基于自适应动态邻域结构的人工鱼群算法

针对人工鱼群算法易陷入局部最优且寻优精度不高的问题，提出了一种基于自适应动态邻域结构的人工鱼群算法。算法中，每条人工鱼先根据鱼群中其他人工鱼与自身的距离及当前迭代次数自适应调整动态邻域结构，再根据该动态邻域结构自适应计算视野和步长；还结合粒子群算法信息策略和公告板对人工鱼的行为进行了改进。仿真实验结果表明，该算法克服局部极值实现全局寻优的能力更强，优化精度更高。

Artificial fish swarm algorithm based on adaptive dynamic neighborhood structure

Aiming at the problems of easily falling into local optimum and low optimization precision in the Artificial Fish Swarm Algorithm（AFSA）, an Artificial Fish Swarm Algorithm based on Adaptive Dynamic Neighborhood Structure（ADAFSA） is proposed. In the algorithm, the dynamic neighbors of each artificial fish are adaptively constructed according to the distance between the fish with the others and the current iteration, then the visual and the step of each artificial can be adaptively calculated with the dynamic neighborhood. The algorithm also adds information strategy of Particle Swarm Optimization（PSO） and billboard to improve behaviors of artificial fish. The simulation results show that the ability of the proposed algorithm overcomes the local optimum to achieve stronger global optimization, and optimization precision is higher.