基于区域失衡子空间的领先NSGAII算法

针对传统进化算法求解多目标优化问题时存在计算量大、难以平衡收敛速度和种群分布均匀性的问题，本文提出了一种基于区域失衡子空间的领先NSGAII算法(NSGAII-URS).首先，基于NSGAII算法，结合局部搜索算法，在每次遗传过程中添加种群领先解解集，引导种群快速收敛；然后，将非支配解所在的目标空间均匀划分，提出稀疏子空间和空闲子空间的概念，通过基于稀疏度的局部搜索策略对失衡子空间优化，进一步提升种群分布的均匀性.我们将本文方法与其他5种先进的多目标进化算法比较，通过基准测试函数进行验证，并采用反世代距离(IGD)和超体积(HV)两个通用指标进行性能评价.实验结果表明，该算法在解的分布性和收敛性方面明显优于对比的其他多目标优化算法.

A leading NSGAII algorithm based on regional unbalanced subspace

In order to address the drawbacks such as a large amount of calculation, the difficulty in balancing convergence speed, and uniformity of population distribution when solving multi objective optimization problems with traditional evolutionary algorithms, a leading NSGAII algorithm based on regional unbalanced subspace (NSGAII URS) is proposed. First, based on the NSGAII algorithm and the local search algorithm, the population leading solution set is added in each genetic process to guide the population to converge quickly. Then the target space, where the non dominated solution is located, is evenly divided, the concepts of sparse subspace and free subspace are introduced. Finally, the unbalanced subspace is optimized by a local search strategy based on sparse degree to further improve the uniformity of the population distribution. The proposed algorithm is compared with five other advanced multi objective evolutionary algorithms, verified by benchmark test function, and two general indicators of inverse generation distance (IGD) and hypervolume (HV) are used for performance evaluation. Experimental results show that the proposed NSGAII algorithm is significantly better than other compared multi objective optimization algorithms in terms of solution distribution and convergence.