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Metaheuristic Optimization Algorithms

Optimizers, Analysis, and Applications
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Description

Metaheuristic Optimization Algorithms: Optimizers, Analysis, and Applications presents the most recent optimization algorithms and their applications across a wide range of scientific and engineering research fields. The book provides readers with a comprehensive overview of eighteen optimization algorithms to address this complex data, including Particle Swarm Optimization Algorithm, Arithmetic Optimization Algorithm, Whale Optimization Algorithm, and Marine Predators Algorithm, along with new and emerging methods such as Aquila Optimizer, Quantum Approximate Optimization Algorithm, Manta-Ray Foraging Optimization Algorithm, and Gradient Based Optimizer, among others. Each chapter includes an introduction to the modeling concepts used to create the algorithm that is followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies.

Author Biography:

Dr. Laith Abualigah is an Associate Professor at Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Jordan. He is also a distinguished researcher at the School of Computer Science, Universiti Sains Malaysia. His main research interests focus on Arithmetic Optimization Algorithms (AOA), Bio-inspired Computing, Nature-inspired Computing, Swarm Intelligence, Artificial Intelligence, Meta-heuristic Modeling, as well as Optimization Algorithms, Evolutionary Computations, Information Retrieval, Text Clustering, Feature Selection, Combinatorial Problems, Optimization, Advanced Machine Learning, Big Data, and Natural Language Processing. Dr. Abualigah currently serves as Associate Editor of the Journal of Cluster Computing (Springer), the Journal of Soft Computing (Springer), and Journal of King Saud University - Computer and Information Sciences (Elsevier).
Release date Australia
May 8th, 2024
Contributor
  • Edited by Laith Abualigah
Pages
250
Audience
  • Professional & Vocational
ISBN-13
9780443139253
Product ID
36838487

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