Metaheuristics Developing Intelligent Solutions for Complex Optimization Problems
Farica Qureshi, Satyam Sharma, Rafiya Nazir · 2025
Metaheuristics are a category of optimization algorithms constructed to tackle complex real-world problems which traditional approaches struggle to address. This chapter analyzes the classification, application and evolution of metaheuristics algorithms in dealing with large-scale, NP-hard and nonlinear constraints. Metaheuristics, involving swarm intelligence, physics-based models, and evolutionary approaches, provide robust search framework by maintaining a balance between exploration and exploitation techniques. The discussion includes their contribution in biomedical image processing, finance, artificial intelligence and robotics. The study also investigates the computational problems of metaheuristics, focusing on parameter tuning, trade-offs in performance and process of hybridization with machine learning. The observations highlight the adaptive behaviour of metaheuristics in dynamic surroundings, offering solutions spanning various domains.