摘要
人工免疫系统是基于生物免疫系统特性而发展的新兴智能系统。基于免疫系统的克隆选择机制,提出一种求解车间作业调度问题的免疫算法。利用免疫算法较强的搜索能力可以实现全局寻优。通过使用克隆、高频变异和抗体抑制等免疫操作,提高了算法的收敛速度和种群的多样性,可以有效地克服遗传算法种群早熟化和收敛速度慢的问题。仿真结果表明,与改进后的遗传算法比较,提出的免疫算法在全局最优解和收敛速度上都有较为明显的优势。
Artificial immune system is a new developing intelligent system based on the principles of the natural immune system. An immune algorithm is developed for job-shop schedule problem based on the elonal selection principle of the natural immune system.Using the strong search ability of immune algorithm can find the global optimal.By using the immune operations such as clone,hypermutation and suppression, it can improve the convergence rate and the diversity of population, and effectively solve the problems of population prematurity and slow convergence rate in genetic algorithm.Simulation result shows that the proposed immune algorithm is better in finding global optimal and convergent rate when comparing with the improved genetic algorithm.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第31期34-36,39,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.60703112
深圳市科技计划项目No.QK200601~~
关键词
车间作业调度问题
免疫算法
克隆选择算法
高频变异
job-shop schedule problem
immune algorithm
clonal selection algorithm
hypermutation
