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Record Nr. |
UNINA9910254330003321 |
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Titolo |
Nature-Inspired Computing and Optimization : Theory and Applications / / edited by Srikanta Patnaik, Xin-She Yang, Kazumi Nakamatsu |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
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Edizione |
[1st ed. 2017.] |
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Descrizione fisica |
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1 online resource (XXI, 494 p. 191 illus., 43 illus. in color.) |
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Collana |
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Modeling and Optimization in Science and Technologies, , 2196-7326 ; ; 10 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Mathematical optimization |
Artificial intelligence |
Computer simulation |
Engineering economy |
Computational Intelligence |
Optimization |
Artificial Intelligence |
Simulation and Modeling |
Engineering Economics, Organization, Logistics, Marketing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references at the end of each chapters. |
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Nota di contenuto |
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From the content: The Nature of Nature: Why Nature Inspired Algorithms Work -- Improved Bat Algorithm in Noise-Free and Noisy Environments -- Multi-objective Ant Colony Optimisation in Wireless Sensor Networks.le. |
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Sommario/riassunto |
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The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and |
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gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals. |
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