Abstract:

When artificial fish swarm algorithm is close to the optimal point during its optimization process, the convergence rate declines so that it is difficult to get exact solutions. Besides, the algorithm is easy to fall into local minima in complex issues. Aiming at the aforementioned disadvantages, a hybrid algorithm is proposed, which combines the compound chaotic search technology and the improved artificial fish swarm algorithm. It adopts the mapping combination with more ergodicity to generate the local search method. The method can avoid that artificial fish are into local extremum area for a long time, so that it reaches the global extreme points more precisely. Meanwhile, the artificial fish swarm algorithm is improved by introducing feedbackswallowed behavior of artificial fish. The improved algorithm reduces optimization complexity at late stage, improves accuracy and guarantees convergence efficiency. Experimental results show that, under the same parameter conditions, the proposed hybrid algorithm outperforms the basic artificial fish swarm algorithm in convergence rate, optimization accuracy and global optimization ability. Experiments demonstrate the efficiency of the proposed method.

Key words: artificial fish swarm algorithm;compound map;chaotic search;global optimization;feedback strategy;swallowed behavior