粒子群算法试算

  粒子群算法，也称粒子群优化算法（Particle Swarm Optimization），缩写为 PSO， 是近年来发展起来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), also called particle swarm optimization (Particle Swarm Optimization), is abbreviated as PSO, which is a new evolutionary algorithm (Evolutionary Algorithm EA) developed in recent years. PSO algorithm is a kind of evolutionary algorithm. It is similar to simulated annealing algorithm. It also starts from the random solution and finds the optimal solution by iteration. It also evaluates the quality of the solution through fitness, but it is simpler than the genetic algorithm rule. It does not have the crossover (Crossover) and the Mutation operation of genetic algorithm. Search for global optimum. This algorithm has attracted the attention of the academic community for its advantages of easy realization, high accuracy and fast convergence, and has shown its superiority in solving practical problems. Particle swarm optimization (PSO) is a parallel algorithm.)