A Modified Particle Swarm Optimization Algorithm and its Application For Solving Traveling Salesman Problem

A modified particle swarm optimization (MPSO) algorithm was proposed. In the modified algorithm, the cooperative mechanism among individuals has been introduced, namely, 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. Additionally, the mutation of velocity has been added according to the phenomena of the bird flying off at a tangent in the nature. Finally, the tentative behavior is developed, according to the studying law of mankind. This kind of enhanced study behavior accords with the biological natural law even more, and helps to find the global optimum solution more easily. At the same time, concepts of adjustment operator and adjustment sequence were proposed, based on which the MPSO algorithm was successfully rebuilt to solve a typical combinatorial optimization problem: traveling salesman problem, which is a well-known NP-hard problem in the discrete domain. For solving traveling salesman problem, numerical simulation results for the benchmark TSP problems shows the effectiveness and efficiency of the proposed method