引入萤火虫行为和Levy飞行的粒子群优化算法

粒子群优化（PSO）算法具有易陷入局部最小值和全局搜索能力差的缺陷，对PSO算法的改进大多只是在某一方面利用单一搜索策略进行改进，针对这种改进策略不能全面优化PSO算法性能的问题，提出一种引入萤火虫行为和Levy飞行的粒子群优化（FBLFPSO）算法。根据改进的自调节步长的萤火虫搜索策略改善PSO的局部搜索能力，避免PSO陷入局部最小值；后期利用Levy飞行策略增强种群多样性，提高PSO全局搜索能力，跳出局部最优解。仿真实验结果表明，与现有相关算法相比，FBLFPSO的全局搜索能力和搜索精度都有较大提高。

Particle swarm optimization algorithm with firefly behavior and Levy flight

Particle Swarm Optimization (PSO) is easy to fall into local minimum, and has poor global search ability. Many improved algorithms cannot optimize PSO performance fully by using a single search strategy in a way. In order to solve the problem, a novel PSO with Firefly Behavior and Levy Flight (FBLFPSO) was proposed. The local search ability of PSO was improved to avoid falling into local optimum by using improved self-regulating step firefly search strategy. Then, the principle of Levy flight was taken to enhance population diversity and improve the global search ability of PSO, which contributed to escape from local optimal solution. The simulation results show that, compared with the existing correlation algorithms, the global search ability and the search accuracy of FBLFPSO are greatly improved.