基于群智能算法的玻璃切割问题求解研究

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。

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

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.