摘要: 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。
关键词:
群智能算法,
量子粒子群优化,
蚁群优化,
玻璃切割,
旅行商问题
Abstract: 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.
Key words:
swarm intelligent algorithm,
Quantum-behaved Particle Swarm Optimization(QPSO),
Ant Colony Optimization(ACO),
glass-block cutting,
Traveling Salesman Problem(TSP)
中图分类号:
毛力, 童科, 沈明明, 董洪伟. 基于群智能算法的玻璃切割问题求解研究[J]. 计算机工程, 2010, 36(15): 171-173.
MAO Li, TONG Ke, CHEN Meng-Meng, DONG Hong-Wei. Study on Glass-block Cutting Problem Solving Based on Swarm Intelligent Algorithm[J]. Computer Engineering, 2010, 36(15): 171-173.