摘要
基于克隆选择原理与细胞超变异思想构造了一种免疫进化算法CHIEA(Clonal selection and hyper mutations based immune evolution algorithm)求解静态JSP问题(Job shop scheduling problem)。随机混排变异算子的构造和抗体连续累积变异的实施丰富了细胞超变异的内容,基于优先列表编码方式的采用和免疫进化算子的构造提高了搜索效率,加速了算法收敛并提高了解的质量。通过与COELLO的AIS(Artificial immune system)算法的全面比较得出,CHIEA求解不同类型中小规模的静态JSP问题时具有更好的优化性能。
An immune evolution algorithm CHIEA（Clonal selection and hyper mutations based immune evolution algorithm） is proposed for solving deterministic job shop scheduling problems. The algorithm is based on clonal selection and hyper mutations. A random permutation operator and a consecutive mutation method of antibodies is introduced to extend the concept of hyper mutations. The preference list based representation and the immune evolution operator improves searching efficiency, accelerates convergence of the algorithm and advances solutions generated. A thorough comparison between CHIEA and COELLO＇AIS（Artificial immune system） proves CHIEA has better optimizing performances for deterministic job shop scheduling problems varying in styles and appropriate sizes.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2006年第5期87-91,共5页
Journal of Mechanical Engineering
基金
国家863计划(2003AA411110)
博士点基金(20040699025)资助项目。
关键词
静态JSP
免疫进化
细胞超变异
优先列表编码
Deterministic job shop scheduling problem
Immune evolution
Hyper mutations
Preference list based representation
