Study of a Self-Escape Hybrid Discrete Particle Swarm Optimization for TSP
求解TSP问题的自逃逸混合离散粒子群算法研究

To deal with the problem of premature convergence and slow search speed,a new algorithm which named the discrete particle swarm optimization algorithm(DPSO)has been proposed based on redefining speed and position of the DPSO,for solving the symmetrical traveling salesman problem(TSP)in this paper.We change the algorithm to self-escape hybrid discrete particle swarm optimization(SEHDPSO)after combining a strategy called self-escape method and local search method.The SEHDPSO uses to explore the global minima thoroughly,which derives from the phenomena that some organisms can escape dynamically from the original cradle when they find the survival density is too high to live.The subsequent experiment result shows that the SEHDPSO can not only speed up the convergence significantly but also solve the premature problem effectively.