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针对粒子群算法(PSO)在搜索空间范围较大时搜索能力变差,甚至出现不收敛问题,提出一种对PID控制器3个参数进行整定的带有目标性初始化粒子群的改进粒子群优化算法(PSOOI)。通过引入粒子间相似熵和参数内熵对初始化粒子种群进行调整,以获得分散性较高的初始种群,提高粒子群算法的全局搜索能力和收敛速度。最后针对典型的控制对象进行PID控制器3个参数整定,研究结果表明所提出的PSOOI控制算法具有较大搜索空间范围时的全局寻优能力和快速收敛性优点。
Aiming at the problem that the particle swarm optimization (PSO) has poor search ability and even non-convergence when the search space is large, an improved Particle Swarm Optimization (PSO) with target initialization particle swarm optimization Algorithm (PSOOI). The initial particle population is adjusted by introducing the similarity between the particles and the entropy of the parameter to obtain the initial population with higher dispersion and improve the global search ability and convergence rate of the particle swarm optimization algorithm. Finally, according to the typical control object, three parameters of PID controller are set. The results show that the proposed PSOOI control algorithm has the advantages of global optimization ability and fast convergence when it has a large search space.