Advancements in Optimization: A 20-Year Review of Trends, Innovations, and Applications

Abstract

Optimization, a foundational discipline in mathematics, computer science, and engineering, is dedicated to identifying the most effective solutions to complex problems by minimizing or maximizing objective functions subject to defined constraints. This paper provides a comprehensive review of the prevailing research trends in optimization during the past two decades, examining the evolution, interconnections, and strategic implications of Multi-Objective Optimization, Evolutionary Algorithms, Machine Learning Optimization, and general Algorithm Development. We explore the broad spectrum of contemporary applications and delineate promising future directions, including optimization under uncertainty, explainable optimization, and the nascent field of quantum optimization