粒子群优化算法求解最优路径

代码说明：

  粒子群优化最优路径算法，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(Similar to simulated annealing (SA), particle swarm optimization (PSO) optimizes the optimal path by iteratively searching for the optimal solution from a random solution. It also evaluates the quality of the solution by fitness, but it is simpler than the genetic algorithm rules. It does not have the crossover and Mutation operations of genetic algorithm. It finds the global optimum by following the best values currently searched.)

文件列表：

粒子群优化算法求解最优路径\Arrange.m, 256 , 2017-09-18
粒子群优化算法求解最优路径\a_FCM_3.m, 674 , 2017-09-20
粒子群优化算法求解最优路径\ceshide.m, 232 , 2009-08-12
粒子群优化算法求解最优路径\floyed.m, 927 , 2009-08-12
粒子群优化算法求解最优路径\GenerateChangeNums.m, 622 , 2017-09-18
粒子群优化算法求解最优路径\HoldByOdds.m, 157 , 2017-09-18
粒子群优化算法求解最优路径\liziqun.m, 3486 , 2017-09-18
粒子群优化算法求解最优路径\mtspf_ga.m, 6108 , 2009-09-27
粒子群优化算法求解最优路径\mtsp_ga.m, 6890 , 2017-09-21
粒子群优化算法求解最优路径\myLength.m, 123 , 2009-08-12
粒子群优化算法求解最优路径\PathDistance.m, 211 , 2017-09-18
粒子群优化算法求解最优路径\PathExchange.m, 415 , 2017-09-18
粒子群优化算法求解最优路径\PathPlot.m, 236 , 2017-09-18
粒子群优化算法求解最优路径, 0 , 2018-07-16