摘要： 针对约束多目标优化问题，提出一种改进的粒子群优化算法，采用距离量度和自适应惩罚函数相结合的约束处理技术，通过可行解比例有效均衡目标函数和约束条件，提高算法的边界搜索能力。定义新的k最近邻聚集密度，保持解集分布性，并将聚集密度和轮盘赌选择相结合选取全局最优粒子。仿真结果表明，该算法在Pareto解集均匀性及逼近性方面均具有优势。

关键词: 约束多目标优化, 距离量度, 自适应惩罚, 粒子群优化算法

Abstract: An improved constrained multi-objective Particle Swarm Optimization(PSO) algorithm which adopts constraint handling technology based on distance measures and adaptive penalty functions is proposed. The proportion of feasible solutions is used to keep the balance between objective function and constraints and improve the boundary searching capability of the algorithm. A new k nearest neighbors crowding density is defined to maintain the diversity of solutions. It selects the global optimal particle through the combination of crowding density and roulette selection. Experimental results demonstrate that the algorithm is efficient for solving constrained multi-objective optimization problems

Key words: constrained multi-objective optimization, distance measure, adaptive penalty, Particle Swarm Optimization(PSO) algorithm

中图分类号: 

• TP301