关键词: TSP, 量子蚁群算法, 改进型量子蚁群算法, 量子旋转门

Abstract: Against the disadvantages of being easy to fall into local optimum and slow convergence speed on solving Traveling Salesman Problem（TSP） with traditional quantum ant colony algorithm（QACA）, this paper proposes an Improved Quantum Ant Colony Algorithm（IQACA） on solving Traveling Salesman Problem. This algorithm designs a new strategy which is used to update pheromone volatilization factor adaptively, and it can update the pheromone dynamically. Furthermore a new quantum revolving door is adopted to change the convergence tend of quantum probability amplitude. Three basic function extremum optimization simulation compared with traditional quantum ant colony algorithm, proves that the paper’s algorithm has better performance. Experimental results based on TSP library（TSPLIB） show that both convergence rate and optimized global results are much improved compared with other algorithms, and it can avoid falling into local optimum effectively.

Key words: Traveling Salesman Problem（TSP）, Quantum Ant Colony Algorithm（QACA）, Improved Quantum Ant Colony Algorithm（IQACA）, quantum revolving door