Recent Advances in Machine Learning-based Optimization Techniques: A Systematic Review

Abstract

Exploring the convergence of machine learning and optimization techniques, this paper delves into the recent progress and wide-ranging applications of these methods in various domains, including chemical engineering, pharmaceuticals, and environmental management. Through a detailed analysis of pivotal studies published, the paper evaluates the effectiveness, efficiency, applicability, and limitations of different machine learning strategies in optimization scenarios. It reveals how the integration of machine learning in optimization transcends disciplinary boundaries, significantly impacting sectors from healthcare to environmental sustainability. The paper highlights significant developments such as the emergence of hybrid and evolutionary models, advancements in personalized healthcare, and breakthroughs in sustainable resource management and security. Additionally, it discusses the challenges and ethical implications inherent in these advancements, particularly the need for comprehensive datasets and substantial computational resources. Ultimately, the insights gathered suggest that machine learning-based optimization is crucial for fostering innovation and addressing complex challenges in various fields, emphasizing the need for its responsible and ethical use in the ever-changing technological landscape.