Abstract
Charged particle swarm optimization (CPSO) is well suited to the dynamic search problem since inter-particle repulsion maintains population diversity and good tracking can be achieved with a simple algorithm. This work extends the application of CPSO to the dynamic problem by considering a bi-modal parabolic environment of high spatial and temporal severity. Two types of charged swarms and an adapted neutral swarm are compared for a number of different dynamic environments which include extreme ‘needle-in-the-haystack’ cases. The results suggest that charged swarms perform best in the extreme cases, but neutral swarms are better optimizers in milder environments.
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References
Kennedy J. and Eberhart, R.C.: Particle Swarm Optimization. Proc of the IEEE International Conference on Neural Networks IV (1995) 1942–1948
Eberhart R.C. and Shi Y.: Particle swarm optimization: Developments, applications and resources. Proc Congress on Evolutionary Computation (2001) 81–86
Saunders P.T.: An Introduction to Catastrophe Theory. Cambridge University Press (1980)
Angeline P.J.: Tracking extrema in dynamic environments. Proc Evolutionary Programming IV. (1998) 335–345
Bäck T.: On the behaviour of evolutionary algorithms in dynamic environments. Proc Int. Conf. on Evolutionary Computation. (1998) 446–451
Branke J.: Evolutionary algorithms for changing optimization problems. Proc Congress on Evolutionary Computation. (1999) 1875–1882
Eberhart R.C. and Shi Y.: Tracking and optimizing dynamic systems with particle swarms. Proc Congress on Evolutionary Computation. (2001) 94–97
Carlisle A. and Dozier G.: Adapting particle swarm optimization to dynamic environments. Proc of Int Conference on Artificial Intelligence. (2000) 429–434
Blackwell and Bentley P.J.: Dynamic search with charged swarms. Proc Genetic and Evolutionary Computation Conference. (2002) 19–26
Blackwell and Bentley P.J.: Don’t push me! Collision avoiding swarms. Proc Congress on Evolutionary Computation. (2002) 1691–1696
Hu X. and Eberhart R.C.: Adaptive particle swarm optimization: detection and response to dynamic systems. Proc Congress on Evolutionary Computation. (2002) 1666–1670
De Jong K: An analysis of the behavior of a class of genetic adaptive systems. PhD thesis, University of Michigan (1975)
Clerc M.: The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. Proc Congress on Evolutionary Computation. (1999) 1951–1957
Blackwell and Bentley P.J.: Improvised Music with Swarms, Proc Congress on Evolutionary Computation. (2002) 1462–1467
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Blackwell, T.M. (2003). Swarms in Dynamic Environments. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_1
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DOI: https://doi.org/10.1007/3-540-45105-6_1
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