Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications
Π½ΠΎΠ΅. 2020β―Π³. Β· Springer Nature
Π-ΠΊΠ½ΠΈΠ³Π°
192
Π‘ΡΡΠ°Π½ΠΈΡΠΈ
reportΠΡΠ΅Π½ΠΈΡΠ΅ ΠΈ ΡΠ΅ΡΠ΅Π½Π·ΠΈΠΈΡΠ΅ Π½Π΅ ΡΠ΅ ΠΏΠΎΡΠ²ΡΠ΄Π΅Π½ΠΈ Β ΠΠΎΠ·Π½Π°ΡΡΠ΅ ΠΏΠΎΠ²Π΅ΡΠ΅
ΠΠ° Π΅-ΠΊΠ½ΠΈΠ³Π°Π²Π°
This book exemplifies how algorithms are developed by mimicking nature. Classical techniques for solving day-to-day problems is time-consuming and cannot address complex problems. Metaheuristic algorithms are nature-inspired optimization techniques for solving real-life complex problems. This book emphasizes the social behaviour of insects, animals and other natural entities, in terms of converging power and benefits. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm, whale optimization algorithm, firefly algorithm, bat algorithm, ant lion optimization algorithm, grasshopper optimization algorithm, butterfly optimization algorithm and others. The algorithms have been arranged in chapters to help readers gain better insight into nature-inspired systems and swarm intelligence. All the MATLAB codes have been provided in the appendices of the book to enable readers practice how to solve examplesincluded in all sections. This book is for experts in Engineering and Applied Sciences, Natural and Formal Sciences, Economics, Humanities and Social Sciences.
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