提出了一种新的多目标粒子群优化(MOPSO)算法，该算法采用自适应网格方法来估计非劣解集中粒子的密度信息、平衡全局和局

部搜索能力的 Pareto 最优解的搜索机制、删除品质差的多余粒子的 Archive 集的修剪技术。通过对三峡梯级多目标优化调度问题的计算，

表明该算法是求解大规模复杂多目标优化问题的一种有效手段。-A new multi-objective particle swarm optimization(MOPSO) is proposed. The proposed algorithms employs three techniques: adaptive

grid algorithms, which can obtain the valid density value of particles in Archive set　Pareto optimal solution searching algorithm, which can equalize

the ability of global and local searching　Archive pruning techniques, which can remove inferior particles in Archive set to fix the size of Archive set.

The algorithm is applied to solve multi-objective optimal regulation of Three Gorges. The simulation performance indicates the effectiveness of the

presented algorithm with regard to solving the large scale complex multi-objective optimization problem.