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Introduction to Nature-Inspired Optimization
A succinct introduction to key algorithms in the fast-developing field of nature-inspired algorithms
George Lindfield (Author), John Penny (Author)
9780128036365, Elsevier Science
Paperback, published 12 August 2017
256 pages, 75 illustrations
23.4 x 19 x 1.7 cm, 0.52 kg
"Besides being very useful to those who are interested in discrete optimizations problems and applying various nature-inspired metaheuristics to them, the involved reader can also benefit from comparative studies of algorithms highlighting their strengths and weaknesses. The book is written in a clean and easily uderstandable, but still highly scientific language and it is beneficial reading for post-docs and researchers working with MATLAB and interested in metaheuristic approaches to optimization problems." --Zentralblatt MATH
Introduction to Nature-Inspired Optimization brings together many of the innovative mathematical methods for non-linear optimization that have their origins in the way various species behave in order to optimize their chances of survival. The book describes each method, examines their strengths and weaknesses, and where appropriate, provides the MATLAB code to give practical insight into the detailed structure of these methods and how they work. Nature-inspired algorithms emulate processes that are found in the natural world, spurring interest for optimization. Lindfield/Penny provide concise coverage to all the major algorithms, including genetic algorithms, artificial bee colony algorithms, ant colony optimization and the cuckoo search algorithm, among others. This book provides a quick reference to practicing engineers, researchers and graduate students who work in the field of optimization.
1. Introduction2. Genetic algorithms (GAs).3. Artificial bee colony (ABC) algorithm 4. The bat algorithm.5. Strawberry optimization algorithm6. Ant colony optimization (ACO)7. Cuckoo search algorithm8. Other algorithms and hybrid algorithms9. General comparison of the nature of the methods
