自适应粒子群算法求解Agent联盟

Solving Agent coalition using adaptive particle swarm optimization algorithm

联盟生成是多Agent系统的一个关键问题,主要研究如何在多Agent系统中动态生成面向任务的最优A-gent联盟.引入粒子群算法来解决这一问题,受到惯性权重c0在进化过程中所起作用的启发,引入自适应惯性权重cadp对粒子群算法进行改进,使其不再易于陷入局部极小.对比实验结果表明,该算法在解的性能和收敛速度上均优于相关算法.

Coalltion Generation is a key issue in a Multi-Agent System which primarily focuses on generation of an optimal task-oriented coalition in a dynamic manner. A particle swarm optimization （PSO） algorithm is adopted to solve the problem. And a novel ＂adaptive inertia weight＂ is proposed to improve PSO by the illumination of function of inertia weight ,so as to avoid falling into local minimum. The results of comparison experiments show that this algorithm is superior to other related methods in both performance of solution and convergence rate.