Metaheuristic Optimization Algorithm

Metaheuristic optimization algorithms are computational techniques used to find near-optimal solutions for complex problems where exhaustive search is infeasible. Current research emphasizes adapting these algorithms for multi-objective optimization, particularly within machine learning (e.g., hyperparameter tuning) and engineering applications, often leveraging frameworks to streamline implementation and analysis. These methods are proving valuable across diverse fields, improving efficiency in areas such as scheduling, resource allocation, and the development of neural networks by providing robust and adaptable solutions to challenging optimization problems.