论文部分内容阅读
为了解决柔性流水车间排产优化问题(flexible flow-shop scheduling problem,FFSP),以最小化最大完工时间为优化目标,提出了一种新的改进算法—IICA算法作为全局优化算法。在标准帝国竞争算法的基础上,引入汉明距离的概念判断个体之间的相似度,将各帝国集团内最弱的殖民地用一个随机解代替并保留失去所有殖民地的帝国个体。最后通过标准实例测试,将IICA算法与多种群体智能进化算法以及标准帝国竞争算法进行仿真比较,验证了IICA算法在解决柔性流水车间排产优化问题的有效性,具有较好的收敛速度和更好的全局最优解。
In order to solve the flexible flow-shop scheduling problem (FFSP), aiming at the optimization goal of minimizing the maximum completion time, a new improved algorithm-IICA algorithm is proposed as a global optimization algorithm. On the basis of standard imperial competition algorithm, the concept of Hamming distance is introduced to judge the similarity between individuals, replacing the weakest colonies in each empire group with a random solution and keeping the empires who lose all colonies. Finally, through the test of standard examples, the IICA algorithm is compared with the multi-population intelligent evolutionary algorithm and the standard imperial competition algorithm to verify the validity of the IICA algorithm in solving the problem of flexible production shop scheduling optimization, with better convergence speed and more Good global optimal solution.