Abstract
The balance of exploration and exploitation in particle swarm optimisation is closely related to the choice of the algorithm’s parameters. Achieving the right balance is essential for the success of a given optimisation task. This choice is a difficult task, since for different functions being optimised the ideal parameter sets can also bee very different. In this paper we try to deal with this issue by introducing two new mechanisms in the basic particle swarm optimiser: a predator-prey strategy to help maintain diversity in the swarm and a symbiosis based adaptive scheme to allow the co-evolution of the algorithm parameters and the parameters of the function being optimised.
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References
Kennedy, J., Eberhart, R.C.: Particle swarm optimisation. In: Proc. IEEE International Conference on Neural Networks, Piscataway, NJ, pp. 1942–1948 (1995)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Angeline, P.J.: Evolutionary optimisation versus particle swarm optimisation: philosophy and performance differences. In: The Seventh Annual Conf. on Evolutionary Programming (1998)
Shi, Y., Eberhart, R.C.: Empirical study of particle swarm optimisation. In: Proceedings of the 1999 Congress on Evolutionary Computation, Piscataway, NJ, pp. 1945–1950 (1999)
Clerc, M., Kennedy, J.: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)
Kennedy, J.: Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proc. Congress on Evolutionary Computation 1999, Piscataway, NJ, pp. 1931–1938 (1999)
Blackwell, T., Bentley, P.J.: Don’t Push Me! Collision-Avoiding Swarms. In: Proc. of the Congress on Evolutionary Computation 2002 (2002)
Muhlenbein, H., Schlierkamp-Voosen, D.: Predictive models for the breeder genetic algorithm: I. Continuous parameter optimisation. Evolutionary Computation 1(1), 25–49
Silva, A., Neves, A., Costa, E.: Chasing the Swarm: A Predator Prey Approach to Function Optimisation. In: Proc. of MENDEL 2002 – 8th International Conference on Soft Computing, Brno, Czech Republic, June 5-7 (2002)
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Silva, A., Neves, A., Costa, E. (2003). SAPPO: A Simple, Adaptable, Predator Prey Optimiser. In: Pires, F.M., Abreu, S. (eds) Progress in Artificial Intelligence. EPIA 2003. Lecture Notes in Computer Science(), vol 2902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24580-3_14
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DOI: https://doi.org/10.1007/978-3-540-24580-3_14
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