Abstract
Swarm Intelligence algorithms have been extensively applied to solve optimization problems. However, some of them, such as Particle Swarm Optimization, may not present the ability to generate diversity after environmental changes. In this paper we propose a hybrid algorithm to overcome this problem by applying a very interesting feature of the Fish School Search algorithm to the Particle Swarm Optimization algorithm, the collective volitive operator. We demonstrated that our proposal presents a better performance when compared to the FSS algorithm and some PSO variations in dynamic environments.
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Cavalcanti-Júnior, G.M., Bastos-Filho, C.J.A., Lima-Neto, F.B., Castro, R.M.C.S. (2011). A Hybrid Algorithm Based on Fish School Search and Particle Swarm Optimization for Dynamic Problems. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_67
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DOI: https://doi.org/10.1007/978-3-642-21524-7_67
Publisher Name: Springer, Berlin, Heidelberg
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