CAJ | 학술논문

对于多目标电网优化规划问题，建立以经济性和可靠性为目标的电网规划模型，通过二进制编码的量子粒子群算法进行优化。为了提高最优解的多样性和分布性，采用拥挤距离排序的方法对外部存储器中的最优解进行更新和维护，使得算法找到尽可能多的Pareto最优解。仿真结果显示，基于拥挤距离排序的二进制量子粒子群算法比其他智能算法寻得的最优解有更好的分布性和收敛性。
대우다목표전망우화규화문제，건립이경제성화가고성위목표적전망규화모형，통과이진제편마적양자입자군산법진행우화。위료제고최우해적다양성화분포성，채용옹제거리배서적방법대외부존저기중적최우해진행경신화유호，사득산법조도진가능다적Pareto최우해。방진결과현시，기우옹제거리배서적이진제양자입자군산법비기타지능산법심득적최우해유경호적분포성화수렴성。
For the multi-objective optimization problem of power network planning, the model is established based on economy and reliability as the goal, through optimizing the quantum particle swarm algorithm of binary code. In order to improve the diversity and distribution of the optimal solution, the optimal sorting method of crowding distance in external memory solutions for the renewal and the maintenance, makes it find as many Pareto optimal solutions as possible. The simulation results show that the optimal binary quantum particle based on crowding distance sorting algorithm for group has a better distribution and convergence than other intelligent algorithm.