Applied Nature-Inspired Computing: Algorithms and Case Studies [[electronic resource] /] / edited by Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya
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| Titolo: |
Applied Nature-Inspired Computing: Algorithms and Case Studies [[electronic resource] /] / edited by Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya
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| Pubblicazione: | Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020 |
| Edizione: | 1st ed. 2020. |
| Descrizione fisica: | 1 online resource (281 pages) |
| Disciplina: | 006.38 |
| Soggetto topico: | Computational intelligence |
| Algorithms | |
| Computer science—Mathematics | |
| Computer simulation | |
| Computational Intelligence | |
| Algorithm Analysis and Problem Complexity | |
| Mathematics of Computing | |
| Simulation and Modeling | |
| Persona (resp. second.): | DeyNilanjan |
| AshourAmira S | |
| BhattacharyyaSiddhartha | |
| Nota di contenuto: | 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. |
| Sommario/riassunto: | 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. |
| Titolo autorizzato: | Applied Nature-Inspired Computing: Algorithms and Case Studies |
| ISBN: | 981-13-9263-3 |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 9910767546503321 |
| Lo trovi qui: | Univ. Federico II |
| Opac: | Controlla la disponibilità qui |
Serie:
Springer Tracts in Nature-Inspired Computing, . 2524-552X