This paper considers the single machine scheduling problem with uniform parallel machines in which the objective is to minimize the makespan. Four different GA based heuristics are designed by taking different combina...This paper considers the single machine scheduling problem with uniform parallel machines in which the objective is to minimize the makespan. Four different GA based heuristics are designed by taking different combinations of crossover methods, viz. single point crossover method and two point crossover method, and job allocation methods while generating initial population, viz. equal number of jobs allocation to machines and proportionate number of jobs allocation to machines based on machine speeds. A detailed experiment has been conducted by assuming three factors, viz. Problem size, crossover method and job allocation method on 135 problem sizes each with two replications generated randomly. Finally, it is suggested to use the GA based heuristic with single point crossover method, in which the proportionate number of jobs allocated to machines based on machine speeds.展开更多
A neurocomputing model for Genetic Algorithm (GA) to break the speed bottleneck of GA was proposed. With all genetic operations parallel implemented by NN-based sub-modules, the model integrates both the strongpoint o...A neurocomputing model for Genetic Algorithm (GA) to break the speed bottleneck of GA was proposed. With all genetic operations parallel implemented by NN-based sub-modules, the model integrates both the strongpoint of parallel GA (PGA) and those of hardware GA (HGA). Moreover a new crossover operator named universe crossover was also proposed to suit the NN-based realization. This model was tested with a benchmark function set, and the experimental results validated the potential of the neurocomputing model. The significance of this model means that HGA and PGA can be integrated and the inherent parallelism of GA can be explicitly and farthest realized, as a result, the optimization speed of GA will be accelerated by one or two magnitudes compered to the serial implementation with same speed hardware, and GA will be turned from an algorithm into a machine.展开更多
文摘This paper considers the single machine scheduling problem with uniform parallel machines in which the objective is to minimize the makespan. Four different GA based heuristics are designed by taking different combinations of crossover methods, viz. single point crossover method and two point crossover method, and job allocation methods while generating initial population, viz. equal number of jobs allocation to machines and proportionate number of jobs allocation to machines based on machine speeds. A detailed experiment has been conducted by assuming three factors, viz. Problem size, crossover method and job allocation method on 135 problem sizes each with two replications generated randomly. Finally, it is suggested to use the GA based heuristic with single point crossover method, in which the proportionate number of jobs allocated to machines based on machine speeds.
基金NationalNaturalScienceFoundationofChina (No .60 2 3 40 2 0 )
文摘A neurocomputing model for Genetic Algorithm (GA) to break the speed bottleneck of GA was proposed. With all genetic operations parallel implemented by NN-based sub-modules, the model integrates both the strongpoint of parallel GA (PGA) and those of hardware GA (HGA). Moreover a new crossover operator named universe crossover was also proposed to suit the NN-based realization. This model was tested with a benchmark function set, and the experimental results validated the potential of the neurocomputing model. The significance of this model means that HGA and PGA can be integrated and the inherent parallelism of GA can be explicitly and farthest realized, as a result, the optimization speed of GA will be accelerated by one or two magnitudes compered to the serial implementation with same speed hardware, and GA will be turned from an algorithm into a machine.
