A Comparative Study of NSGAIII and RVEA on MW2 Benchmark Problem with Real-world Relevance

Authors

  • Syed Muhammad Faizan Alam Department of Robotics and Artificial Intelligence, SZABIST University Karachi, Pakistan
  • Ummi Mursaleen Department of Robotics and Artificial Intelligence, SZABIST University Karachi, Pakistan
  • Aimen Daud Department of Robotics and Artificial Intelligence, SZABIST University Karachi, Pakistan
  • Syed Umarullah Hussaini SZABIST University Karachi

DOI:

https://doi.org/10.63094/AITUSRJ.25.4.2.2

Keywords:

Multi-objective optimization, NSGA-III, RVEA, MW2 benchmark, Portfolio optimization, Hypervolume (HV), Inverted Generational Distance (IGD), Convergence analysis, Diversity metrics, PlatEMO, Evolutionary algorithms, Pareto front, Reference-point method, Many-objective optimization, Algorithm comparison, Risk-return tradeoff, Financial optimization, WFG problem suite, Performance metrics, Decision support systems

Abstract

This paper presents a comparative investigation of the two state of the art Multi objective Evolutionary algorithms NSGAIII and RVEA using the PlatEMO platform on the MW2 benchmark problem. In MW2 problem there are twelve decision variables with two objectives, is plotted to practical Portfolio optimization where goal of depositors is to balance the risk and return across various assets. It is assessed with typical metrics such as Hypervolume (HV), Inverted Generational Distance (IGD), and Diversity. Analyzing 30 independent runs present that NSGAIII constantly gives better convergence and spread than RVEA. This study provides real world insights for algorithm selection in competitive decision making scenarios.

References

Deb, K., Jain, H. (2014). An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems with Box Constraints. IEEE Transactions on Evolutionary Computation.

Cheng, R., Jin, Y. (2015). A Reference Vector Guided Evolutionary Algorithm for MultiObjective Optimization. IEEE Transactions on Evolutionary Computation.

Li, K., Deb, K., Zhang, Q., & Kwong, S. (2015). An Evolutionary Many-Objective Optimization Algorithm Based on Dominance and Decomposition. IEEE Transactions on Evolutionary Computation.

Zhou, A., Qu, B.-Y., Li, H., Zhao, S.-Z., Suganthan, P. N., & Zhang, Q. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation.

Tian, Y., Zhang, X., Cheng, R., Jin, Y. (2017). PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization. Proceedings of the 2017 IEEE Congress on Evolutionary Computation (CEC).

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Published

2025-11-21

How to Cite

Alam, S. M. F., Mursaleen, U., Daud, A., & Hussaini, S. U. (2025). A Comparative Study of NSGAIII and RVEA on MW2 Benchmark Problem with Real-world Relevance . AITU SCIENTIFIC RESEARCH JOURNAL, 4(2), 10–14. https://doi.org/10.63094/AITUSRJ.25.4.2.2

Issue

Vol. 4 No. 2 (2025): Vol. 4 No. 2 (2025): AITU-SRJ Volume 4 Issue 2 (July-December) 2025

Section

Articles

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Copyright (c) 2025 AITU SCIENTIFIC RESEARCH JOURNAL

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