改进伪并行遗传算法求解作业车间调度问题

针对遗传算法在求解极复杂优化问题中出现的过早收敛、执行效率差的缺点,提出了一种改进的伪并行遗传算法.该算法将并行进化与串行搜索相结合,提高了算法的收敛速度.同时该算法通过种群因子控制伪并行算法中的各子种群的规模,不仅保证了搜索过程中勘探和开采的平衡,克服过早收敛,而且减少了计算的复杂性,特别是在处理复杂优化问题上具有较高的性能.实验结果证明了该算法的有效性.

An Improved Pseudo Parallel Genetic Algorithm for Solving Job-shop Scheduling Problems

An improved pseudo-parallel genetic algorithm(PPGA) was proposed to solve the premature convergence and inefficient implementation problems of optimizing problems.This algorithm unified the parallel evolution and the serial search,enhanced the algorithm convergence rate.Simultaneously each sub-population′s scale was controlled by the population parameter in the PPGA,not only guaranteed the balance of between the exploration and the mining,overcame prematurely restrains,but also reduced the computation complexity,specially had the high performance in the processing complex optimization question.The experimental result had proven this algorithm′ validity.