A Novel PSO Algorithm for Traveling Salesman Problem Based on Dynamic Membrane System

Abstract

Membrane computing is a class of distributed parallel computing model. In this paper, we propose a novel evolutionary computation method based on dynamic active membrane system. First, an improved particle swarm optimization based on neighborhood searching of every particle that called NPSO is proposed. That is, instead of learning from Pbest and Gbest during the whole evolution, the proposed NPSO learns from Pbest and NPbest (the NPbest is selected by the Neighborhood Searching Based Learning Strategy) in the early stage to preserve swarm diversity. After the predefined number of iterations, the NPSO switches into the conventional global version PSO to accelerate convergence speed. Second, in order to avoid suffering from premature convergence in the early stage, NPSO is partitioned into two stages that in the first stage is to preserve swarm diversity and in the second stage is to enhance the convergence speed towards global optimum. The classic Traveling Salesman Problem (TSP) is one of the most significant stochastic routing problems so we use the proposed NPSO to solve it. In fact, the NPSO can achieve better balance between exploration and exploitation as well. Experimental results show that the proposed NPSO algorithm is more superior or competitive.