Abstract
Both GA and PSO are typical evolution algorithm with their own advantages. In this paper, a new evolution algorithm is introduced based on GA and PSO. The total population are divided into several tribes, which one Hero individual and several common particles are included in each tribe. The movement velocity of each hero is calculated by global peak point and local best point among this tribe just like PSO. Other common particles will search neighbourhood of hero using recombination method of GA. Hero algorithm will converge fast and escape from local peak inheriting advantages of GA and PSO. These conclusions are proven from experiment of some familiar Benchmark functions.
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REFERENCES
J. Kennedy and R.C. Eberhart (1995), Particle swarm optimization. In: Proceedings of the 1995 IEEE International Conference on Neural Networks, 4, pp. 1942–1948. IEEE Press.
Y. Shi and R.C. Eberhart (1998), A modified Particle Swarm Optimiser, IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, May 4–9.
M. Clerc (1999), The swarm and the queen: towards a deterministic and adaptive particle swarm optimisation. In: Proceedings of the 1999 Congress of Evolutionary Computation, 3, pp. 1951–1957. IEEE Press.
P.J. Angeline (1998), Using selection to improve particle swarm optimization. In 1998 IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, USA, pp. 84–89.
M. Lovbjerg, T.K. Rasmussen, and T. Krink (2001), Hybrid particle swarm optimiser with breeding and subpopulations. In: Proceedings of the Third Genetic and Evolutionary Computation Conference 2001, San Francisco, USA.
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Guo, D., Zhou, C., Liu, M. (2006). A HERO EVOLUTIONARY ALGORITHM HYBRIDIZING FROM PSO AND GA. In: LIU, G., TAN, V., HAN, X. (eds) Computational Methods. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-3953-9_10
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DOI: https://doi.org/10.1007/978-1-4020-3953-9_10
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-3952-2
Online ISBN: 978-1-4020-3953-9
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