摘要
为了克服遗传算法早熟和最优解较差的缺点 ,提出了求解作业车间调度问题的新方法。首先设计了简单、易操作的编码和解码方法 ,然后应用思维进化计算的趋同和异化操作求解该问题。增强了进化的方向性 ,在趋同过程中 ,充分利用优秀个体的信息 ,利用信息矩阵记录优秀个体的特征 ,并以信息矩阵为指导产生新个体。利用异化操作进行全局搜索 ,使算法具有全局收敛性。
To overcome prematurity & of genetic algorithm, a new algorithm to solve job-shop scheduling was presented. A simple and easy operation method of coding and decoding was designed first. Then job-shop scheduling could be solved by conducting convergence and dissimilation of Mind Evolutionary Computation. In the course of convergence, make full use of information of excellent individuals the characteristics of these superior individuals memorized in information matrix, new individuals would be generated under the guidance of information matrix. Conducting overall research by dissimilation operation. Simulation experiment results have shown the effectiveness of this algorithm.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2004年第10期1242-1246,共5页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目 ( 60 1740 0 2 )
山西省青年科学基金资助项目 ( 2 0 0 3 10 3 1)~~
关键词
作业车间调度问题
编码和解码
信息矩阵
趋同和异化
思维进化计算
job-shop scheduling
coding and decoding
information matrix
convergence and dissimilation
mind evolutionary computation
