Metaheuristic Optimization Algorithms

Optimizers, Analysis, and Applications

Laith Abualigah

• Publisher: Morgan Kaufmann
• Year: 2024
• Print ISBN:
  9780443139253
• eBook ISBN:
  9780443139260
• Topic: Computer Science
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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

Table of contents

• Title of Book
• Cover image
• Title page
• Table of Contents
• Copyright
• List of contributors
• 1 Particle swarm optimization algorithm: review and applications
• 2 Social spider optimization algorithm: survey and new applications
• 3 Animal migration optimization algorithm: novel optimizer, analysis, and applications
• 4 A Survey of cuckoo search algorithm: optimizer and new applications
• 5 Teaching–learning-based optimization algorithm: analysis study and its application
• 6 Arithmetic optimization algorithm: a review and analysis
• 7 Aquila optimizer: review, results and applications
• 8 Whale optimization algorithm: analysis and full survey
• 9 Spider monkey optimizations: application review and results
• 10 Marine predator’s algorithm: a survey of recent applications
• 11 Quantum approximate optimization algorithm: a review study and problems
• 12 Crow search algorithm: a survey of novel optimizer and its recent applications
• 13 A review of Henry gas solubility optimization algorithm: a robust optimizer and applications
• 14 A survey of the manta ray foraging optimization algorithm
• 15 A review of mothflame optimization algorithm: analysis and applications
• 16 Gradient-based optimizer: analysis and application of the Berry software product
• 17 A review of krill herd algorithm: optimization and its applications
• 18 Salp swarm algorithm: survey, analysis, and new applications
• Index