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Metaheuristics Developing Intelligent Solutions for Complex Optimization Problems
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Author(s): Farica Qureshi (GNA University, Phagwara, India), Satyam Sharma (GNA University, Phagwara, India)and Rafiya Nazir (GNA University, Phagwara, India)
Copyright: 2026
Pages: 30
Source title:
Metaheuristic Algorithms and Optimizing Neural Networks for Biomedical Image Processing
Source Author(s)/Editor(s): Prasanalakshmi Balaji (King Khalid University, Saudi Arabia), K. Martin Sagayam (Karunya Institute of Technology and Sciences, India), Aditi Sharma (Symbiosis International University, India)and Korhen Cengiz (University of Fujairah, UAE)
DOI: 10.4018/979-8-3373-0523-3.ch002
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Abstract
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.
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