Insertion-based Particle Swarm Optimization with local interaction

Insertion-based Particle Swarm Optimization (IPSO) is an optimization method for Traveling Salesman Problem (TSP) s. IPSO can find a better solution in a short time compared with Simulated Annealing (SA) and Genetic Algorithm (GA). In order to find a near optimal solution, we propose IPSOLB by improving IPSO. IPSOLB is based on the local version of PSO. In this method, a particle is affected by its neighbor particles instead of the whole population. As a result of this modification, agents are not converged immediately on the best solution obtained so far, and the population maintains various candidate solutions. For this reason, in comparison with IPSO, IPSOLB can be expected to search a solution while avoiding local optimum solutions. Through numerical experiments, it is shown that the proposed method can find a better solution than IPSO in reasonable time.