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.