一种新的多目标粒子群优化算法

论文提出了一种基于拥挤度和动态惯性权重聚合的多目标粒子群优化算法，该算法采用Pareto支配关系来更新粒子的个体最优值，用外部存档策略保存搜索过程中发现的非支配解；采用适应值拥挤度裁剪归档中的非支配解，并从归档中的稀松区域随机选取精英作为粒子的全局最优位置，以保持解的多样性；采用动态惯性权重聚合的方法以使算法尽可能地逼近各目标的最优解。仿真结果表明，该算法性能较好，能很好地求解多目标优化问题。

Novel particle swarm optimization method for multi-objective optimization

The paper presents a Particle Swarm Optimization algorithm based on crowding distance and Dynamic Weighted Aggregation(CDDWA-PSO) for multi-objective optimization problems.PSO is modified by storing nondominated solutions externally and selecting a nondominated solution from external archive randomly using as the global best.The proposed algorithm introduces the Pareto dominance relationship and crowding distance of fitness to preserve population diversity,and incorporates DWA procedure to close to the best solution of every objective.Several benchmark cases are tested and the results show that the method can efficiently find multiple Pareto optimal solutions well.