An Improved Artificial Fish-School Algorithm Based on Group Behaviors and Self-Adaptive Mutation

Yang Tao; Wei Han

doi:10.4028/www.scientific.net/AMM.462-463.360

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 462-463An Improved Artificial Fish-School Algorithm Based...

An Improved Artificial Fish-School Algorithm Based on Group Behaviors and Self-Adaptive Mutation

Abstract:

According to the shortages of the standard Artificial Fish-school Algorithm (AFSA), this paper improves the behaviors of the fish-school by considering the group behaviors of neighborhood sensing effects, leader mode and Glowworm Swarm Optimization Algorithm (GSOA). Combining the self-adaptive step and vision, Gaussian mutation, Genetic Algorithm (GA), a new AFSA based on self-adaptive mutation operation is raised. By using the validation functions to test, the results show that the rate of convergence, optimization precision and the ability to avoid precocious phenomenon of the new algorithm are much better than the standard one.

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Periodical:

Applied Mechanics and Materials (Volumes 462-463)

Pages:

360-364

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.462-463.360

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Cite this paper

Online since:

November 2013

Authors:

Yang Tao*, Wei Han

Keywords:

AFSA, Leader Mode, Mutation Operation, Neighborhood Sensing Effects

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* - Corresponding Author

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