INVESTIGATION OF OPTIMIZATION TECHNIQUES FOR NONLINEAR PROGRAMMING PROBLEMS

A wide variety of optimization problems include the nonlinear objective functions that are referred to as nonlinear programming problems (NLPs). This study explores numerous optimisation methods intended to effectively and precisely address NLPs. The best strategies for resolving these challenging issues will be determined and assessed. The investigation starts off by providing a summary of the mathematical formulations and important ideas that form the theoretical basis of NLPs. It highlights the inherent challenges and complexities involved in addressing several classes of NLPs, including as convex and non-convex problems. Using a wide range of benchmark NLP problems, a thorough experimental investigation is carried out to evaluate the performance of different strategies. Numerous factors, including convergence rate, solution quality, robustness, and computational efficiency are examined in the study. The effectiveness and applicability of each optimisation technique for various problem kinds and problem sizes are assessed through comparison and analysis of the outcomes. The paper also examines recent improvements and breakthroughs in NLP optimisation techniques, taking into account new paradigms like machine learning and met heuristic algorithms. The advantages and drawbacks of integrating these strategies into conventional optimisation techniques are highlighted. The study's conclusions advance knowledge of NLP optimisation strategies and offer useful information to practitioners and academics tackling practical issues. Based on the features of the problem and available computational resources, the results can direct the selection and use of suitable optimisation techniques