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Handbook of nature-inspired optimization algorithms . Volume II Solving contrained single objective real-parameter optimization problems : the state of the art / / Ali Wagdy Mohamed, Diego Oliva, Ponnuthurai Nagaratnam Suganthan, editors
| Handbook of nature-inspired optimization algorithms . Volume II Solving contrained single objective real-parameter optimization problems : the state of the art / / Ali Wagdy Mohamed, Diego Oliva, Ponnuthurai Nagaratnam Suganthan, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (220 pages) |
| Disciplina | 519.3 |
| Collana | Studies in systems, decision and control |
| Soggetto topico |
Mathematical optimization
Nature-inspired algorithms |
| ISBN | 3-031-07516-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- Particle Swarm Optimization Based Optimization for Industry Inspection -- 1 Introduction -- 2 Image Acquisition, Feature Extraction and Processing -- 2.1 Gabor Feature Extraction and Image Processing -- 3 Experimental Process and Results -- 3.1 Effect with Different Iterations of Parameter Optimization Using PSO -- 4 Conclusion -- References -- Ant Algorithms: From Drawback Identification to Quality and Speed Improvement -- 1 Introduction -- 2 Classic Ant Algorithms -- 3 Ant Algorithms and Truck Loading -- 4 Ant Algorithms and Job Scheduling -- 5 Ant Algorithms and Graph Coloring -- 6 Conclusion -- References -- Fault Location Techniques Based on Traveling Waves with Application in the Protection of Distribution Systems with Renewable Energy and Particle Swarm Optimization -- 1 Introduction -- 2 Basic Principle of Fault Determination -- 2.1 Fault Section Determination -- 3 Determination of the Interphase Short Circuit Fault Location -- 4 Traveling Waves -- 4.1 Wavelet Transform -- 5 Fundamentals of Particle Swarm Optimization -- 6 Determination of Fault Location for Electric Power Distribution Networks with DGs -- 6.1 Dynamic Protection Suitable for the High Incorporation of Renewable Energy Sources into the Electric Energy Distribution System -- 6.2 Microgrid Protection with High Incorporation of Renewable Energies -- 7 Applicability of Dynamic Protection Based on Traveling Waves in Electrical Systems with High Incorporation of Renewable Energy Sources -- 8 Particle Swarm Optimization Algorithm -- 9 Future Applications -- 10 Conclusion -- References -- Improved Particle Swarm Optimization and Non-quadratic Penalty Method for Non-linear Programming Problems with Equality Constraints -- 1 Introduction -- 1.1 Penalty Function Method -- 1.2 Penalty Function -- 2 Particle Swarm Optimization -- 2.1 Improvements in PSO.
3 Methodology -- 4 Parameter Setup -- 5 Experimental Setup -- 6 Conclusion -- 7 Future Work -- References -- Recent Trends in Face Recognition Using Metaheuristic Optimization -- 1 Introduction -- 2 Metaheuristic Optimization Approaches -- 2.1 Genetic Algorithms (GAs) -- 2.2 Particle Swarm Optimization (PSO) -- 2.3 Ant Colony Optimization (ACO) -- 2.4 Bacterial Foraging Optimization Algorithm (BFOA) -- 2.5 Firefly Algorithm (FA) -- 3 Biometrics Modalities -- 3.1 Face Recognition System -- 3.2 Performance Metrics -- 4 Face Recognition Based Optimization Approaches -- 5 Databases and Discussion -- 5.1 Databases -- 5.2 Discussion -- 5.3 Future Trends -- 6 Conclusion -- References -- Chaos Game Optimization Algorithm with Crossover Operator for Solving Constraint Engineering Optimization Problems -- 1 Introduction -- 2 Chaos Game Optimization (CGO) -- 3 Crossover Based Chaos Game Optimization (CrCGO) -- 4 Numerical Results -- 4.1 Benchmark Optimization Problems -- 4.2 Contender Methods -- 4.3 Evaluation Criteria -- 4.4 Non-parametric Statistical Tests -- 4.5 Results and Discussions -- 5 Conclusion -- References -- UAV-Assisted IoT Data Collection Optimization Using Gaining-Sharing Knowledge Algorithm -- 1 Introduction -- 2 System Model and Problem Formulation -- 3 Gaining-sharing Knowledge (GSK) Algorithm -- 4 Results and Discussion -- 5 Conclusion -- References -- Energy Aware Tikhonov-Regularized FPA Technique for Task Scheduling in Wearable Biomedical Devices -- 1 Introduction -- 2 Wearable Biomedical System Overview -- 3 Problem Formulation -- 3.1 Objective Function -- 3.2 Tikhonov Regularization and Operation Constraints -- 3.3 Flower Pollination Optimization Algorithm -- 4 Experiments and Discussion -- 4.1 Energy Consumption Profiling -- 4.2 Parameters Setup and Trials -- 5 Conclusion -- References. Material Generation Algorithm Combined with Epsilon Constraint Handling Scheme for Engineering Optimization -- 1 Introduction -- 2 Material Generation Algorithm -- 3 Problem Statement -- 3.1 Constrained Optimization -- 3.2 Constraint Handling -- 4 Engineering Design Problems of CEC 2020 -- 5 Numerical Investigations -- 6 Conclusion -- References -- Optimum Design of Truss Structures with Atomic Orbital Search Considering Discrete Design Variables -- 1 Introduction -- 2 Atomic Orbital Search -- 3 Problem Statement -- 4 Numerical Investigations -- 4.1 10-Bar Truss Structure -- 4.2 25-Bar Truss Structure -- 4.3 52-Bar Truss Structure -- 4.4 160-Bar Truss Structure -- 5 Conclusion -- References. |
| Record Nr. | UNINA-9910591038103321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Handbook of nature-inspired optimization algorithms . Volume I Solving single objective bound-constrained real-parameter numerical optimization problems : the state of the art / / Ali Mohamed, Diego Oliva and Ponnuthurai Nagaratnam, editors
| Handbook of nature-inspired optimization algorithms . Volume I Solving single objective bound-constrained real-parameter numerical optimization problems : the state of the art / / Ali Mohamed, Diego Oliva and Ponnuthurai Nagaratnam, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (282 pages) |
| Disciplina | 519.3 |
| Collana | Studies in Systems, Decision and Control |
| Soggetto topico |
Mathematical optimization
Nature-inspired algorithms |
| ISBN | 3-031-07512-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910590081903321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Nature inspired algorithms and their applications / / editors, S. Balamurugan [et al.]
| Nature inspired algorithms and their applications / / editors, S. Balamurugan [et al.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc. : , : Scrivener Publishing, , [2022] |
| Descrizione fisica | 1 online resource (384 pages) |
| Disciplina | 571.0284 |
| Soggetto topico | Nature-inspired algorithms |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-68166-9
1-119-68198-7 1-119-68199-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Introduction to Nature-Inspired Computing -- 1.1 Introduction -- 1.2 Aspiration From Nature -- 1.3 Working of Nature -- 1.4 Nature-Inspired Computing -- 1.4.1 Autonomous Entity -- 1.5 General Stochastic Process of Nature-Inspired Computation -- 1.5.1 NIC Categorization -- 1.5.1.1 Bioinspired Algorithm -- 1.5.1.2 Swarm Intelligence -- 1.5.1.3 Physical Algorithms -- 1.5.1.4 Familiar NIC Algorithms -- References -- 2 Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning -- 2.1 Introduction of Genetic Algorithm -- 2.1.1 Background of GA -- 2.1.2 Why Natural Selection Theory Compared With the Search Heuristic Algorithm? -- 2.1.3 Working Sequence of Genetic Algorithm -- 2.1.3.1 Population -- 2.1.3.2 Fitness Among the Individuals -- 2.1.3.3 Selection of Fitted Individuals -- 2.1.3.4 Crossover Point -- 2.1.3.5 Mutation -- 2.1.4 Application of Machine Learning in GA -- 2.1.4.1 Genetic Algorithm Role in Feature Selection for ML Problem -- 2.1.4.2 Traveling Salesman Problem -- 2.1.4.3 Blackjack-A Casino Game -- 2.1.4.4 Pong Against AI-Evolving Agents (Reinforcement Learning) Using GA -- 2.1.4.5 SNAKE AI-Game -- 2.1.4.6 Genetic Algorithm's Role in Neural Network -- 2.1.4.7 Solving a Battleship Board Game as an Optimization Problem Which Was Initially Released by Milton Bradley in 1967 -- 2.1.4.8 Frozen Lake Problem From OpenAI Gym -- 2.1.4.9 N-Queen Problem -- 2.1.5 Application of Data Mining in GA -- 2.1.5.1 Association Rules Generation -- 2.1.5.2 Pattern Classification With Genetic Algorithm -- 2.1.5.3 Genetic Algorithms in Stock Market Data Mining Optimization -- 2.1.5.4 Market Basket Analysis -- 2.1.5.5 Job Scheduling -- 2.1.5.6 Classification Problem -- 2.1.5.7 Hybrid Decision Tree-Genetic Algorithm to Data Mining.
2.1.5.8 Genetic Algorithm-Optimization of Data Mining in Education -- 2.1.6 Advantages of Genetic Algorithms -- 2.1.7 Genetic Algorithms Demerits in the Current Era -- 2.2 Introduction to Artificial Bear Optimization (ABO) -- 2.2.1 Bear's Nasal Cavity -- 2.2.2 Artificial Bear ABO Gist Algorithm: -- Pseudo Algorithm: -- Implementation: -- 2.2.3 Implementation Based on Requirement -- 2.2.3.1 Market Place -- 2.2.3.2 Industry-Specific -- 2.2.3.3 Semi-Structured or Unstructured Data -- 2.2.4 Merits of ABO -- 2.3 Performance Evaluation -- 2.4 What is Next? -- References -- 3 Efficiency of Finding Best Solutions Through Ant Colony Optimization (ACO) Technique -- 3.1 Introduction -- 3.1.1 Example of Optimization Process -- 3.1.2 Components of Optimization Algorithms -- 3.1.3 Optimization Techniques Based on Solutions -- 3.1.3.1 Optimization Techniques Based on Algorithms -- 3.1.4 Characteristics -- 3.1.5 Classes of Heuristic Algorithms -- 3.1.6 Metaheuristic Algorithms -- 3.1.6.1 Classification of Metaheuristic Algorithms: Nature-Inspired vs. Non-Nature-Inspired -- 3.1.6.2 Population-Based vs. Single-Point Search (Trajectory) -- 3.1.7 Data Processing Flow of ACO -- 3.2 A Case Study on Surgical Treatment in Operation Room -- 3.3 Case Study on Waste Management System -- 3.4 Working Process of the System -- 3.5 Background Knowledge to be Considered for Estimation -- 3.5.1 Heuristic Function -- 3.5.2 Functional Approach -- 3.6 Case Study on Traveling System -- 3.7 Future Trends and Conclusion -- References -- 4 A Hybrid Bat-Genetic Algorithm-Based Novel Optimal Wavelet Filter for Compression of Image Data -- 4.1 Introduction -- 4.2 Review of Related Works -- 4.3 Existing Technique for Secure Image Transmission -- 4.4 Proposed Design of Optimal Wavelet Coefficients for Image Compression -- 4.4.1 Optimized Transformation Module. 4.4.1.1 DWT Analysis and Synthesis Filter Bank -- 4.4.2 Compression and Encryption Module -- 4.4.2.1 SPIHT -- 4.4.2.2 Chaos-Based Encryption -- 4.5 Results and Discussion -- 4.5.1 Experimental Setup and Evaluation Metrics -- 4.5.2 Simulation Results -- 4.5.2.1 Performance Analysis of the Novel Filter KARELET -- 4.5.3 Result Analysis Proposed System -- 4.6 Conclusion -- References -- 5 A Swarm Robot for Harvesting a Paddy Field -- 5.1 Introduction -- 5.1.1 Working Principle of Particle Swarm Optimization -- 5.1.2 First Case Study on Birds Fly -- 5.1.3 Operational Moves on Birds Dataset -- 5.1.4 Working Process of the Proposed Model -- 5.2 Second Case Study on Recommendation Systems -- 5.3 Third Case Study on Weight Lifting Robot -- 5.4 Background Knowledge of Harvesting Process -- 5.4.1 Data Flow of PSO Process -- 5.4.2 Working Flow of Harvesting Process -- 5.4.3 The First Phase of Harvesting Process -- 5.4.4 Separation Process in Harvesting -- 5.4.5 Cleaning Process in the Field -- 5.5 Future Trend and Conclusion -- References -- 6 Firefly Algorithm -- 6.1 Introduction -- 6.2 Firefly Algorithm -- 6.2.1 Firefly Behavior -- 6.2.2 Standard Firefly Algorithm -- 6.2.3 Variations in Light Intensity and Attractiveness -- 6.2.4 Distance and Movement -- 6.2.5 Implementation of FA -- 6.2.6 Special Cases of Firefly Algorithm -- 6.2.7 Variants of FA -- 6.3 Applications of Firefly Algorithm -- 6.3.1 Job Shop Scheduling -- 6.3.2 Image Segmentation -- 6.3.3 Stroke Patient Rehabilitation -- 6.3.4 Economic Emission Load Dispatch -- 6.3.5 Structural Design -- 6.4 Why Firefly Algorithm is Efficient -- 6.4.1 FA is Not PSO -- 6.5 Discussion and Conclusion -- References -- 7 The Comprehensive Review for Biobased FPA Algorithm -- 7.1 Introduction -- 7.1.1 Stochastic Optimization -- 7.1.2 Robust Optimization -- 7.1.3 Dynamic Optimization -- 7.1.4 Alogrithm. 7.1.5 Swarm Intelligence -- 7.2 Related Work to FPA -- 7.2.1 Flower Pollination Algorithm -- 7.2.2 Versions of FPA -- 7.2.3 Methods and Description -- 7.3 Limitations -- 7.4 Future Research -- 7.5 Conclusion -- References -- 8 Nature-Inspired Computation in Data Mining -- 8.1 Introduction -- 8.2 Classification of NIC -- 8.2.1 Swarm Intelligence for Data Mining -- 8.2.1.1 Swarm Intelligence Algorithm -- 8.2.1.2 Applications of Swarm Intelligence in Data Mining -- 8.2.1.3 Swarm-Based Intelligence Techniques -- 8.3 Evolutionary Computation -- 8.3.1 Genetic Algorithms -- 8.3.1.1 Applications of Genetic Algorithms in Data Mining -- 8.3.2 Evolutionary Programming -- 8.3.2.1 Applications of Evolutionary Programming in Data Mining -- 8.3.3 Genetic Programming -- 8.3.3.1 Applications of Genetic Programming in Data Mining -- 8.3.4 Evolution Strategies -- 8.3.4.1 Applications of Evolution Strategies in Data Mining -- 8.3.5 Differential Evolutions -- 8.3.5.1 Applications of Differential Evolution in Data Mining -- 8.4 Biological Neural Network -- 8.4.1 Artificial Neural Computation -- 8.4.1.1 Neural Network Models -- 8.4.1.2 Challenges of Artificial Neural Network in Data Mining -- 8.4.1.3 Applications of Artificial Neural Network in Data Mining -- 8.5 Molecular Biology -- 8.5.1 Membrane Computing -- 8.5.2 Algorithm Basis -- 8.5.3 Challenges of Membrane Computing in Data Mining -- 8.5.4 Applications of Membrane Computing in Data Mining -- 8.6 Immune System -- 8.6.1 Artificial Immune System -- 8.6.1.1 Artificial Immune System Algorithm (Enhanced) -- 8.6.1.2 Challenges of Artificial Immune System in Data Mining -- 8.6.1.3 Applications of Artificial Immune System in Data Mining -- 8.7 Applications of NIC in Data Mining -- 8.8 Conclusion -- References -- 9 Optimization Techniques for Removing Noise in Digital Medical Images -- 9.1 Introduction. 9.2 Medical Imaging Techniques -- 9.2.1 X-Ray Images -- 9.2.2 Computer Tomography Imaging -- 9.2.3 Magnetic Resonance Images -- 9.2.4 Positron Emission Tomography -- 9.2.5 Ultrasound Imaging Techniques -- 9.3 Image Denoising -- 9.3.1 Impulse Noise and Speckle Noise Denoising -- 9.4 Optimization in Image Denoising -- 9.4.1 Particle Swarm Optimization -- 9.4.2 Adaptive Center Pixel Weighted Median Exponential Filter -- 9.4.3 Hybrid Wiener Filter -- 9.4.4 Removal of Noise in Medical Images Using Particle Swarm Optimization -- 9.4.4.1 Curvelet Transform -- 9.4.4.2 PSO With Curvelet Transform and Hybrid Wiener Filter -- 9.4.5 DFOA-Based Curvelet Transform and Hybrid Wiener Filter -- 9.4.5.1 Dragon Fly Optimization Algorithm -- 9.4.5.2 DFOA-Based HWACWMF -- 9.5 Results and Discussions -- 9.5.1 Simulation Results -- 9.5.2 Performance Metric Analysis -- 9.5.3 Summary -- 9.6 Conclusion and Future Scope -- References -- 10 Performance Analysis of Nature-Inspired Algorithms in Breast Cancer Diagnosis -- 10.1 Introduction -- 10.1.1 NIC Algorithms -- 10.2 Related Works -- 10.3 Dataset: Wisconsin Breast Cancer Dataset (WBCD) -- 10.4 Ten-Fold Cross-Validation -- 10.4.1 Training Data -- 10.4.2 Validation Data -- 10.4.3 Test Data -- 10.4.4 Pseudocode -- 10.4.5 Advantages of K-Fold or 10-Fold Cross-Validation -- 10.5 Naive Bayesian Classifier -- 10.5.1 Pseudocode of Naive Bayesian Classifier -- 10.5.2 Advantages of Naive Bayesian Classifier -- 10.6 K-Means Clustering -- 10.7 Support Vector Machine (SVM) -- 10.8 Swarm Intelligence Algorithms -- 10.8.1 Particle Swarm Optimization -- 10.8.2 Firefly Algorithm -- 10.8.3 Ant Colony Optimization -- 10.9 Evaluation Metrics -- 10.10 Results and Discussion -- 10.11 Conclusion -- References -- 11 Applications of Cuckoo Search Algorithm for Optimization Problems -- 11.1 Introduction -- 11.2 Related Works. 11.3 Cuckoo Search Algorithm. |
| Record Nr. | UNINA-9910555132603321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc. : , : Scrivener Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Nature inspired algorithms and their applications / / editors, S. Balamurugan [et al.]
| Nature inspired algorithms and their applications / / editors, S. Balamurugan [et al.] |
| Pubbl/distr/stampa | Hoboken, NJ : , : John Wiley & Sons, Inc. : , : Scrivener Publishing, , [2022] |
| Descrizione fisica | 1 online resource (384 pages) |
| Disciplina | 571.0284 |
| Soggetto topico | Nature-inspired algorithms |
| ISBN |
1-119-68166-9
1-119-68198-7 1-119-68199-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Introduction to Nature-Inspired Computing -- 1.1 Introduction -- 1.2 Aspiration From Nature -- 1.3 Working of Nature -- 1.4 Nature-Inspired Computing -- 1.4.1 Autonomous Entity -- 1.5 General Stochastic Process of Nature-Inspired Computation -- 1.5.1 NIC Categorization -- 1.5.1.1 Bioinspired Algorithm -- 1.5.1.2 Swarm Intelligence -- 1.5.1.3 Physical Algorithms -- 1.5.1.4 Familiar NIC Algorithms -- References -- 2 Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning -- 2.1 Introduction of Genetic Algorithm -- 2.1.1 Background of GA -- 2.1.2 Why Natural Selection Theory Compared With the Search Heuristic Algorithm? -- 2.1.3 Working Sequence of Genetic Algorithm -- 2.1.3.1 Population -- 2.1.3.2 Fitness Among the Individuals -- 2.1.3.3 Selection of Fitted Individuals -- 2.1.3.4 Crossover Point -- 2.1.3.5 Mutation -- 2.1.4 Application of Machine Learning in GA -- 2.1.4.1 Genetic Algorithm Role in Feature Selection for ML Problem -- 2.1.4.2 Traveling Salesman Problem -- 2.1.4.3 Blackjack-A Casino Game -- 2.1.4.4 Pong Against AI-Evolving Agents (Reinforcement Learning) Using GA -- 2.1.4.5 SNAKE AI-Game -- 2.1.4.6 Genetic Algorithm's Role in Neural Network -- 2.1.4.7 Solving a Battleship Board Game as an Optimization Problem Which Was Initially Released by Milton Bradley in 1967 -- 2.1.4.8 Frozen Lake Problem From OpenAI Gym -- 2.1.4.9 N-Queen Problem -- 2.1.5 Application of Data Mining in GA -- 2.1.5.1 Association Rules Generation -- 2.1.5.2 Pattern Classification With Genetic Algorithm -- 2.1.5.3 Genetic Algorithms in Stock Market Data Mining Optimization -- 2.1.5.4 Market Basket Analysis -- 2.1.5.5 Job Scheduling -- 2.1.5.6 Classification Problem -- 2.1.5.7 Hybrid Decision Tree-Genetic Algorithm to Data Mining.
2.1.5.8 Genetic Algorithm-Optimization of Data Mining in Education -- 2.1.6 Advantages of Genetic Algorithms -- 2.1.7 Genetic Algorithms Demerits in the Current Era -- 2.2 Introduction to Artificial Bear Optimization (ABO) -- 2.2.1 Bear's Nasal Cavity -- 2.2.2 Artificial Bear ABO Gist Algorithm: -- Pseudo Algorithm: -- Implementation: -- 2.2.3 Implementation Based on Requirement -- 2.2.3.1 Market Place -- 2.2.3.2 Industry-Specific -- 2.2.3.3 Semi-Structured or Unstructured Data -- 2.2.4 Merits of ABO -- 2.3 Performance Evaluation -- 2.4 What is Next? -- References -- 3 Efficiency of Finding Best Solutions Through Ant Colony Optimization (ACO) Technique -- 3.1 Introduction -- 3.1.1 Example of Optimization Process -- 3.1.2 Components of Optimization Algorithms -- 3.1.3 Optimization Techniques Based on Solutions -- 3.1.3.1 Optimization Techniques Based on Algorithms -- 3.1.4 Characteristics -- 3.1.5 Classes of Heuristic Algorithms -- 3.1.6 Metaheuristic Algorithms -- 3.1.6.1 Classification of Metaheuristic Algorithms: Nature-Inspired vs. Non-Nature-Inspired -- 3.1.6.2 Population-Based vs. Single-Point Search (Trajectory) -- 3.1.7 Data Processing Flow of ACO -- 3.2 A Case Study on Surgical Treatment in Operation Room -- 3.3 Case Study on Waste Management System -- 3.4 Working Process of the System -- 3.5 Background Knowledge to be Considered for Estimation -- 3.5.1 Heuristic Function -- 3.5.2 Functional Approach -- 3.6 Case Study on Traveling System -- 3.7 Future Trends and Conclusion -- References -- 4 A Hybrid Bat-Genetic Algorithm-Based Novel Optimal Wavelet Filter for Compression of Image Data -- 4.1 Introduction -- 4.2 Review of Related Works -- 4.3 Existing Technique for Secure Image Transmission -- 4.4 Proposed Design of Optimal Wavelet Coefficients for Image Compression -- 4.4.1 Optimized Transformation Module. 4.4.1.1 DWT Analysis and Synthesis Filter Bank -- 4.4.2 Compression and Encryption Module -- 4.4.2.1 SPIHT -- 4.4.2.2 Chaos-Based Encryption -- 4.5 Results and Discussion -- 4.5.1 Experimental Setup and Evaluation Metrics -- 4.5.2 Simulation Results -- 4.5.2.1 Performance Analysis of the Novel Filter KARELET -- 4.5.3 Result Analysis Proposed System -- 4.6 Conclusion -- References -- 5 A Swarm Robot for Harvesting a Paddy Field -- 5.1 Introduction -- 5.1.1 Working Principle of Particle Swarm Optimization -- 5.1.2 First Case Study on Birds Fly -- 5.1.3 Operational Moves on Birds Dataset -- 5.1.4 Working Process of the Proposed Model -- 5.2 Second Case Study on Recommendation Systems -- 5.3 Third Case Study on Weight Lifting Robot -- 5.4 Background Knowledge of Harvesting Process -- 5.4.1 Data Flow of PSO Process -- 5.4.2 Working Flow of Harvesting Process -- 5.4.3 The First Phase of Harvesting Process -- 5.4.4 Separation Process in Harvesting -- 5.4.5 Cleaning Process in the Field -- 5.5 Future Trend and Conclusion -- References -- 6 Firefly Algorithm -- 6.1 Introduction -- 6.2 Firefly Algorithm -- 6.2.1 Firefly Behavior -- 6.2.2 Standard Firefly Algorithm -- 6.2.3 Variations in Light Intensity and Attractiveness -- 6.2.4 Distance and Movement -- 6.2.5 Implementation of FA -- 6.2.6 Special Cases of Firefly Algorithm -- 6.2.7 Variants of FA -- 6.3 Applications of Firefly Algorithm -- 6.3.1 Job Shop Scheduling -- 6.3.2 Image Segmentation -- 6.3.3 Stroke Patient Rehabilitation -- 6.3.4 Economic Emission Load Dispatch -- 6.3.5 Structural Design -- 6.4 Why Firefly Algorithm is Efficient -- 6.4.1 FA is Not PSO -- 6.5 Discussion and Conclusion -- References -- 7 The Comprehensive Review for Biobased FPA Algorithm -- 7.1 Introduction -- 7.1.1 Stochastic Optimization -- 7.1.2 Robust Optimization -- 7.1.3 Dynamic Optimization -- 7.1.4 Alogrithm. 7.1.5 Swarm Intelligence -- 7.2 Related Work to FPA -- 7.2.1 Flower Pollination Algorithm -- 7.2.2 Versions of FPA -- 7.2.3 Methods and Description -- 7.3 Limitations -- 7.4 Future Research -- 7.5 Conclusion -- References -- 8 Nature-Inspired Computation in Data Mining -- 8.1 Introduction -- 8.2 Classification of NIC -- 8.2.1 Swarm Intelligence for Data Mining -- 8.2.1.1 Swarm Intelligence Algorithm -- 8.2.1.2 Applications of Swarm Intelligence in Data Mining -- 8.2.1.3 Swarm-Based Intelligence Techniques -- 8.3 Evolutionary Computation -- 8.3.1 Genetic Algorithms -- 8.3.1.1 Applications of Genetic Algorithms in Data Mining -- 8.3.2 Evolutionary Programming -- 8.3.2.1 Applications of Evolutionary Programming in Data Mining -- 8.3.3 Genetic Programming -- 8.3.3.1 Applications of Genetic Programming in Data Mining -- 8.3.4 Evolution Strategies -- 8.3.4.1 Applications of Evolution Strategies in Data Mining -- 8.3.5 Differential Evolutions -- 8.3.5.1 Applications of Differential Evolution in Data Mining -- 8.4 Biological Neural Network -- 8.4.1 Artificial Neural Computation -- 8.4.1.1 Neural Network Models -- 8.4.1.2 Challenges of Artificial Neural Network in Data Mining -- 8.4.1.3 Applications of Artificial Neural Network in Data Mining -- 8.5 Molecular Biology -- 8.5.1 Membrane Computing -- 8.5.2 Algorithm Basis -- 8.5.3 Challenges of Membrane Computing in Data Mining -- 8.5.4 Applications of Membrane Computing in Data Mining -- 8.6 Immune System -- 8.6.1 Artificial Immune System -- 8.6.1.1 Artificial Immune System Algorithm (Enhanced) -- 8.6.1.2 Challenges of Artificial Immune System in Data Mining -- 8.6.1.3 Applications of Artificial Immune System in Data Mining -- 8.7 Applications of NIC in Data Mining -- 8.8 Conclusion -- References -- 9 Optimization Techniques for Removing Noise in Digital Medical Images -- 9.1 Introduction. 9.2 Medical Imaging Techniques -- 9.2.1 X-Ray Images -- 9.2.2 Computer Tomography Imaging -- 9.2.3 Magnetic Resonance Images -- 9.2.4 Positron Emission Tomography -- 9.2.5 Ultrasound Imaging Techniques -- 9.3 Image Denoising -- 9.3.1 Impulse Noise and Speckle Noise Denoising -- 9.4 Optimization in Image Denoising -- 9.4.1 Particle Swarm Optimization -- 9.4.2 Adaptive Center Pixel Weighted Median Exponential Filter -- 9.4.3 Hybrid Wiener Filter -- 9.4.4 Removal of Noise in Medical Images Using Particle Swarm Optimization -- 9.4.4.1 Curvelet Transform -- 9.4.4.2 PSO With Curvelet Transform and Hybrid Wiener Filter -- 9.4.5 DFOA-Based Curvelet Transform and Hybrid Wiener Filter -- 9.4.5.1 Dragon Fly Optimization Algorithm -- 9.4.5.2 DFOA-Based HWACWMF -- 9.5 Results and Discussions -- 9.5.1 Simulation Results -- 9.5.2 Performance Metric Analysis -- 9.5.3 Summary -- 9.6 Conclusion and Future Scope -- References -- 10 Performance Analysis of Nature-Inspired Algorithms in Breast Cancer Diagnosis -- 10.1 Introduction -- 10.1.1 NIC Algorithms -- 10.2 Related Works -- 10.3 Dataset: Wisconsin Breast Cancer Dataset (WBCD) -- 10.4 Ten-Fold Cross-Validation -- 10.4.1 Training Data -- 10.4.2 Validation Data -- 10.4.3 Test Data -- 10.4.4 Pseudocode -- 10.4.5 Advantages of K-Fold or 10-Fold Cross-Validation -- 10.5 Naive Bayesian Classifier -- 10.5.1 Pseudocode of Naive Bayesian Classifier -- 10.5.2 Advantages of Naive Bayesian Classifier -- 10.6 K-Means Clustering -- 10.7 Support Vector Machine (SVM) -- 10.8 Swarm Intelligence Algorithms -- 10.8.1 Particle Swarm Optimization -- 10.8.2 Firefly Algorithm -- 10.8.3 Ant Colony Optimization -- 10.9 Evaluation Metrics -- 10.10 Results and Discussion -- 10.11 Conclusion -- References -- 11 Applications of Cuckoo Search Algorithm for Optimization Problems -- 11.1 Introduction -- 11.2 Related Works. 11.3 Cuckoo Search Algorithm. |
| Record Nr. | UNINA-9910830496003321 |
| Hoboken, NJ : , : John Wiley & Sons, Inc. : , : Scrivener Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Nature-inspired optimization algorithms with Java : a look at optimization techniques / / Shashank Jain
| Nature-inspired optimization algorithms with Java : a look at optimization techniques / / Shashank Jain |
| Autore | Jain Shashank |
| Pubbl/distr/stampa | New York, New York : , : Apress L. P., , [2022] |
| Descrizione fisica | 1 online resource (182 pages) |
| Disciplina | 519.6 |
| Soggetto topico |
Mathematical optimization
Nature-inspired algorithms Java (Computer program language) |
| ISBN | 1-4842-7401-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Introduction to Optimization: Problems and Techniques -- 2. Mammals: Whale, Gray Wolf, and Bat Optimization -- 3. Birds: Particle Swarm and Cuckoo Search Optimization -- 4. Insects: Firefly Optimization -- 5. Sea Creatures: Salp Swarm Optimization. |
| Record Nr. | UNINA-9910523783603321 |
Jain Shashank
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| New York, New York : , : Apress L. P., , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
