趋优算子和Levy Flight混合的粒子群优化算法

针对Levy Flight粒子群优化算法（LFPSO）普适性不强和搜索效率不高等问题，提出了一种改进的LFPSO算法（ILFPSO），即趋优算子与Levy Flight混合的粒子群优化算法。首先，对Levy Flight进行改进，防止产生无效解，得到改进的Levy Flight；然后，将既有一定全局搜索能力又有较强局部搜索能力的趋优算子与改进的Levy Flight有机融合，以便更好地平衡算法的全局和局部搜索能力；最后，对速度边界动态调整，有利于搜索前期找到全局最优点和搜索后期找到局部最优解。28个benchmark函数优化仿真结果表明，与4种最先进的PSO改进算法LFPSO、ELPSO、SRPSO和RLPSO相比，ILFPSO更具有竞争性的优化性能、更好的普适性和更快的运行速度。

Particle Swarm Optimization Algorithm Based on Combining Global-Best Operator and Levy Flight

In order to enhance the optimization performance of the particle swarm optimization algorithm with Levy Flight (LFPSO), this paper proposes an improved LFPSO (ILFPSO), namely PSO based on combining the global-best operator and Levy Flight. First, the Levy Flight operator is accurately improved so that it can prevent the algorithm from generating invalid solutions, and an improved Levy Flight operator is obtained. Then because of the advantage of the global-best operator which has both some global and strong local search ability, this paper combines the global-best operator and improved Levy Flight in order to balance the global and local optimization ability of the algorithm. Finally, the velocity boundary is updated dynamically, which is helpful to find the global optimal solution in the early search stage and local optimal solutions in the later stage. 28 benchmark functions are used to evaluate the feasibility of ILFPSO. The experimental results show that, compared with 4 state-of-the-art PSO variants, such as LFPSO, ELPSO, SRPSO and RLPSO, ILFPSO obtains stronger competitive power, better universality and faster running speed.