129–138. Springer, Heidelberg (2010)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, Piscataway, NJ, vol. IV, pp. 1942–1948 (1995)
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Evolutionary Computation Congress, Honolulu, Hawaii, USA, pp. 1671–1676 (2002)
Moser, I.: All Currently Known Publications on Approaches Which Solve the Moving Peaks Problem. Swinburne University of Technology, Melbourne (2007)
Hu, X., Eberhart, R.C.: Adaptive particle swarm optimization: detection and response to dynamic systems. In: IEEE Congress on Evolutionary Computation, Honolulu, HI, USA, vol. 2, pp. 1666–1670 (2002)
Li, X., Dam, K.H.: Comparing particle swarms for tracking extrema in dynamic environments. In: IEEE Congress on Evolutionary Computation, Canberra, Australia, pp. 1772–1779 (2003)
Li, C., Yang, S.: A clustering particle swarm optimizer for dynamic optimization. In: IEEE Congress on Evolutionary Computation, pp. 439–446 (2009)
Li, C., Yang, S.: Fast Multi-Swarm Optimization for Dynamic Optimization Problems. In: Fourth International Conference on Natural Computation, Jinan, Shandong, China, vol. 7, pp. 624–628 (2008)
Liu, L., Wang, D., Yang, S.: Compound Particle Swarm Optimization in Dynamic Environments. Applications of Evolutionary Computing, 616–625 (2008)
Liu, L., Yang, S., Wang, D.: Particle Swarm Optimization with Composite Particles in Dynamic Environments. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 1–15 (2010)
Lung, R.I., Dumitrescu, D.: A collaborative model for tracking optima in dynamic environments. In: IEEE Congress on Evolutionary Computation, Singapore, pp. 564–567 (2007)
Thomsen, R.: Multimodal optimization using crowding-based differential evolution. In: IEEE Congress on Evolutionary Computation, Portland, Oregon, USA, pp. 1382–1389 (2004)
Viswanathan, R.: Learning automaton: Models and applications. Ph.D. dissertation, Yale Univ., New Haven, CT (1972)
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Parvin, H., Minaei, B., Ghatei, S. (2011). A New Particle Swarm Optimization for Dynamic Environments. In: Herrero, Á., Corchado, E. (eds) Computational Intelligence in Security for Information Systems. Lecture Notes in Computer Science, vol 6694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21323-6_37
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DOI: https://doi.org/10.1007/978-3-642-21323-6_37
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