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Comparative Study of NSGA-II, MOEA/D, and SPEA2 in Fuzzy Multi-Objective Optimization

Author(s) Manoj Kumar Singh Tomar
Country India
Abstract This study presents a comprehensive comparative analysis of three leading evolutionary algorithms—NSGA-II, MOEA/D, and SPEA2—within a fuzzy multi-objective optimization framework. The primary goal is to evaluate their performance in solving complex optimization problems under uncertainty, where objectives and constraints are represented through fuzzy sets. Benchmark problems from the ZDT and DTLZ families were employed to assess each algorithm’s efficiency based on convergence, diversity, and robustness metrics such as Hypervolume (HV), Generational Distance (GD), Inverted GD (IGD), Spread, and Spacing. Experimental results reveal that the integration of fuzzy modeling significantly enhances optimization performance by providing flexibility and robustness against imprecise or uncertain data. Among the algorithms compared, the fuzzy-enhanced NSGA-II demonstrated superior convergence to the Pareto front, higher diversity, and improved stability, followed by MOEA/D, while SPEA2 showed comparatively lower performance. Statistical tests confirmed the significance of these results, establishing that fuzzy-based multi-objective optimization can yield more realistic and reliable decision outcomes in uncertain environments.
Keywords NSGA-II, MOEA/D, SPEA2, Fuzzy Multi-Objective Optimization, Evolutionary Algorithms, Pareto Front, Hypervolume, Generational Distance, Spread, Uncertainty Modeling
Field Engineering
Published In Volume 6, Issue 1, January 2025
Published On 2025-01-18
Cite This Comparative Study of NSGA-II, MOEA/D, and SPEA2 in Fuzzy Multi-Objective Optimization - Manoj Kumar Singh Tomar - IJLRP Volume 6, Issue 1, January 2025. DOI 10.70528/IJLRP.v6.i1.1834
DOI https://doi.org/10.70528/IJLRP.v6.i1.1834
Short DOI https://doi.org/hbbj4n
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