UID:
almahu_9948274959202882
Format:
XIII, 389 p. 194 illus., 138 illus. in color.
,
online resource.
Edition:
1st ed. 2020.
ISBN:
9789811521331
Series Statement:
Springer Tracts in Nature-Inspired Computing,
Content:
This book addresses the frontier advances in the theory and application of nature-inspired optimization techniques, including solving the quadratic assignment problem, prediction in nature-inspired dynamic optimization, the lion algorithm and its applications, optimizing the operation scheduling of microgrids, PID controllers for two-legged robots, optimizing crane operating times, planning electrical energy distribution systems, automatic design and evaluation of classification pipelines, and optimizing wind-energy power generation plants. The book also presents a variety of nature-inspired methods and illustrates methods of adapting these to said applications. Nature-inspired computation, developed by mimicking natural phenomena, makes a significant contribution toward the solution of non-convex optimization problems that normal mathematical optimizers fail to solve. As such, a wide range of nature-inspired computing approaches has been used in multidisciplinary engineering applications. Written by researchers and developers from a variety of fields, this book presents the latest findings, novel techniques and pioneering applications.
Note:
Nature-inspired Metaheuristic Optimization: Recent Advances and Applications -- Prediction in Nature Inspired Dynamic Optimization -- Plants Genetics Inspired Evolutionary Optimization: A Descriptive Tutorial -- Trends on fitness landscape analysis in evolutionary computation and meta-heuristics -- Lion Algorithm and its Applications -- A self-adaptive nature-inspired procedure for solving the quadratic assignment problem.
In:
Springer eBooks
Additional Edition:
Printed edition: ISBN 9789811521324
Additional Edition:
Printed edition: ISBN 9789811521348
Additional Edition:
Printed edition: ISBN 9789811521355
Language:
English
DOI:
10.1007/978-981-15-2133-1
URL:
https://doi.org/10.1007/978-981-15-2133-1
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