基于Pareto的多目标克隆进化算法

为了克服部分多目标进化算法中容易出现退化与早熟，造成收敛速度过慢的不足，结合精英保留策略、基于近部规则的环境选择以及免疫克隆算法中的比例克隆等思想，提出一种基于Pareto的多目标克隆进化算法NPCA(Non-dominated Pareto Clonal Algorithm)。通过部分多目标优化测试函数ZDT和DTLZ对算法进行了性能测试，验证了该算法能获得分布更加均匀的Parcto前沿，解的收敛性明显优于典型的多目标进化算法。

Pareto-based Multi-object Clonal Evolutionary Algorithm

To overcome the shortcomings of partial multiobjective evolutionary algorithms,we combine some outstanding thoughts in SPEA2 and immune multi-objective optimization algorithm then innovate out a Pareto-based multi-object clonal evolutionary algorithm NPCA(non-dominated Pareto clonal algorithm). And testing the algorithm with the famous multi-objective optimization problems ZDTand DTLZ, the results show that the new algorithm NPCA obviously takes advantages over the typical multi-objective evolutionary algorithms.