Improved NSGA-III using neighborhood information and scalarization

Recent efforts in the evolutionary multi-objective optimization (EMO) community focus on addressing shortcomings of current solution techniques adopted for solving many-objective optimization problems (MaOPs). One such challenge faced by classical multi-objective evolutionary algorithms is diversity preservation in optimization problems with more than three objectives, namely MaOPs. In this vein, NSGAIII has replaced the crowding distance measure in NSGA-II with reference points in the objective space to ensure diversity of the converged solutions along the pre-determined solutions in the environmental selection phase. NSGA-III uses the Paretodominance principle to obtain the non-dominated solutions in the environmental selection phase. However, the Pareto-dominance principle loses its selection pressure in high-dimensional optimization problems, because most of the obtained solutions become non-dominated. Inspired by θ-DEA, we address the selection pressure issue in NSGA-III, by exploiting the decomposition principle of MOEA/D using reference points for multiple single-objective optimization problems. Moreover, similar to MOEA/D, the parent selection process is restricted to the neighboring solutions, as opposed to random selection of parent solutions from the entire population in NSGA-III. The effectiveness of the proposed method is demonstrated on different well-known benchmark optimization problems for 3- to 10- objectives. The results compare favorably with those from MOEA/D, NSGA-III, and θ-DEA.