An Artificial Fish Swarm Algorithm Based on Chaos Search
Hai Ma, Yanjiang Wang · 2009
Artificial fish swarm algorithm is a new random optimization algorithm based on simulation of fish swarm behavior. Preliminary study shows that it has many features such as good global convergence and high convergence speed. However, it may be trapped in local optimum in the later evolution period and it has the low search accuracy. An artificial fish swarm algorithm based on chaos search is proposed, which can not only overcome the disadvantage of easily getting into the local optimum in the later evolution period, but also keep the rapidity of the previous period. Finally, the basic artificial fish swarm algorithm is compared with this method using four benchmark test functions. The experiment results demonstrate that the new algorithm proposed is better than the basic artificial fish swarm algorithm in the aspects of convergence and stability.