Genetic Quantum Particle Swarm Optimization Algorithm for Solving Traveling Salesman Problems

摘要

This paper presents a Genetic Quantum Particle Swarm Optimization (GQPSO) algorithm to solve Traveling Salesman Problems (TSPs). This algorithm is proposed based on the concepts of swap operator and swap sequence by introducing crossover, mutation and inverse operators in Genetic Algorithm (GA). Our algorithm overcomes such drawbacks as low convergence rate and local optimum when using Particle Swarm Optimization (PSO) or Quantum Particle Swarm Optimization (QPSO) algorithm to solve TSP. The experiment result shows that GQPSO algorithm has very powerful global search ability and its convergence rate is sharply accelerated compared to that of QPSO algorithm. GQPSO algorithm will have very good application prospects in solving combinational optimization problems.