Firefly Algorithm and Pattern Search Hybridized for Global Optimization
Firefly optimization algorithm is one of the latest swarm intelligence based optimization algorithm. A new hybrid optimization algorithm, which combines pattern search with firefly algorithm, namely FAPS, is proposed for numerical global optimization. There are two alternative phases of the proposed algorithm: the global exploration phase realized by firefly algorithm and the exploitation phase completed by pattern search. The performance of the proposed FAPS algorithm was tested on a comprehensive set of benchmark functions. The numerical experiments demonstrate that the new algorithm has high viability, accuracy and stability and the performance of firefly algorithm is much improved by introducing a pattern search method.
Keywordsglobal optimization firefly algorithm pattern search hybridization
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- 1.Holland, J.: Adaptation in natural and artificial systems. University of Michigan Press (1975)Google Scholar
- 2.Kennedy, J., Eberhart, R.: Particle swarm optimization. In: IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)Google Scholar
- 11.Yang, X.S.: Nature-inspired metaheuristic algorithms. Luniver Press, Beckington (2008)Google Scholar