Abstract
The particle swarm algorithm has shown ability to optimise in continuous problem spaces, although it can struggle in problem spaces containing multiple optima. A variant, called Waves of Swarm Particles (WoSP), has been shown to be able to handle problem spaces containing multiple optima by sequentially exploring these optima. In this chapter, the WoSP algorithm is adapted to suit complex quantised problem spaces and applied to a highly constrained problem with many constraint-violating solutions but few constraint-satisfying solutions. The performance obtained is remarkably good and reflects the power of the WoSP algorithm, which combines the search abilities of particle swarm with that of evolution.
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References
Blackwell, T., Branke, J.: Multi Swarms, Exclusion and Anti-Convergence in Dynamic Environments. IEEE Transaction on Evolutionary Computing 10(4), 459–472 (2006)
Brits, R., Engelbrecht, A.P., van den Bergh, F.: A Niching Particle Swarm Optimizer. In: Proceedings of the 4th Asia-Pacific Conference on Simulated Evolution and Learning 2002 (SEAL 2002), Singapore, pp. 692–696 (2002)
Clerc, M., Kennedy, J.: The Particle Swarm-explosion, Stability and Convergence in a Multi Dimensional Complex Space. IEEE Transactions on Evolutionary Computing 6(1), 58–73 (2002)
Eberhart, R.C., Dobbins, P., Simpson, P.: Computational Intelligence PC Tools. Academic Press, Boston (1996)
Eberhart, R.C., Shi, Y.: Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimisation. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 84–88 (2000)
Engelbrecht, A.P.: Fundamentals of Computational Swarm Intelligence. Wiley, West Sussex (2006)
Hendtlass, T.: A Particle Swarm Algorithm for High Dimensional, Problem Spaces. In: Proceedings of the IEEE Swarm Workshop, pp. 149–154 (2005)
Hendtlass, T.: WoSP: A Multi-Optima Particle Swarm Algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 727–734 (2005)
Hendtlass, T.: Fitness Estimation and the Particle Swarm Algorithm. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 4266–4272 (2007)
Janson, S., Middendorf, M.: A Hierarchical Particle Swarm Optimizer. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 1666–1670 (2003)
Janson, S., Middendorf, M.: A Hierarchical Particle Swarm Optimizer for Dynamic Optimization Problems. In: Raidl, G.R., Cagnoni, S., Branke, J., Corne, D.W., Drechsler, R., Jin, Y., Johnson, C.G., Machado, P., Marchiori, E., Rothlauf, F., Smith, G.D., Squillero, G. (eds.) EvoWorkshops 2004. LNCS, vol. 3005, pp. 513–524. Springer, Heidelberg (2004)
Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1948. IEEE, Piscataway (1995)
Kennedy, J., Eberhart, R.C.: The Particle Swarm: Social Adaptation in Information-Processing Systems. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, ch. 25. McGraw-Hill Publishing Company, England (1999)
Parrot, D., Li, X.: A Particle Swarm Model for Tracking Multiple Peaks in a Dynamic Environment using Speciation. In: Proceeding of the IEEE Congress on Evolutionary Computation, pp. 98–103 (2004)
Salami, M., Hendtlass, T.: A Fast Evaluation Strategy for Evolutionary Algorithms. Applied Soft Computing 2(3), 156–173 (2003)
Salami, M.: Fast Evolutionary Algorithm for Evolvable Hardware. PhD Thesis, Swinburne University of Technology (2003)
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Hendtlass, T. (2009). Quantised Problem Spaces and the Particle Swarm Algorithm. In: Chiong, R., Dhakal, S. (eds) Natural Intelligence for Scheduling, Planning and Packing Problems. Studies in Computational Intelligence, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04039-9_7
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DOI: https://doi.org/10.1007/978-3-642-04039-9_7
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