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计算机工程 ›› 2010, Vol. 36 ›› Issue (15): 171-173. doi: 10.3969/j.issn.1000-3428.2010.15.060

• 人工智能及识别技术 • 上一篇    下一篇

基于群智能算法的玻璃切割问题求解研究

毛 力,童 科,沈明明,董洪伟   

  1. (江南大学信息工程学院,无锡 214122)
  • 出版日期:2010-08-05 发布日期:2010-08-25
  • 作者简介:毛 力(1967-),男,副教授、硕士,主研方向:人工智能,数据挖掘;童 科、沈明明,硕士研究生;董洪伟,副教授、博士
  • 基金资助:
    江苏省高校高技术产业化基金资助项目“全自动数控玻璃切割系统”(JHB05-31)

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

MAO Li , TONG Ke, SHEN Ming-ming, DONG Hong-wei   

  1. (School of Information Technology, Jiangnan University, Wuxi 214122)
  • Online:2010-08-05 Published:2010-08-25

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)

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