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

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