一种基于多Agent协同的准并行遗传算法

A Multi-Agent Cooperating Approach to Quasi-Parallel Genetic Algorithms

提出了一种基于多Agent协同操作的准并行遗传算法结构 .该算法由若干运行简单遗传算法的计算单元组成 ,每个单元也就是独立的计算Agent.算法依照资源分配向量为各单元分配不同的计算资源 ,并根据个体迁移矩阵驱动它们进行个体交换 .从多Agent系统的观点看 ,资源的分配体现了算法对各Agent的协调 ,个体的迁移则体现了Agent之间的协作 .该算法很容易在串行计算机上实现 ,此时各个计算单元具有微观上串行、宏观上并行的准并行关系 .对二维准并行算法动态性能的分析表明 :由于统筹考虑了各计算单元间的协同关系 ,算法能够更充分有效地利用有限的计算资源 ,在解决不同的优化问题时表现出了很高的性能 .

This paper describes a kind of parallel genetic algorithm that is based on the idea of multi-agent cooperation.The algorithm consists of several computing units,in each of which a simple genetic algorithm is maintained,thus each computing unit can be regarded as an independent autonomous agent.The algorithm allocates computing resources to each unit according to the resource-allocating vector and carries through exchange of individuals between units according to the individual-migrating matrix.From the viewpoint of multi-agent system,the allocation of computing resource represents the coordination between agents,while the migration of individuals represents the collaboration between them.The algorithm can be implemented easily in a serial computer and it has the quasi-parallel feature in this case.The analyses to such a quasi-parallel genetic algorithm in two dimension show that since the cooperation between computing agents is taken into account in the algorithm,the computing resources can be utilized in a more effective way and thus better performances are presented when the algorithm deals with different kinds of optimizing problems.