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 NS...

• Type: Conference
• Year: 2006
• Where: GECCO
• Authors: Dipti Srinivasan, Lily Rachmawati
• Keywords: Evolutionary Algorithm, GECCO 2006, Multi-objective Evolutionary Algorithms, Multiobjective Evolutionary Algorithm, Optimization