摘要
为了提高非劣解向Pareto最优前沿收敛的速度及进一步提高解的精度,在设计了一种新的杂交算子并改进了NSGA-Ⅱ的拥挤操作的基础上,提出了一种基于分级策略的多目标演化算法。数值实验表明,新算法能够非常高效地处理高维的最优前沿为凸的、非凸的和不连续前沿的多目标测试函数,得到的非劣解具有很好的分布性质。但在处理高维的具有太多局部最优前沿的多峰函数时极易陷入局部最优前沿。
This paper proposes a novel multi-objective evolutionary algorithm based on a novel crossover operation and improves crowding operation of NSGA-Ⅱ,in order to quicken further rate of convergence of solutions to Pareto optimal front and improve precision of solutions.The numeric experiments results indicate the new algorithm is very efficient for muhi-objective test problems of high-dimension with Pareto optimal front of convex or non-convex or discontinuous and convex.The obtained non-dominated solutions have a good distribution property.But as to high-dimension functions with too many local Pareto optimal fronts,it traps in local Pareto optimal front easily.
出处
《计算机工程与应用》
CSCD
北大核心
2007年第11期75-77,86,共4页
Computer Engineering and Applications
关键词
多目标优化问题
多目标演化算法
PARETO最优
multi-objective optimization problem
multi-objective evolutionary algorithm
Pareto optimality
