Enhanced Non-dominated Sorting Harris's Hawk Multi-objective Optimizer

Abstract

This paper proposes an enhanced non-dominated sorting Harris's hawk multi-objective optimizer (ENDSHHMO) algorithm. In the original non-dominated sorting Harris's hawk multi-objective optimizer (NDSHHMO) algorithm, the convergence parameter is used to control the diversification and intensification during the search process. The parameter value decreases linearly as the number of iterations of the algorithm increases. This adjustment strategy of the parameter cannot fully reflect the actual optimization search process. Therefore, an improved adjustment strategy has been proposed and integrated with the NDSHHMO algorithm. This strategy can ensure that the proposed algorithm has a better diversification and intensification ability during the optimization process and improves the convergence to the Pareto front. The performance of the proposed enhanced NDSHHMO algorithm has been evaluated using a set of well-known multi-objective optimization problems. The results of the ENDSHHMO are compared with the NDSHHMO algorithm, which shows that the proposed algorithm is superior.