引入Lévy flight和萤火虫行为的鱼群算法
Fish swarm algorithm with Lévy flight and firefly behavior

针对人工鱼群算法(artificial fish-swarm algorithm,AFSA)和萤火虫算法(FA)在多维多极值函数寻优过程中易陷入局部最优和精度有待提 高等问题，提出基于萤火虫行为和Lévy flight 的鱼群算法(LFFSA)。该算法将萤火虫算法(firefly algorithm,FA)中萤火虫个体的移 动策略引入到鱼群的聚群、觅食两种行为模式中，进而将Lévy flight 引入到鱼群的搜索策略中，使得鱼群的搜索 更加高效。此外，采取一种基于动态参数的非线性变视野和变步长来限定鱼群的搜索范围。仿真分析表 明，LFFSA 较两种基本算法具有更好的全局搜索能力和寻优精度。
Since the artificial fish-swarm algorithm(AFSA) and firefly algorithm(FA) are easily converging to local optimum and have low accuracy in the optimization process for solving multi-dimensional and multi-extreme value functions, an algorithm called Fish swarm algorithm with firefly behavior and Levy flight(LFFSA) is proposed, which introduces the migration strategy of firefly algorithm into the two behavior patterns of fish swarm as:the swarming and the preying behaviors. Furthermore, the Lévy flight is introduced into the search strategy. Besides, nonlinearity visual and step length based on dynamic parameter are simultaneously considered for limiting the search band. Simulation results demonstrate that the LFFSA has a better performance in convergence speed and optimization accuracy.