A leading NSGAII algorithm based on regional unbalanced subspace

Abstract:

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.