求解旅行商问题的混合粒子群优化算法

摘要

A new hybrid intelligent optimization was given to solve the traveling salesman problem (TSP) by introducing the thought of an LRO algorithm, chaos optimization algorithm, and particle swarm optimization (PSO). A group of optimal initial values were found by using the features of LRO. Next, by employing the method of discrete chaotic particle swarm optimization and introducing the swap operator, swap sequence, and chaos sequence, an optical chaos PSO adaptive for the TSP problem was proposed. The stability and convergence of the optimization was proved decisively. The numerical simulation results show that this new optimization method has a good convergence rate and less iterative steps, thus allowing a satisfactory solution to be found rapidly. The method provides a new inspiration for solving the TSP problem.%为高效解决旅行商问题,结合光学寻优算法、混沌优化算法、粒子群优化算法,提出了一种新的混合智能优化算法,应用光学寻优算法的优点,为粒子群中粒子找到了一组最优的初始值,引入交换子、交换序列、混沌序列,提出了适合旅行商问题的光学混沌粒子群算——并严格证明了新算法的稳定性、收敛性.数值实验仿真结果表明,该算法收敛速度快、迭代次数少,能快速找到令人满意的最优解,为解决旅行商问题提供了新的思路.