Artificial Fish Swarm Algorithm Based on Tabu Search

Abstract

In this paper, through the analysis of artificial fish swarm algorithm, the algorithm is effectively improved. Tabu search is added into the artificial fish swarm algorithm, and Tabu search table is set, so that the artificial fish swarm algorithm can effectively avoid falling into the local optimization when searching for the optimal solution, which speeds up the later convergence speed and improves the performance of the algorithm. The simulation results show that compared with Tabu search algorithm and artificial fish swarm algorithm, the improved artificial fish swarm algorithm has obvious improvement in convergence speed, calculation accuracy and jumping out of local optimal ability.