粒子群复形法求解旅行商问题

针对众多领域的组合优化问题可转化为旅行商问题（TSP），提出求解TSP的粒子群复形（CPSO）算法.该算法在迭代的每一步，都将全部点根据适应值进行排序，让好点与差点进行两两配对.根据配对的两点连线中点的适应值与好点的适应值的比值，确定在连线的某位置取出一点.将取出的点与差点和整体最优点的差值点进行线性组合, 所得到的新点取代当前两点中的差点.对TSP解序列提出5种运算, 得到能求解TSP的CPSO算法.并求解了14个点的TSP问题与印刷电路板（PCB）数控钻走刀路线优化问题.结果表明，与遗传算法和蚁群算法相比，该算法具有更强的搜索性能和更好的稳定性，收敛速度更快.

Complex particle swarm optimization algorithm for solving travelling salesman problem

Aimed at many fields' combinational optimization problems being transformed into travelling salesman problem(TSP),a complex particle swarm optimization(CPSO) algorithm was put forward to solve TSP.All points were arranged according to the fitness value in each iterative step of the algorithm,and the good points and the bad points were made pairs respectively.One place of line was confirmed by the ratio of midpoint fitness and good point fitness values in a pair points' line,and one point was selected from the place.The selected point and the difference point of bad point and best point were linearly combined,and a new point was formed to replace the bad point.Five operations for solution sequence of TSP were posed,and CPSO for solving TSP was obtained.The problems of 14 points TSP and optimal moving path for printed circuit board(PCB) were solved.Results show that compared with genetic algorithm and ant colony optimization,the proposed algorithm has more powerful search property,more stable and converges faster.