一种基于MAS-GA的混合进化计算模型  

A Genetic Algorith Model Based on MAS-GA

作　　者：王文彦[1] 

机构地区：[1]广东工业大学计算机学院,广东广州510000

出　　处：《石油化工高等学校学报》2007年第3期33-36,共4页Journal of Petrochemical Universities

基　　金：湖南省自然科学基金资助(06JJ2033)

摘　　要：针对优化规划求解目前还缺乏高效、实用的算法,而传统遗传算法由于受确定编码形式的制约而缺乏对复杂系统的表述能力的问题,提出了一种基于Agent行为遗传和范例学习的新型进化算法。并分别采用新型遗传算法和传统遗传算法对同一算例网络进行优化规划,实验结果说明了本文算法具有更好的复杂的问题表述能力、计算效率、收敛稳定性以及可扩展性。Distribution system optimal planning has vital significance, but there isn＇t efficient and practical algorithm at traditional genetic algorithm has a poor expressive power for complicated problem because of the restriction of its ncodmg mode. which limits the application fields of genetic algorithm. A new type of genetic algorithm based on Agent behavior and paradigm learning was presented. An example distribution network was optimized by genetic algorithm and traditional genetic algorithm respectively. The comparative result proves the new genetic has higher expressive power, computing efficiency, convergent stability and extendable capability.

关 键 词：AGENT行为 Agent学习 进化算法 优化规划 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]