一种加权距离连续K中心选址问题求解方法

K中心选址作为一种经典问题,学者们提出了很多好的解决方法,但是对于加权距离连续K中心选址问题的研究一直没有很好的进展.本文针对连续K中心选址问题,以最小加权距离作为优化目标提出改进的粒子群优化算法(SA-PSO).本文将模拟退火机制引入PSO算法并且加入惯性权重等策略对算法进行改进,使得该算法可以更快收敛于全局最优.仿真实验结果表明,SA-PSO算法相比于GA算法和K-means算法,具有更强的稳定性,收敛速度更快,并且优化得到的加权距离更小.

Method for Weighted Distance Continuous K-center Facility Location Problem

K-center location is a classic problem. Scholars proposed many good solutions,but the weighted distance continuous K-center facility location problem was not well solved. In this paper,a new particle swarm optimization algorithm( SA-PSO) is proposed to construct a new model. The weighted distance from the service station to the demand node is minimized as the optimization target,and the deployment position of the service station is solved to solve the problem of weighted distance continuous K-center location. In the SAPSO,an improved strategy such as simulated annealing mechanism and inertia weight is introduced,which makes the algorithm not easy to fall into local optimum and accelerate the convergence speed. The simulation results show that compared with the traditional PSO algorithm,GA algorithm and K-means algorithm,the weighted distance obtained by SA-PSO algorithm is smaller. The algorithm is more stable and the convergence speed is faster.