Application on job-shop scheduling with genetic algorithm based on the mized strategy
Adaptive Genetic Algorithm for solving job-shop scheduling problems has the defects of the slow convergence speed on the early stage and it is easy to trap into local optimal solutions, this paper introduces a time operator depending on the time evolution to solve this problem. Its purpose is to overcome the defect of adaptive Genetic Algorithm whose crossover and mutation probability can not make a corresponding adjustment with evolutionary process. Algorithms structure is hierarchical, scheduling problems can be fully demonstrated the characteristics by using this strategy, not only improve the convergence rate but also maintain the diversity of the population, furthermore avoid premature. The population in the same layer evolve with two goals-time optimal and cost optimal at the same time, the basic Genetic Algorithm is applied between layers. The improved algorithm was tested by Muth and Thompson benchmarks, the results show that the optimized algorithm is highly efficient and improves both the quality of solutions and speed of convergence.
Adaptive Time operator Hierarchic structure
Liang Xu Wang Shuang Huang Ming
Software Technology Institute, Dalian Jiao Tong University, Dalian 116028
国际会议
2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)
广西桂林
英文
2007-2009
2009-06-17（万方平台首次上网日期，不代表论文的发表时间）