基于群智能算法的玻璃切割问题求解研究

Study on Glass-block Cutting Problem Solving Based on Swarm Intelligent Algorithm

通过对玻璃切割问题的研究,提出一种融合量子粒子群优化和蚁群优化的混合算法(QPSO-ACO算法)。该算法对QPSO及ACO的模型进行必要的修改,以实现对玻璃切割中的旅行商问题的较好求解。同时充分利用QPSO的快速性、全局收敛性和ACO的正反馈性及求精解效率高等特点,达到优势互补。实验结果表明,QPSO-ACO算法寻优能力较强,是解决玻璃切割问题的有效方法。

Through the study on the glass-block cutting problem,a new hybrid algorithm of Quantum-behaved Particle Swarm Optimization and Ant Colony Optimization（QPSO-ACO algorithm） is proposed.The algorithm modifies the model of QPSO and ACO to solve Traveling Salesman Problem（TSP） in glass-block cutting.It makes full use of the positive feedback mechanism and high solution efficiency of ACO,as well as the fast convergence of QPSO.Experimental results show that QPSO-ACO algorithm has stronger optimization ability in solving the glass-block cutting problem.