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提出了一种基于冯·诺依曼邻域结构的人工鱼群算法.每条人工鱼只和与自己相连的上下左右的人工鱼进行信息交换,从而减少了计算邻域中心位置和极值位置的计算量,有效地维持了种群的多样性,加快了算法的运行速度.在觅食行为中,人工鱼通过直接移动到搜索到的较好位置,来加快搜索速度.在随机游动行为中,人工鱼以小半径进行搜索,因此算法的优化精度得到了提高.采用动态调整人工鱼视野和步长的方法,较好地平衡了全局搜索能力和局部搜索能力.仿真和实例计算结果表明,该算法具有更好的优化性能.
An artificial fish swarm algorithm based on von Neumann neighborhood structure is proposed, in which each artificial fish exchanges information only with the upper and lower artificial fish that are connected with itself, thus reducing the calculation of the center and extreme positions of the neighborhood , Which effectively maintains the diversity of the population and accelerates the speed of the algorithm.In the foraging behavior, the artificial fish speed up the search speed by moving directly to the better searched position.In the random walk behavior , Artificial fish search with a small radius, so the optimization accuracy of the algorithm has been improved.Using the method of dynamically adjusting the fish field of view and the step size, the global search ability and the local search ability are well balanced.The simulation and example calculation results show that, The algorithm has better optimization performance.