An Empirical Study of Aggregation Operators with Pareto Dominance in Multiobjective Genetic Algorithm

ABSTRACTGenetic algorithms (GAs) have been widely used in solving multiobjective optimization problems (MOP). The foremost hindrance limiting strength of GA is the large number of nondominated solutions and the computational complexity involved in selecting a preferential candidate among the set of nondominated solutions. In this paper, we analyze the approach of applying aggregation operator in place of density-based indicator mechanism in cases where Pareto dominance method fails to decide the preferential solution. We also propose a new aggregation function () and compare the results obtained with prevailing aggregation functions suggested in the literature. We demonstrate that the proposed method is computationally less expensive with overall complexity of . To show the efficacy and consistency of the proposed method, we applied it on different, two- and three-objective benchmark functions. Results indicate a good convergence rate along with a near-perfect diverse approximation set.