论文部分内容阅读
流水车间调度是一类典型的生产调度问题,属于NP-难问题.针对传统的最优化方法难以求解大规模问题,提出了一个Memetic算法,在算法的局部搜索中使用一种新型的基于NEH的邻域结构,并且其邻域规模随着搜索的进行能够动态变化,可以大大提高算法的搜索能力.通过对标准Benchmark问题的测试,所得结果表明提出的基于新邻域结构的Memetic算法具有较好的性能,并且优于已有文献中的粒子群算法.
Flow shop scheduling is a typical production scheduling problem, which belongs to the NP-hard problem. Aim at the traditional optimization method is difficult to solve large-scale problems, a Memetic algorithm is proposed, and a new NEH-based Neighborhood structure and the size of its neighborhood can be dynamically changed as the search progresses, which can greatly improve the search ability of the algorithm.By testing the standard Benchmark problem, the results show that the proposed Memetic algorithm based on the new neighborhood structure has better performance Performance, and better than the existing literature particle swarm optimization.