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Integrated LI-NSGA-II Approach for Solving the Non-linear Multi-objective Optimization Problem

Authors

  • Muskan Kapoor Jaypee University of Information Technology, Waknaghat, Solan (H.P.)
  • Bhupendra Kumar Pathak Assistant Professor
  • Rajiv Kumar Jaypee University of Information Technology, Waknaghat, Solan (H.P.)

Abstract

Real-world engineering projects frequently involve complex, non-linear multi-objective optimization challenges. Traditional methods like PERT/CPM and basic heuristics often fail to provide optimal solutions in such scenarios. Multi-objective evolutionary algorithms, such as genetic algorithms and non-dominated sorting genetic algorithms, are more effective for identifying true Pareto solutions. Among these, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is a well-known algorithm for solving multi-objective optimization problems. This study presents an integrated approach, called LI-NSGA-II, which combines Lagrange's Interpolation (LI) with NSGA-II to solve non-linear multi-objective optimization problems in real-world projects. In this LI-NSGA-II approach, LI is used to handle the non-linearity of competing objectives, whereas NSGA-II optimizes these objectives to find optimal solutions. The outcomes achieved through the LI-NSGA-II approach are ideal for real-time monitoring and control of non-linear multi-objective optimization problems.

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How to Cite

Kapoor, M., Pathak, B. K., & Kumar, R. Integrated LI-NSGA-II Approach for Solving the Non-linear Multi-objective Optimization Problem. Austrian Journal of Statistics, 53(5), 39–51. Retrieved from https://ajs.or.at/index.php/ajs/article/view/1898

Issue

Vol. 53 No. 5 (2024): Machine Learning and Statistical Modeling for Real-World Data Applications and Artificial Intelligence

Section

Machine Learning and Statistical Modeling for Real-World Data Applications and Artificial Intelligence

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