Swarm Intelligence and Bio-Inspired Computation : Theory and Applications

Yang, Xin-she (EDT); Cui, Zhihua (EDT); Xiao, Renbin (EDT); Gandomi, Amir Hossein (EDT); Karamanoglu, Mehmet (EDT)

ISBN 10: 0124051634 / ISBN 13: 9780124051638

Edité par Elsevier, 2013

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N° de réf. du vendeur 19493704-n

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Titre : Swarm Intelligence and Bio-Inspired ...

Éditeur : Elsevier

Date d'édition : 2013

Reliure : Couverture rigide

Etat : New

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Synopsis :

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers.

À propos des auteurs:

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).

Amir H. Gandomi, PhD, is a leading researcher in global optimization and big data analytics, currently serving as a Professor of Data Science and an ARC DECRA Fellow at the University of Technology Sydney (UTS). With over 450 journal publications and 60,000 citations, he is among the most cited researchers worldwide. Dr. Gandomi has authored 14 books and received numerous accolades, including the IEEE TCSC Award and the Achenbach Medal. His editorial roles span several prestigious journals, and he is a sought-after keynote speaker in the fields of artificial intelligence and genetic programming. Previously, he held academic positions at the Stevens Institute of Technology and Michigan State University, where he contributed significantly to advancing knowledge in machine learning and evolutionary computation.