Global view-based selection mechanism for many-objective evolutionary algorithms

Abstract

In traditional many-objective evolutionary algorithms (MaOEAs), solutions survived to the next generation are individually selected which leads to the favorable quality upon the population composed of these selected solutions not necessarily to be gained. However, MaOEAs which are widely used in solving many-objective optimization problems (MaOPs) are considerably preferred due to their population-based nature. In this paper, a global view-based selection mechanism has been proposed to concern the quality of the selected solutions from a global prospective, which is capable of simultaneously facilitating the performance of the entire selected solutions. Indeed, the proposed selection mechanism is equivalent to solve a linear assignment problem whose cost matrix is constructed by the entries concurrently measuring the convergence and diversity of each solution. In addition, this design principle is also utilized for the mating selection to guarantee the convergence and diversity of the selected parents to generate promising offspring. As a case study, the proposed global view-based selection mechanism is integrated into NSGA-III (i.e., GS-NSGA-III). To validate the proposed selection mechanism, extensive experiments are performed by GS-NSGA-III against four state-of-the-art MaOEAs over 8-, 10-, and 15-objective DTLZ1-DTLZ7 test problems. The results measured by the selected performance metric reveal that GS-NSGA-III shows considerable competitiveness in addressing MaOPs.