Applied nature-inspired computing: algorithms and case studies:
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 optimi...
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| Other Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Singapore, Singapore
Springer
[2020]
|
| Series: | Springer tracts in nature-inspired computing
|
| Subjects: | |
| Summary: | 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 |
| Item Description: | 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. |
| Physical Description: | xii, 275 Seiten Illustrationen, Diagramme 444 grams |
| ISBN: | 9789811392658 |
Staff View
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| 245 | 1 | 0 | |a Applied nature-inspired computing: algorithms and case studies |c Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors |
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| 500 | |a 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. | ||
| 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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| id | DE-604.BV046916464 |
| illustrated | Illustrated |
| index_date | 2024-07-03T15:29:44Z |
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| institution | BVB |
| isbn | 9789811392658 |
| language | English |
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| physical | xii, 275 Seiten Illustrationen, Diagramme 444 grams |
| publishDate | 2020 |
| publishDateSearch | 2020 |
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| publisher | Springer |
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| series2 | Springer tracts in nature-inspired computing |
| spelling | Applied nature-inspired computing: algorithms and case studies Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors Singapore, Singapore Springer [2020] xii, 275 Seiten Illustrationen, Diagramme 444 grams txt rdacontent n rdamedia nc rdacarrier 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 bicssc bisacsh Computational intelligence Algorithms Computer science—Mathematics Computer simulation Engineering Hardcover, Softcover / Technik/Allgemeines, Lexika Dey, Nilanjan 1984- (DE-588)1104273500 edt Ashour, Amira 1975- (DE-588)1127237292 edt Bhattacharyya, Siddhartha 1975- Sonstige (DE-588)1079224548 oth Erscheint auch als Online-Ausgabe 978-981-13-9263-4 |
| spellingShingle | Applied nature-inspired computing: algorithms and case studies bicssc bisacsh Computational intelligence Algorithms Computer science—Mathematics Computer simulation Engineering |
| title | Applied nature-inspired computing: algorithms and case studies |
| title_auth | Applied nature-inspired computing: algorithms and case studies |
| title_exact_search | Applied nature-inspired computing: algorithms and case studies |
| title_exact_search_txtP | Applied nature-inspired computing: algorithms and case studies |
| title_full | Applied nature-inspired computing: algorithms and case studies Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors |
| title_fullStr | Applied nature-inspired computing: algorithms and case studies Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors |
| title_full_unstemmed | Applied nature-inspired computing: algorithms and case studies Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors |
| title_short | Applied nature-inspired computing: algorithms and case studies |
| title_sort | applied nature inspired computing algorithms and case studies |
| topic | bicssc bisacsh Computational intelligence Algorithms Computer science—Mathematics Computer simulation Engineering |
| topic_facet | bicssc bisacsh Computational intelligence Algorithms Computer science—Mathematics Computer simulation Engineering |
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