现阶段在研究电网经济环保负荷优化分配问题时，将多目标优化问题转化为单目标优化问题需要遍历各权重组合并进行对比，且获得的权重组合精度较低，和最优权重组合之间仍有一定差距。据此提出一种利用发电机组数和优化目标个数共同确定粒子维数的自适应多目标权重粒子群优化(adaptive multi-objective weights particle swarm optimization algorithm, AMWPSO)算法，并利用包含6台机组的IEEE 30总线系统的数据，在计及和忽略网损2种情况下对负荷分配模型进行求解。求解结果表明所提优化算法一方面能够自动生成各目标的权重比，避免了繁琐的权重对比计算。另一方面和人工设置的邻近权重组合相比，求解的各目标权重精度高，且在发电成本和污染物排放2个综合目标下的表现更好。

At this stage, when studying the economic and environmental load distribution of the power grid, the conversion of the multi-objective optimization problem into a single-objective optimization problem generally requires traversing the reorganization of the weights for comparison. Moreover, the obtained weight combination has a low precision, and there is still a certain gap between the optimal weight combination. In view of this situation, an adaptive multi-objective weighted particle swarm optimization (AMWPSO) algorithm is proposed that uses the number of generator sets and the number of optimization targets to determine the particle dimension, and uses the data of the IEEE 30 bus system including 6 units to account for and ignore the network. Solve the load distribution model under two conditions. The solution results show that the proposed optimization algorithm can automatically generate the weight ratio of each target on the one hand, avoiding the tedious weight comparison calculation. On the other hand, compared with the combination of manually set neighboring weights, the calculated target weights have high accuracy and perform better under the two comprehensive targets of power generation cost and pollutant emission.