Solving traveling salesman problem based on improved particle swarm optimization algorithm

An improved particle swarm optimization (IPSO) algorithm is proposed to solve a typical combinatorial optimization problem: traveling salesman problem, which is a well-known NP-complete problem. In the improved algorithm, particles not only adjust its own flying speed according to itself and the best individual of the swarm but also learn from other individuals according to certain probability. This kind of study behavior accords with the biological natural law even more, and furthermore helps to find the global optimum solution. At the same time, this paper proposes the concepts of Adjustment Operator and Adjustment Sequence based on which particle swarm optimization (PSO) and IPSO algorithm were successfully rebuilt, according to the ideas of single node regulating algorithm. For solving traveling salesman problem, numerical simulation results show the effectiveness and efficiency of the proposed method.