Abstract: 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.

Key words: multi-objective optimization, Quantum Particle Swarm Optimization（QPSO）, binary code, crowding distance, Pareto optimal solution

摘要： 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。

关键词: 多目标优化, 量子粒子群算法, 二进制编码, 拥挤距离, Pareto最优解