摘要
通过将多智能体系统加入基本的粒子群算法(PSO),提出了一种新的函数优化方法——多智能体遗传PSO算法(MAGPA)。该方法将智能体固定在网格上,而每个智能体通过邻域的竞争和合作,随机交叉操作,变异操作,再联合PSO的进化机制,不断地感受局部环境,逐步影响整个智能体网格,以增强对环境的适应度。该算法可以有效地保持智能体的多样性,提高优化的准确性。
The efforts of this paper are proposing a new multi-agent genetic particle swarm optimization algorithm（MAGPA） for function optimization by introducing the multi-agent system to the particle swarm optimization（PSO） algorithm. Each agent is fixed on the grid, and through the competition and cooperation operation with its neighbors, the neighborhood random crossing operation within its neighboring area, the mutation operation, and combining the evolutionary mechanism of the PSO algorithm, every individual senses local environment unceasingly, and affects the entire agent grid gradually, so that it enhances its fitness to the environment. This algorithm can maintain the diversity of the swarm effectively, and improve the precision of optimization.
出处
《电子测试》
2010年第2期31-35,共5页
Electronic Test
