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Keywords:
NSGA-III, MOEA/DDRA, MW3, Many-objective Optimization, PlatEMO
Abstract
This study explores and compares the performance of two evolutionary algorithms—NSGA-III and MOEA/D-DRA—on the MW3 benchmark problem using the PlatEMO framework. The MW3 challenge simulates a real-world scenario involving multi- objective decision-making, common in engineering design and supply chain optimization. This instance is many-objective in nature. For evaluation, a wide variety of metrics such as Generational Distance (GD), Inverted Generational Distance (IGD), Hypervolume (HV), Spread, Spacing, Runtime, Closest Point to Pareto Front (CPF), Distance Mean (DM), DeltaP , and Proximity-based IGD (IGDp) along with Pareto Diversity (PD) are used. Based on these metrics, it was found that NSGAIII demonstrates superior feasibility rates and spacing consistency alongside other notable advantages.
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