Parallel Adaptive Artificial Fish Swarm Algorithm Based on Differential Evolution

Abstract

Artificial fish swarm algorithm (AFSA) is a newly proposed swarm intelligent optimization algorithm. It is proved to be a promising approach to complex engineering problems, yet still there exist some defects of this algorithm. To solve the problem that AFSA has a low rate of convenience, low optimization precision, premature convergence and poor ability of balancing exploitation and exploration, an improved artificial fish swarm algorithm (PAAFSA-DE) is proposed. This algorithm divides the population into two sub groups with the same size, and different adaptive strategies are applied to the two groups respectively to make one group focus on global search and the other on local search. The two sub populations evolve independently and individual migration are conducted regularly to achieve information communication, increase the population diversity and improve convergence rate of algorithm. When the information on the bulletin board does not change for a certain times, the differential evolution strategy will be introduced to make the algorithm escape from local extreme. The comparing simulation results on the benchmark function optimization problems demonstrate that the improved algorithm is feasible and effective. It performs better than basic AFSA, the balance ability of exploitation and exploration is enhanced, and convergence efficiency and optimization precision are improved greatly as well as the stability is strengthened.