Abstract：In order to overcome the shortcomings that the artificial fish swarm algorithm (AFSA) traps into local optima easily and has slow computational speed and low convergence accuracy, an improved adaptive AFSA algorithm based on differential factor and membrane computing (MC) is proposed. The algorithm keeps the diversity of fish swarm and overcomes the problem of trapping into local optima easily by using the framework of MC and rules. In addition, the algorithm simplifies prey behavior and enhances the performance of speed and accuracy by using differential factor to adjust visual, step, delta and attempts. Experimental results show that this algorithm can improve the calculation efficiency and accuracy effectively.
