中文说明：粒子群算法，也称粒子群优化算法（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。
English Description：
Particle swarm optimization, also known as particle swarm optimization, abbreviated as PSO, is a new 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, it also starts from the random solution and finds the optimal solution through iteration. It also evaluates the quality of the solution through fitness, but it is simpler than genetic algorithm rules. It does not have the "crossover" and "mutation" of genetic algorithm Operation, which finds the global optimum by following the current optimal value. This algorithm is easy to implement, high accuracy, fast convergence and so on, which has attracted the attention of the academic community, and shows its superiority in solving practical problems. Particle swarm optimization is a parallel algorithm.