摘要
量子蚁群算法将量子理论与传统的蚁群算法结合,是一种高效的生物进化算法,已经广泛应用到诸多领域,但算法在寻优过程中仍然普遍存在陷入局部最优解的问题。针对量子蚁群算法的不足,通过引入粒子群的学习模式来改进算法,使得种群在进化过程中有更多的可能性,避免算法早熟收敛。将提出的新型改进量子蚁群算法应用于传统TSP实验,实验结果表明改进算法对问题求解效果较好,对量子蚁群算法的性能有一定的提高。
Quantum ant colony algorithm combines quantum theory with traditional ant colony algorithm,which is an efficient biological evolutionary algorithm.It has been widely used in many fields,but the algorithm still has the problem of falling into the local optimal solution in the process of optimization.In view of the deficiency of the quantum ant colony algorithm,the particle swarm learning model is introduced to improve the algorithm,so that the population has more possibilities in the evolutionary process,and avoid premature convergence.The new improved quantum ant colony algorithm is applied to the traditional TSP experiment.The experimental results show that the improved algorithm has better effect on the problem,and the performance of the quantum ant colony algorithm is improved.
出处
《电脑知识与技术（过刊）》
2017年第12X期214-216,共3页
Computer Knowledge and Technology
关键词
量子技术
蚁群算法
粒子群
TSP
智能优化
Quantum technology
Ant Colony Algorithm
Particle swarm
TSP
Intelligent optimization
