标准量子行为的粒子群优化(Quantum-behaved particle swarm optimization,QPSO)算法依然存在早熟收敛的缺点,针对此问题,提出了一种改进的量子粒子群算法(Particle swarm optimization based on quantum,PSO-Q)。在PSO-Q算法中,采用分...标准量子行为的粒子群优化(Quantum-behaved particle swarm optimization,QPSO)算法依然存在早熟收敛的缺点,针对此问题,提出了一种改进的量子粒子群算法(Particle swarm optimization based on quantum,PSO-Q)。在PSO-Q算法中,采用分组策略基于不同的更新公式同时提高局部搜索和全局搜索能力,并且共享组间有用的信息,达到探索与开发能力的平衡。在不降低搜索精度的情况下,分组策略扩大了种群搜索过程中的搜索范围,其中一组保持QPSO搜索方法的基本搜索能力,主要开发已有搜索空间。另外一组共享整个群里的有效信息,增加新领域探索能力,可以避免种群多样性的不断下降。在标准测试函数的对比实验中,仿真结果表明该算法具有较强的搜索能力并且达到了较高的优化精度。展开更多
Computer-aided process planning （CAPP） is an essential component of computer integrated manufacturing （CIM） system. A good process plan can be obtained by optimizing two elements, namely, operation sequence and th...Computer-aided process planning （CAPP） is an essential component of computer integrated manufacturing （CIM） system. A good process plan can be obtained by optimizing two elements, namely, operation sequence and the machining parameters of machine, tool and tool access direction （TAD） for each operation. This paper proposes a novel optimization strategy for process planning that considers different dimensions of the problem in parallel. A multi-dimensional tabu search （MDTS） algo-rithm based on this strategy is developed to optimize the four dimensions of a process plan, namely, operation sequence （OperSeq）, machine sequence （MacSeq）, tool sequence （TooISeq） and tool approach direction sequence （TADSeq）, sequentially and iteratively. In order to improve its efficiency and stability, tabu search, which is incorporated into the proposed MDTS al- gorithm, is used to optimize each component of a process plan, and some neighbourhood strategies for different components are presented for this tabu search algorithm. The proposed MDTS algorithm is employed to test four parts with different numbers of operations taken from the literature and compared with the existing algorithms like genetic algorithm （GA）, simulated annealing （SA）, tabu search （TS） and particle swarm optimization （PSO）. Experimental results show that the developed algo-rithm outperforms these algorithms in terms of solution quality and efficiency.展开更多
文摘标准量子行为的粒子群优化(Quantum-behaved particle swarm optimization,QPSO)算法依然存在早熟收敛的缺点,针对此问题,提出了一种改进的量子粒子群算法(Particle swarm optimization based on quantum,PSO-Q)。在PSO-Q算法中,采用分组策略基于不同的更新公式同时提高局部搜索和全局搜索能力,并且共享组间有用的信息,达到探索与开发能力的平衡。在不降低搜索精度的情况下,分组策略扩大了种群搜索过程中的搜索范围,其中一组保持QPSO搜索方法的基本搜索能力,主要开发已有搜索空间。另外一组共享整个群里的有效信息,增加新领域探索能力,可以避免种群多样性的不断下降。在标准测试函数的对比实验中,仿真结果表明该算法具有较强的搜索能力并且达到了较高的优化精度。
基金supported by the State Key Program of National Natural Science Foundation of China (Grant No. 51035001)National Natural Science Foundation of China (Grant Nos. 50825503, 50875101)
文摘Computer-aided process planning （CAPP） is an essential component of computer integrated manufacturing （CIM） system. A good process plan can be obtained by optimizing two elements, namely, operation sequence and the machining parameters of machine, tool and tool access direction （TAD） for each operation. This paper proposes a novel optimization strategy for process planning that considers different dimensions of the problem in parallel. A multi-dimensional tabu search （MDTS） algo-rithm based on this strategy is developed to optimize the four dimensions of a process plan, namely, operation sequence （OperSeq）, machine sequence （MacSeq）, tool sequence （TooISeq） and tool approach direction sequence （TADSeq）, sequentially and iteratively. In order to improve its efficiency and stability, tabu search, which is incorporated into the proposed MDTS al- gorithm, is used to optimize each component of a process plan, and some neighbourhood strategies for different components are presented for this tabu search algorithm. The proposed MDTS algorithm is employed to test four parts with different numbers of operations taken from the literature and compared with the existing algorithms like genetic algorithm （GA）, simulated annealing （SA）, tabu search （TS） and particle swarm optimization （PSO）. Experimental results show that the developed algo-rithm outperforms these algorithms in terms of solution quality and efficiency.
