Lagrange’s Interpolation Embedded Multi-objective Genetic Algorithm to Solve Non-linear Multi-objective Optimization Problems

Abstract

This study describes a novel strategy for solving non-linear multi-objective optimization problems encountered in real-world engineering projects. To find the best trade-off points, a Lagrange's Interpolation embedded multi-objective genetic algorithm (MOGA) is used. In this approach, Lagrange's Interpolation (LI) method is used to capture the non-linear relationship between time and cost. After that, LI is combined with MOGA to create a comprehensive strategy for solving non-linear multi-objective optimization problems in the real world. The study has implications for real-time monitoring and control of the project scheduling process.

Authors

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

DOI:

https://doi.org/10.31449/inf.v48i4.8494

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Published

01/23/2025

How to Cite

Kapoor, M., Pathak, B. K., & Kumar, R. (2025). Lagrange’s Interpolation Embedded Multi-objective Genetic Algorithm to Solve Non-linear Multi-objective Optimization Problems. Informatica, 48(4). https://doi.org/10.31449/inf.v48i4.8494

Issue

Vol. 48 No. 4 (2024)

Section

Student papers

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