Empowering Intelligent Systems: A Comprehensive Review of Modern Optimization Techniques and Real-World Applications

Abstract

Optimization techniques are fundamental enablers of modern intelligent systems. They are pivotal in many different applications. This paper presents a comprehensive and comparative review of key optimization algorithms—including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Gradient Descent, and the advanced Gaining Sharing Knowledge (GSK) model. The study critically analyses their design principles, convergence behaviour, adaptability, and parameter sensitivity. Leveraging fifty peer-reviewed real-world case studies published between 2020 and 2024, the review demonstrates how these techniques have been effectively applied across smart grid control, machine learning model tuning, intelligent healthcare, and logistics optimization. A comparative table summarizes algorithmic performance across five key dimensions, revealing practical trade-offs and domain-specific suitability. The study also identifies major challenges such as scalability, real-time adaptation, and explainability. In response, it outlines promising future directions including hybrid adaptive frameworks, quantum-inspired search strategies, and context-aware intelligent optimization. This review aims to guide researchers and practitioners in selecting, adapting, and deploying robust optimization strategies aligned with the complex demands of next-generation intelligent environments.