Cooperative Artificial Fish Swarm Optimization

Basic Artificial Fish Swarm(AFS) Algorithm is a new type of heuristic swarm intelligence algorithm but optimization is difficult to get a very high precision due to the randomness of the artificial fish behavior.This paper presents an extended AFS algorithm, namely the Cooperative Artificial Fish Swarm (CAFS),which significantly improves the original AFS in solving complex optimization problems. In this work,firstly,CAFS algorithm is used for optimizing six widely-used benchmark functions and the comparative results produced by CAFS, Particle Swarm Optimization (PSO) are studied.Secondly,K-medoids and CAFS algorithm is used for data clustering on several benchmark data sets.The simulation results show that the proposed CAFS outperforms the other two algorithms in terms of accuracy,robustness and convergence speed.