The Elite Optimality Procedure for Multi-Objective Evolutionary Algorithms

Multi-Objective Evolutionary Algorithms (MOEAs) are likely used to identify non-dominated solutions or Pareto front (the known Pareto front) in multi-objective optimization problems. The quality of Pareto front depends on evolution strategies that are evaluated under performance metrics of generational distance, spacing, and error ratio. In this paper, a procedure of Elite optimality is proposed to transform the known Pareto front (PFknown) into the true Pareto front (PFtrue). The Elite optimality procedure improves the quality of the Pareto fronts that deals with the biggest challenge in the multi-objective evolutionary algorithms.

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