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Nature-Inspired Optimization Algorithms

  • 2nd book:metaData.edition - September 9, 2020
  • book:metaData.latestEdition
  • common:contributors.author Xin-She Yang
  • publicationLanguages:language

Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balanc… seeMoreDescription

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Nature-Inspired Optimization Algorithms, Second Edition provides an introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, and multi-objective optimization. This book can serve as an introductory book for graduates, for lecturers in computer science, engineering and natural sciences, and as a source of inspiration for new applications.

promoMetaData.keyFeatures

  • Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literature
  • Provides a theoretical understanding and practical implementation hints
  • Presents a step-by-step introduction to each algorithm
  • Includes four new chapters covering mathematical foundations, techniques for solving discrete and combination optimization problems, data mining techniques and their links to optimization algorithms, and the latest deep learning techniques, background and various applications

promoMetaData.readership

Graduates, PhD students and lecturers in computer science, engineering and natural sciences and also researchers and biomedical engineers

promoMetaData.tableOfContents

1. Introduction to Algorithms 2. Mathematical Foundations3. Analysis of Algorithms4. Random Walks and Optimization5. Simulated Annealing6. Genetic Algorithms7. Differential Evolution8. Particle Swarm Optimization9. Firefly Algorithms10. Cuckoo Search11. Bat Algorithms12. Flower Pollination Algorithms13. A Framework for Self-Tuning Algorithms14. How to Deal With Constraints15. Multi-Objective Optimization16. Data Mining and Deep LearningAppendix A Test Function Benchmarks for Global OptimizationAppendix B Matlab® Programs

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  • productDetails.edition: 2
  • book:metaData.latestEdition
  • productDetails.published: September 14, 2020
  • publicationLanguages:languageTitle: publicationLanguages:en

promoMetaData.aboutTheAuthor

XY

Xin-She Yang

Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader in Modelling and Simulation at Middlesex University London, Fellow of the Institute of Mathematics and its Application (IMA) and a Book Series Co-Editor of the Springer Tracts in Nature-Inspired Computing. He has published more than 25 books and more than 400 peer-reviewed research publications with over 82000 citations, and he has been on the prestigious list of highly cited researchers (Web of Sciences) for seven consecutive years (2016-2022).
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School of Science and Technology, Middlesex University, UK

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