基于混合遗传算法的并行多处理器系统的任务调度(英文)

A Hybrid Genetic Algorithm for Tasks Scheduling in Parallel Multiprocessor Systems

针对并行多处理器系统的任务调度问题,提出一个新的混合遗传算法(HGA).HGA使用拓扑排序表的交叉来保证下代的合法性和搜索空间的全局性,为了提高HGA的收敛速度,根据拉马克进化理论,在交叉算子中使用贪婪策略来提高个体的适应值,模拟结果显示HGA的调度结果和算法的时间复杂性都是令人满意的.

It presents a new hybrid genetic algorithm (HGA) to solve the tasks scheduling problem in parallel multiprocessor systems. It uses the crossover of topological sort list to guarantee that each offspring is a feasible solution and the search space is a global one. In order to improve the convergence of GA, it uses greedy strategy to improve the fitness of one chromosome in the population in crossover operator, based on Lamarckian theory in the evolution. The simulation results show that the HGA produces encouraging results in terms of quality of solution and time complexity.