An Efficient Multi-objective Evolutionary Algorithm with Steady-State Replacement Model

The generic Multi-objective Evolutionary Algorithm (MOEA) aims to produce Pareto-front approximations with good convergence and diversity property. To achieve convergence, most multi-objective evolutionary algorithms today employ Pareto-ranking as the main criteria for fitness calculation. The computation of Pareto-rank in a population is time consuming, and arguably the most computationally expensive component in an iteration of the said algorithms. This paper proposes a Multiobjective Evolutionary Algorithm which avoids Pareto-ranking altogether by employing the transitivity of the domination relation. The proposed algorithm is an elitist algorithm with explicit diversity preservation procedure. It applies a measure reflecting the degree of domination between solutions in a steadystate replacement strategy to determine which individuals survive to the next iteration. Results on nine standard test functions demonstrated that the algorithm performs favorably compared to the popular NSGA-II in terms of convergence as well as diversity of the Pareto-set approximation, and is computationally more efficient