摘要
为了解决遗传算法寻优过程中的早熟收敛问题 ,本文提出了一种基于混沌迁移策略的伪并行遗传算法 ,该算法针对实时性要求不高的优化问题采用串行的算法结构实现分解型并行遗传算法的“独立进化、信息交换”思想 .在并行进化的个体异步迁移过程中 ,引入了混沌迁移序列引导个体迁移过程 ,利用其遍历性和随机性 ,保证了子种群之间能够进行充分高效的信息交换 .仿真研究和在库存优化方面的应用研究表明 ,这种算法具有很强的全局搜索能力 ,寻优效率高 ,有效克服了标准遗传算法的早熟收敛问题 .
To address the premature convergence in the searching process of genetic algorithm a chaotic migration-based pseudo parallel genetic algorithm (CMPPGA) is proposed,which applied the idea of isolated evolution and information exchanging in distributed parallel genetic algorithm by serial program structure to solve the optimization problem of low real-time demand.In this algorithm,the asynchronic migration of individuals during parallel evolution is guided by a chaotic migration sequence.Because the sequence is ergodic and stochastic,information exchanging among sub-populations is ensured to be efficient and sufficient.Simulation study of CMPPGA and its application to inventory optimization have proved its capability of strong global search and superiority to SGA and high immunity against premature convergence.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
2004年第6期997-1002,共6页
Control Theory & Applications
基金
国家十五 863计划项目 (2 0 0 1AA4110 40
2 0 0 1AA414 2 40 )
关键词
并行遗传算法
混沌迁移
早熟收敛
库存优化
parallel genetic algorithm
chaotic migration
premature convergence
inventory optimization
