求解柔性作业车间调度的动态群智能优化算法

A Dynamic Swarm Intelligence Optimization Algorithm based on Flexible Job-shop Scheduling Problem

针对柔性作业车间调度问题,提出一种新型两阶段动态混合群智能优化算法.算法初始阶段采用动态邻域的协同粒子群进行粗搜索,第二阶段提出了基于混沌算子的蜂群进行细搜索,既增强了种群多样性,又提高了算法搜索精度,实现了全局搜索与局部搜索能力的有效平衡.针对柔性作业车间调度问题特点,采用独特的编码方式和位置更新策略来避免不合法解的产生.最后将此算法在不同规模的实例上进行了仿真测试,并与最近提出的其他几种具有代表性的算法进行了比较,验证了算法的有效性和优越性.

A novel dynamic hybrid swarm intelligence optimization algorithm is presented for flexible Job-shop Scheduling Problem, in the first stage, we make use of the dynamic multi- swarms to maintain the population diversity, in the second stage, and we make use of the chaotic operator in ABC algorithm to improve the search precision. This algorithm apply to scheduling problem directly with the novel coding mode and location updating strategy, The simulation results of some classical Job-Shop scheduling problems and instance demonstrated that the proposed algorithm could effectively overcome the early-maturing and improve global search capability, Comparing to other algorithms obtained by the proposed algorithm was better. the optimal solution or near optimal solution