An Aggregation Based Approach with Pareto Ranking in Multiobjective Genetic Algorithm

Genetic algorithms (GA) have been widely used in solving multiobjective optimization problems. The foremost problem limiting the strength of GA is the large number of nondominated solutions and complexity in selecting a preferential candidate among the set of nondominated solutions. In this paper we propose a new aggregation operator which removes the need of calculating crowding distance when two or more candidate solutions belong to the same set of nondominated front. This operator is computationally less expensive with overall complexity of O(m). To prove the effectiveness and consistency, we applied this operator on 11 different, two-objective benchmarks functions with two different recombination and mutation operator pairs. The simulation was carried out over several independent runs and results obtained have been discussed.