猫群算法求解阻塞流水车间调度问题

阻塞流水车间调度是现实生产调度中一类很重要的组合优化问题,其已被证明是典型的NP难问题。为了提高该问题的求解性能,文中提出了猫群算法（CSO）求解阻塞流水线调度问题。猫群算法是近几年来提出的群体智能算法,算法以一小部分猫执行跟踪模式,其余大部分猫执行搜寻模式,通过这两种模式同时进行局部搜索和全局搜索以达到优化目标。文中利用标准Car问题算例进行仿真实验,并与标准粒子群算法（PSO）和蝙蝠算法（BA）进行比较,结果表明猫群算法在求解生产调度问题的可行性和有效性。

Solving the blocking flow shop scheduling problem using cat swarm optimization

Blocking flow-shop scheduling is a kind of very important combinatorial optimization problem in the practical production scheduling, it has been proved to be a typical NP-hard problem. In order to improve the performance of solving the problem, this paper proposes cat swarm optimization （CSO） to solve the blocking flow shop scheduling problem. The cat swarm optimization is the swarm intelligence algorithm which has been proposed in recent years, the algorithm uses a fraction of cats to execute trace mode, and the rest of the cats executing search mode, by using the two modes to have global search and local search at the same time in order to achieve the optimization goal. It has simulation experiment by using the standard car example problems, and compares with standard particle swarm optimization （PSO） and bats algorithm （BA） in solving this problem, the results show that cat swarm optimization algorithm can solve the scheduling problem effective and feasible.