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Applied nature-inspired computingalg...

Ashour, Amira S.

Applied nature-inspired computingalgorithms and case studies /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Applied nature-inspired computingedited by Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya.
其他題名:	algorithms and case studies /
其他作者:	Dey, Nilanjan.
出版者:	Singapore :Springer Singapore :2020.
面頁冊數:	xii, 275 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Natural computation.
電子資源:	https://doi.org/10.1007/978-981-13-9263-4
ISBN:	9789811392634$q(electronic bk.)

Applied nature-inspired computingalgorithms and case studies /
Applied nature-inspired computingalgorithms and case studies /[electronic resource] :edited by Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya. - Singapore :Springer Singapore :2020. - xii, 275 p. :ill., digital ;24 cm. - Springer tracts in nature-inspired computing,2524-552X. - Springer tracts in nature-inspired computing..

Chapter 1. Particle Swarm Optimization of Morphological Filters for Electrocardiogram Baseline Drift Estimation -- Chapter 2. Detection of Breast Cancer using Fusion of MLO and CC View Features Through a Hybrid Technique Based on Binary Firefly algorithm and Optimum Path Forest Classification -- Chapter 3. Recommending Healthy Personalized Daily Menus - A Cuckoo Search based Hyper-Heuristic Approach -- Chapter 4. A Hybrid Bat-Inspired Algorithm for Power Transmission Expansion Planning on a Practical Brazilian Network -- Chapter 5. An Application of Binary Grey Wolf Optimizer (BGWO) variants for Unit Commitment Problem -- Chapter 6. Sensorineural hearing loss identification via discrete wavelet packet entropy and cat swarm optimization -- Chapter 7. Chaotic Variants of Grasshopper Optimisation Algorithm and their application to Protein Structure Prediction -- Chapter 8. Examination of Retinal Anatomical Structures - A Study with Spider Monkey Optimization Algorithm -- Chapter 9. Nature-Inspired Metaheuristics Search Algorithms for Solving the Economic Load Dispatch Problem of Power System: A Comparative Study -- Chapter 10. Parallel-series System Optimization by Weighting Sum Methods and Nature-inspired Computing -- Chapter 11. Development of Artificial Neural Networks trained by Heuristic Algorithms for Prediction of Exhaust Emissions and Performance of a Diesel Engine Fuelled with Biodiesel Blends.

This book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. It introduces readers to a broad range of algorithms, such as genetic algorithms, particle swarm optimization, the firefly algorithm, flower pollination algorithm, collision-based optimization algorithm, bat algorithm, ant colony optimization, and multi-agent systems. In turn, it provides an overview of meta-heuristic algorithms, comparing the advantages and disadvantages of each. Moreover, the book provides a brief outline of the integration of nature-inspired computing techniques and various computational intelligence paradigms, and highlights nature-inspired computing techniques in a range of applications, including: evolutionary robotics, sports training planning, assessment of water distribution systems, flood simulation and forecasting, traffic control, gene expression analysis, antenna array design, and scheduling/dynamic resource management.

ISBN: 9789811392634$q(electronic bk.)

Standard No.: 10.1007/978-981-13-9263-4doiSubjects--Topical Terms:

339160
Natural computation.

LC Class. No.: QA76.9.N37

Dewey Class. No.: 006.382

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520 $a This book presents a cutting-edge research procedure in the Nature-Inspired Computing (NIC) domain and its connections with computational intelligence areas in real-world engineering applications. It introduces readers to a broad range of algorithms, such as genetic algorithms, particle swarm optimization, the firefly algorithm, flower pollination algorithm, collision-based optimization algorithm, bat algorithm, ant colony optimization, and multi-agent systems. In turn, it provides an overview of meta-heuristic algorithms, comparing the advantages and disadvantages of each. Moreover, the book provides a brief outline of the integration of nature-inspired computing techniques and various computational intelligence paradigms, and highlights nature-inspired computing techniques in a range of applications, including: evolutionary robotics, sports training planning, assessment of water distribution systems, flood simulation and forecasting, traffic control, gene expression analysis, antenna array design, and scheduling/dynamic resource management.
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