远邻粒子群算法及在Delta机器人优化设计中的应用

 为了提高粒子群算法全局寻优能力，提出一种远邻粒子群算法，该算法引入邻域算子概念，每个粒子选择与自身欧氏距离较远的粒子建立邻域，邻域中粒子的数目用邻域算子表示.测试函数实验结果表明，该算法在一定程度上消除了标准粒子群算法容易陷入局部最优的缺点.应用远邻粒子群算法对Delta机器人进行优化设计，结果证实：所提出的远邻粒子群算法较标准粒子群算法具有更好的寻优能力，比邻居递增粒子群算法搜索精度更高.

Distant neighborhood PSO algorithm and its application on the optimization design of the Delta robot

In order to improve the global search capability, a distant neighborhood PSO was presented. In the algorithm, each particle had its own neighborhood by selecting particles far away from it in Euclidean distance. Neighborhood operator was adopted to control the number of particles in its neighborhood. Experimental results of benchmark functions indicated that the algorithm eliminated the standard PSO's weakness of easily converging to local optimal value. The distant neighborhood PSO was applied to optimization design of Delta robot. It turns out that the distant neighborhood PSO has better search capability than the standard PSO and higher search precision than neighbors increasing PSO.