CAJ | 학술논문

文中提出一种多智能体量子粒子群优化算法（ Multi Agent Quantum Particle Swam Optimization ，MAQPSO）求解电力系统无功优化问题，改善了传统量子粒子群算法后期收敛速度慢、易陷入局部最优解等缺点。该算法结合了量子粒子群算法和多智能体进化思想，每一个Agent相当于量子粒子群优化算法中的一个粒子，通过A-gent的邻域竞争、自学习等操作，使得算法能够更迅速、更精确地收敛到全局最优解。通过对IEEE14、30、57和118节点系统的优化仿真，结果表明该算法有收敛精度高、寻优速度快等优点。
문중제출일충다지능체양자입자군우화산법（ Multi Agent Quantum Particle Swam Optimization ，MAQPSO）구해전력계통무공우화문제，개선료전통양자입자군산법후기수렴속도만、역함입국부최우해등결점。해산법결합료양자입자군산법화다지능체진화사상，매일개Agent상당우양자입자군우화산법중적일개입자，통과A-gent적린역경쟁、자학습등조작，사득산법능구경신속、경정학지수렴도전국최우해。통과대IEEE14、30、57화118절점계통적우화방진，결과표명해산법유수렴정도고、심우속도쾌등우점。
This paper proposed a novel quantum particle swam optimization algorithm based on multi agent system ( MAQPSO) approach to solve the reactive power optimization .The algorithm overcomes the defects of conventional quantum particle swam optimization slow convergent speed and easy convergence to local minimum point of error func -tion on later .This algorithm combined quantum particle swam optimization algorithm with multi -agent evolutionary thought .Every Agent serves as a particle of quantum particle swam , competition by the agent of the neighborhood and self-study , thus the algorithm can more quickly and accurately converge to global optimal solution .On the basis of the optimization simulation for IEEE14, 30, 57 and 118 nodes system, the results show that the algorithm has the advan-tages of high convergence precision and speed of optimization .