CAJ | 학술논문

结合多智能体的学习、协调策略及粒子群算法,提出了一种基于多智能体粒子群优化的配电网络重构方法.该方法采用粒子群算法的拓扑结构来构建多智能体的体系结构,在多智能体系统中,每一个粒子作为一个智能体,通过与邻域的智能体竞争、合作,能够更快、更精确地收敛到全局最优解.粒子的更新规则减少了算法不可行解的产生,提高了算法效率.实验结果表明,该方法具有很高的搜索效率和寻优性能.
결합다지능체적학습、협조책략급입자군산법,제출료일충기우다지능체입자군우화적배전망락중구방법.해방법채용입자군산법적탁복결구래구건다지능체적체계결구,재다지능체계통중,매일개입자작위일개지능체,통과여린역적지능체경쟁、합작,능구경쾌、경정학지수렴도전국최우해.입자적경신규칙감소료산법불가행해적산생,제고료산법효솔.실험결과표명,해방법구유흔고적수색효솔화심우성능.
Combining the study of multi-agent technology,coordinating strategies with PSO,a Multi-Agent Particle Swarm Optimization(MA-PSO)algorithm is presented to handle distribution network reconfiguration problem. It applies Von Neuman architecture of Particle Swarm Optimization algorithm to the composition of multi-agent system.An agent in MA-PSO represents a particle to PSO and a candidate solution to the optimization problem.In order to decrease fitness value quickly,agents compete and cooperate with their agent of neighboring area.Making use of these agent-agent interactions,MA-PSO realizes the purpose of minimizing the value of objective function.The rules of particle renovating reduce unfeasible solution in the process of particle renovating,which raises the algorithm efficiency greatly.The experiment results indicate the prominent efficiency and significant global optima searching performance of MS-PSO.