【摘要】 针对标准粒子群优化(PSO)算法易陷入局部最优解的缺点,提出了闭环PSO(CLPSO)算法。算法引入经典控制理论中的反馈机制和闭环控制概念,将每个粒子视为被控对象,根据每一步得到的适应值通过PID控制器动态调整惯性权重,以满足搜索过程中粒子时时变化的需求。该策略极大地保证了粒子多样性,提高了算法的全局搜索能力。将CLPSO算法应用到机组组合问题中,同时结合新的策略以降低问题维数和保证寻优过程中粒子的可行性。仿真结果验证了所提出的算法在解决机组组合问题上的有效性。更多还原

【Abstract】 A closed-loop particle swarm optimization(CLPSO)algorithm is proposed by mapping PSO elements into the closed-loop system based on control theories.At each step,a PID controller is used to calculate an updated inertia weight for each particle in the swarm from its last fitness.With this modification,the limitations caused by a uniform inertia weight for the whole population are avoided,and the particles have sufficient diversity.In solving unit commitment(UC)problems with the CLPSO algorithm proposed,novel strategies are adopted to reduce the problem dimensions and guarantee particle feasibility.Simulation results demonstrate the superiority of the method proposed in solving UC problems.更多还原