Do Optimization Algorithms Hold the Key to Advancing AI and Machine Learning? A Comprehensive Meta-Analysis Review
Hussein Mohammed Ali, Mahmood Yashar Hamza, Tarik Ahmed Rashid, Bryar A. Hassan, Abdulhady Abas Abdullah · 2025
This paper delegates a brief explanation of optimization algorithms, related works in the area, their types, and behaviours, all of which play a significant part in algorithm development. This study examines optimization algorithms, which are essential tools in addressing complex computational problems across diverse fields. These algorithms encompass deterministic and stochastic approaches, each tailored to specific applications. Deterministic algorithms guarantee consistent outcomes for identical inputs, aiding in predictable and replicable problemsolving. In contrast, stochastic algorithms incorporate randomness to enhance exploration, avoiding local optima and enabling solutions to more intricate challenges. The paper delves into various categories of optimization techniques, emphasizing their significance in artificial intelligence (AI) and machine learning (ML). It highlights how these algorithms optimize resources, improve efficiency, and drive innovation. Metaheuristic algorithms, inspired by natural, physical, and human processes, are particularly notable for their adaptability and robust performance in solving real-world optimization problems. Techniques such as genetic algorithms, particle swarm optimization, and simulated annealing demonstrate their efficacy in diverse domains, including engineering, finance, and healthcare. Current trends indicate a growing focus on hybrid methods that combine strengths from multiple algorithms to enhance performance, scalability, and accuracy. The study also addresses challenges such as parameter tuning, convergence rates, and computational costs, proposing solutions to improve algorithmic robustness and application effectiveness. By providing a comprehensive overview of optimization algorithms, their behaviours, and applications, this paper serves as a valuable resource for researchers and practitioners seeking to understand and advance the field. It underscores the transformative role of optimization techniques in modern technological and industrial advancement s, bridging theoretical frameworks and practical implementations.