说明：  粒子群算法，也称粒子群优化算法或鸟群觅食算法（Particle Swarm Optimization），缩写为 PSO， 是近年来由J. Kennedy和R. C. Eberhart等开发的一种新的进化算法(Evolutionary Algorithm - EA)。PSO 算法属于进化算法的一种，和模拟退火算法相似，它也是从随机解出发，通过迭代寻找最优解，它也是通过适应度来评价解的品质，但它比遗传算法规则更为简单，它没有遗传算法的“交叉”(Crossover) 和“变异”(Mutation) 操作，它通过追随当前搜索到的最优值来寻找全局最优。这种算法以其实现容易、精度高、收敛快等优点引起了学术界的重视，并且在解决实际问题中展示了其优越性。粒子群算法是一种并行算法
(Particle Swarm Optimization (PSO) is a new evolutionary algorithm (Evolutionary Algorithm-EA) developed by J. Kennedy and R. C. Eberhart in recent years. PSO algorithm is a kind of evolutionary algorithm. Similar to simulated annealing algorithm, PSO algorithm starts from random solution and searches the optimal solution iteratively. It also evaluates the quality of solution by fitness, but it is simpler than genetic algorithm rules. It has no crossover and Mutation operation of genetic algorithm. It seeks global optimality by following the best values currently searched. This algorithm has attracted the attention of academia because of its easy implementation, high precision, fast convergence and other advantages, and has shown its superiority in solving practical problems. Particle swarm optimization is a parallel algorithm.)