The Resource Applied nature-inspired computing : algorithms and case studies, Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors
Applied nature-inspired computing : algorithms and case studies, Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors
Resource Information
The item Applied nature-inspired computing : algorithms and case studies, Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Applied nature-inspired computing : algorithms and case studies, Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.
This item is available to borrow from 1 library branch.
- 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
- Language
- eng
- Extent
- 1 online resource (281 pages)
- Note
- 5 Conclusion
- Contents
-
- Intro; Preface; Contents; About the Editors; Particle Swarm Optimization of Morphological Filters for Electrocardiogram Baseline Drift Estimation; 1 Introduction; 2 Morphological Filters; 2.1 Morphological Dilation and Erosion; 2.2 Morphological Opening and Closing; 3 Morphological ECG Baseline Estimation; 3.1 One-Stage Morphological ECG Baseline Estimation; 3.2 Two-Stage Morphological ECG Baseline Estimation; 4 Particle Swarm Optimization; 4.1 Strategy of PSO; 4.2 General Communication Strategy by Taking Two Human Particles; 4.3 Structure of Artificial Particle
- 5 Particle Swarm Optimized (PSO) Morphological ECG Baseline Estimation5.1 The Objective of the Optimization; 5.2 Two-Stage PSO Optimization; 6 Results and Discussion; 7 Conclusion; References; 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 Classifier; 1 Introduction; 2 Related Studies; 3 Materials and Methods; 3.1 Preprocessing and Segmentation; 3.2 Feature Extraction; 3.3 Feature Fusion; 3.4 Optimum-Path Forest Classifier; 3.5 Binary Firefly Algorithm Based Feature Selection
- 3.6 Classification4 Results and Discussions; 5 Conclusion; References; Recommending Healthy Personalized Daily Menus-A Cuckoo Search-Based Hyper-Heuristic Approach; 1 Introduction; 2 Related Work; 3 Problem Formulation; 3.1 Solution of the Optimization Problem; 3.2 Fitness Function; 4 Cuckoo Search Based Hyper-Heuristic; 4.1 Overview of Cuckoo Search; 4.2 Cuckoo Search Based Hyper-Heuristic Algorithm; 5 Performance Evaluation; 5.1 System Architecture; 5.2 Experimental Setup; 5.3 Experimental Results; 5.4 Comparative Evaluation; 6 Conclusions and Future Work; References
- A Hybrid Bat-Inspired Algorithm for Power Transmission Expansion Planning on a Practical Brazilian Network1 Introduction; 1.1 Contributions; 1.2 Organization; 2 Background and Related Works; 2.1 Bat Algorithm Approaches; 2.2 Additional Methods for Optimizing the Metaheuristics' Algorithm; 2.3 Southern System; 3 Problem Formulation; 3.1 TEP Formulation; 3.2 The Decomposition Problem; 3.3 The Proposed Search-Space Shrinking (SSS); 3.4 The Proposed Adapted Bat Algorithm (ABA); 4 Results and Discussion; 5 Discussion and Conclusions; 5.1 Why Adapted Bat-Inspired Algorithm Is Efficient
- 5.2 Further Research TopicsReferences; An Application of Binary Grey Wolf Optimizer (BGWO) Variants for Unit Commitment Problem; 1 Introduction; 1.1 Motivation; 1.2 Related Work; 2 Problem Formulation; 2.1 Objective Function; 2.2 System Constraints; 2.3 Thermal Unit Constraints; 3 Solution Methodology; 3.1 Overview of GWO; 3.2 Proposed BGWO Approaches; 3.3 BGWO Implementation to Solve UCP; 3.4 Constraint Repair; 4 Results and Discussion; 4.1 Numerical Results and Discussion:Test System 1; 4.2 Numerical Results and Discussion: Test System 2; 4.3 Numerical Results and Discussion: Test System 3
- Isbn
- 9789811392634
- Label
- Applied nature-inspired computing : algorithms and case studies
- Title
- Applied nature-inspired computing
- Title remainder
- algorithms and case studies
- Statement of responsibility
- Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors
- Language
- eng
- 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
- Cataloging source
- EBLCP
- Dewey number
- 006.3/82
- Index
- no index present
- LC call number
- QA76.9.N37
- Literary form
- non fiction
- Nature of contents
- dictionaries
- http://library.link/vocab/relatedWorkOrContributorDate
-
- 1984-
- 1975-
- 1975-
- http://library.link/vocab/relatedWorkOrContributorName
-
- Dey, Nilanjan
- Ashour, Amira
- Bhattacharyya, Siddhartha
- Series statement
- Springer Tracts in Nature-Inspired Computing Ser
- http://library.link/vocab/subjectName
- Natural computation
- Label
- Applied nature-inspired computing : algorithms and case studies, Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors
- Note
- 5 Conclusion
- Antecedent source
- file reproduced from an electronic resource
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
-
- Intro; Preface; Contents; About the Editors; Particle Swarm Optimization of Morphological Filters for Electrocardiogram Baseline Drift Estimation; 1 Introduction; 2 Morphological Filters; 2.1 Morphological Dilation and Erosion; 2.2 Morphological Opening and Closing; 3 Morphological ECG Baseline Estimation; 3.1 One-Stage Morphological ECG Baseline Estimation; 3.2 Two-Stage Morphological ECG Baseline Estimation; 4 Particle Swarm Optimization; 4.1 Strategy of PSO; 4.2 General Communication Strategy by Taking Two Human Particles; 4.3 Structure of Artificial Particle
- 5 Particle Swarm Optimized (PSO) Morphological ECG Baseline Estimation5.1 The Objective of the Optimization; 5.2 Two-Stage PSO Optimization; 6 Results and Discussion; 7 Conclusion; References; 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 Classifier; 1 Introduction; 2 Related Studies; 3 Materials and Methods; 3.1 Preprocessing and Segmentation; 3.2 Feature Extraction; 3.3 Feature Fusion; 3.4 Optimum-Path Forest Classifier; 3.5 Binary Firefly Algorithm Based Feature Selection
- 3.6 Classification4 Results and Discussions; 5 Conclusion; References; Recommending Healthy Personalized Daily Menus-A Cuckoo Search-Based Hyper-Heuristic Approach; 1 Introduction; 2 Related Work; 3 Problem Formulation; 3.1 Solution of the Optimization Problem; 3.2 Fitness Function; 4 Cuckoo Search Based Hyper-Heuristic; 4.1 Overview of Cuckoo Search; 4.2 Cuckoo Search Based Hyper-Heuristic Algorithm; 5 Performance Evaluation; 5.1 System Architecture; 5.2 Experimental Setup; 5.3 Experimental Results; 5.4 Comparative Evaluation; 6 Conclusions and Future Work; References
- A Hybrid Bat-Inspired Algorithm for Power Transmission Expansion Planning on a Practical Brazilian Network1 Introduction; 1.1 Contributions; 1.2 Organization; 2 Background and Related Works; 2.1 Bat Algorithm Approaches; 2.2 Additional Methods for Optimizing the Metaheuristics' Algorithm; 2.3 Southern System; 3 Problem Formulation; 3.1 TEP Formulation; 3.2 The Decomposition Problem; 3.3 The Proposed Search-Space Shrinking (SSS); 3.4 The Proposed Adapted Bat Algorithm (ABA); 4 Results and Discussion; 5 Discussion and Conclusions; 5.1 Why Adapted Bat-Inspired Algorithm Is Efficient
- 5.2 Further Research TopicsReferences; An Application of Binary Grey Wolf Optimizer (BGWO) Variants for Unit Commitment Problem; 1 Introduction; 1.1 Motivation; 1.2 Related Work; 2 Problem Formulation; 2.1 Objective Function; 2.2 System Constraints; 2.3 Thermal Unit Constraints; 3 Solution Methodology; 3.1 Overview of GWO; 3.2 Proposed BGWO Approaches; 3.3 BGWO Implementation to Solve UCP; 3.4 Constraint Repair; 4 Results and Discussion; 4.1 Numerical Results and Discussion:Test System 1; 4.2 Numerical Results and Discussion: Test System 2; 4.3 Numerical Results and Discussion: Test System 3
- Dimensions
- unknown
- Extent
- 1 online resource (281 pages)
- File format
- one file format
- Form of item
- online
- Isbn
- 9789811392634
- Level of compression
- unknown
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-981-13-9
- Quality assurance targets
- unknown
- Reformatting quality
- unknown
- Specific material designation
- remote
- System control number
-
- on1112420923
- (OCoLC)1112420923
- Label
- Applied nature-inspired computing : algorithms and case studies, Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors
- Note
- 5 Conclusion
- Antecedent source
- file reproduced from an electronic resource
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
-
- Intro; Preface; Contents; About the Editors; Particle Swarm Optimization of Morphological Filters for Electrocardiogram Baseline Drift Estimation; 1 Introduction; 2 Morphological Filters; 2.1 Morphological Dilation and Erosion; 2.2 Morphological Opening and Closing; 3 Morphological ECG Baseline Estimation; 3.1 One-Stage Morphological ECG Baseline Estimation; 3.2 Two-Stage Morphological ECG Baseline Estimation; 4 Particle Swarm Optimization; 4.1 Strategy of PSO; 4.2 General Communication Strategy by Taking Two Human Particles; 4.3 Structure of Artificial Particle
- 5 Particle Swarm Optimized (PSO) Morphological ECG Baseline Estimation5.1 The Objective of the Optimization; 5.2 Two-Stage PSO Optimization; 6 Results and Discussion; 7 Conclusion; References; 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 Classifier; 1 Introduction; 2 Related Studies; 3 Materials and Methods; 3.1 Preprocessing and Segmentation; 3.2 Feature Extraction; 3.3 Feature Fusion; 3.4 Optimum-Path Forest Classifier; 3.5 Binary Firefly Algorithm Based Feature Selection
- 3.6 Classification4 Results and Discussions; 5 Conclusion; References; Recommending Healthy Personalized Daily Menus-A Cuckoo Search-Based Hyper-Heuristic Approach; 1 Introduction; 2 Related Work; 3 Problem Formulation; 3.1 Solution of the Optimization Problem; 3.2 Fitness Function; 4 Cuckoo Search Based Hyper-Heuristic; 4.1 Overview of Cuckoo Search; 4.2 Cuckoo Search Based Hyper-Heuristic Algorithm; 5 Performance Evaluation; 5.1 System Architecture; 5.2 Experimental Setup; 5.3 Experimental Results; 5.4 Comparative Evaluation; 6 Conclusions and Future Work; References
- A Hybrid Bat-Inspired Algorithm for Power Transmission Expansion Planning on a Practical Brazilian Network1 Introduction; 1.1 Contributions; 1.2 Organization; 2 Background and Related Works; 2.1 Bat Algorithm Approaches; 2.2 Additional Methods for Optimizing the Metaheuristics' Algorithm; 2.3 Southern System; 3 Problem Formulation; 3.1 TEP Formulation; 3.2 The Decomposition Problem; 3.3 The Proposed Search-Space Shrinking (SSS); 3.4 The Proposed Adapted Bat Algorithm (ABA); 4 Results and Discussion; 5 Discussion and Conclusions; 5.1 Why Adapted Bat-Inspired Algorithm Is Efficient
- 5.2 Further Research TopicsReferences; An Application of Binary Grey Wolf Optimizer (BGWO) Variants for Unit Commitment Problem; 1 Introduction; 1.1 Motivation; 1.2 Related Work; 2 Problem Formulation; 2.1 Objective Function; 2.2 System Constraints; 2.3 Thermal Unit Constraints; 3 Solution Methodology; 3.1 Overview of GWO; 3.2 Proposed BGWO Approaches; 3.3 BGWO Implementation to Solve UCP; 3.4 Constraint Repair; 4 Results and Discussion; 4.1 Numerical Results and Discussion:Test System 1; 4.2 Numerical Results and Discussion: Test System 2; 4.3 Numerical Results and Discussion: Test System 3
- Dimensions
- unknown
- Extent
- 1 online resource (281 pages)
- File format
- one file format
- Form of item
- online
- Isbn
- 9789811392634
- Level of compression
- unknown
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-981-13-9
- Quality assurance targets
- unknown
- Reformatting quality
- unknown
- Specific material designation
- remote
- System control number
-
- on1112420923
- (OCoLC)1112420923
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<div class="citation" vocab="http://schema.org/"><i class="fa fa-external-link-square fa-fw"></i> Data from <span resource="http://link.liverpool.ac.uk/portal/Applied-nature-inspired-computing--algorithms/xW-Y5gBGDjY/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.liverpool.ac.uk/portal/Applied-nature-inspired-computing--algorithms/xW-Y5gBGDjY/">Applied nature-inspired computing : algorithms and case studies, Nilanjan Dey, Amira S. Ashour, Siddhartha Bhattacharyya, editors</a></span> - <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.liverpool.ac.uk/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.liverpool.ac.uk/">Sydney Jones Library, University of Liverpool</a></span></span></span></span></div>
