Elitist non-dominated sorting Harris hawks optimization: Framework and developments for multi-objective problems

• A novel multi-objective non-sorted Harris Hawks Optimizer (NSHHO) is proposed. • A non-dominated ranking based on crowding distance mechanism is applied to HHO. • A total of 46 case studies is used to check the performance of NSHHO. • NSHHO is compared with NSGA-II, MOEA/D, and MOPSO quantitatively. This paper proposed a novel multi-objective non-sorted Harris Hawks Optimizer (NSHHO) part of the recently developed Harris Hawks Optimizer (HHO) based on an elitist non dominated sorting mechanism. The same Harris Hawks Optimizer methodology was issued for converging towards optimum solutions in a multiple-objective criterion search space. For obtained well-distributed Pareto optimal front and their solution has better coverage. However, a non-dominated ranking with the crowding distance strategy is applied to HHO. To check the performance of NSHHO, a total of 46 case studies include 13 un-constrained, 11 constrained, and 22 real-world design multiple-objective highly nonlinear constraint problems. The obtained results of the proposed NSHHO are compared with NSGA-II, MOPSO and MOEA/D, quantitatively, and different performance metrics are compared qualitatively, which represents the advantage of the newly proposed NSHHO algorithm in solving the unconstrained, constrained, and real-world problems with different linear, nonlinear, continuous and discrete characteristics based Pareto front. We think the proposed NSHHO can be viral as an effective multi-objective optimizer within the field. The open-source software of NSHHO is publicly provided at https://codeocean.com/capsule/2034037/tree and https://aliasgharheidari.com/HHO.html .