Metaheuristic Optimization Algorithms Optimizers, Analysis, and Applications by Laith Abualigah PDF

Metaheuristic Optimization Algorithms Optimizers, Analysis, and Applications by Laith Abualigah PDF | 5.43 MB
291 Pages

Title: Metaheuristic Optimization Algorithms
Author: Laith Abualigah;

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. Metaheuristic Optimization Algorithms have become indispensable tools, with applications in data analysis, text mining, classification problems, computer vision, image analysis, pattern recognition, medicine, and many others. Most complex systems problems involve a continuous flow of data that makes it impossible to manage and analyze manually. The outcome depends on the processing of high-dimensional data, most of it irregular and unordered, present in various forms such as text, images, videos, audio, and graphics. The authors of Meta-Heuristic Optimization Algorithms provide 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, followed by the mathematical and procedural structure of the algorithm, associated pseudocode, and real-world case studies to demonstrate how each algorithm can be applied to a variety of scientific and engineering solutions.

  • World-renowned researchers and practitioners in Metaheuristics present the procedures and pseudocode for creating a wide range of optimization algorithms
  • Helps readers formulate and design the best optimization algorithms for their research goals through case studies in a variety of real-world applications
  • Helps readers understand the links between Metaheuristic algorithms and their application in Computational Intelligence, Machine Learning, and Deep Learning problems


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