New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics / / edited by Oscar Castillo, Patricia Melin

• Autore: Castillo Oscar
• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
• Edizione: 1st ed. 2024.
• Descrizione fisica: 1 online resource (422 pages)
• Disciplina: 006.3
• Soggetto topico: Computational intelligence; Artificial intelligence; Computational Intelligence; Artificial Intelligence
• Altri autori: MelinPatricia
• Nota di contenuto: Intro -- Preface -- About This Book -- Contents -- Fuzzy Logic -- Fuzzy Adaptation of Parameters in a Multi-swarm Particle Swarm Optimization (PSO) Algorithm Applied to the Optimization of a Fuzzy Controller -- 1 Introduction -- 2 Proposal -- 3 Use Case -- 4 Results -- 5 Conclusions and Future Work -- References -- Fuzzifying Intrusion Detection Systems with Modified Artificial Bee Colony and Support Vector Machine Algorithms -- 1 Introduction -- 2 Methodology -- 3 Literature Review -- 3.1 Finding Promising IDS Architectures -- 3.2 Testing Data Sets -- 3.3 Comparing Papers -- 4 Preliminaries -- 4.1 Fuzzy Artificial Bee Colony Algorithm -- 4.2 Intuitionistic Fuzzy Twin Support Vector Machine -- 4.3 Combined Classification Process -- 5 Fuzzy Architecture -- 5.1 Feature Extraction and Normalization -- 5.2 Feature Selection -- 5.3 Classification -- 5.4 Classifier Training process -- 6 Results and Discussion -- 7 Further Research -- References -- Type-2 Mamdani Fuzzy System Optimization for a Classification Ensemble with Black Widow Optimizer -- 1 Introduction -- 2 Basic Concepts and Background -- 2.1 Type-1 and Type-2 Fuzzy Systems -- 2.2 Black Widow Optimizer -- 2.3 Ensemble of Neural Networks -- 3 Proposed Methodology -- 3.1 Medical Images -- 4 Experimental Results -- 5 Conclusions -- References -- Towards Designing Interval Type-3 Fuzzy PID Controllers -- 1 Introduction -- 2 PID Control -- 3 Fuzzy PID Control -- 4 Proposal for Type-3 Fuzzy PID Control -- 5 Illustrative Example -- 6 Conclusions -- References -- Neural Networks -- Classification of Consumption Level in Developing Countries for Time Series Prediction Using a Hierarchical Nested Artificial Neural Network Method -- 1 Introduction -- 2 Case Study -- 3 Methodology -- 4 Experiments and Results -- 5 Conclusions -- References. ; Computer Aided Diagnosis for COVID-19 with Quantum Computing and Transfer Learning -- 1 Introduction -- 2 Fundamentals -- 2.1 Convolutional Neural Network -- 2.2 Transfer Learning -- 2.3 Quantum Computing -- 3 Methods -- 3.1 Dataset -- 3.2 Model Architecture -- 3.3 Quantum Convolutional Preprocessing -- 3.4 Metrics -- 4 Experiments and Results -- 5 Conclusion and Future Work -- References -- Prescribed-Time Trajectory Tracking Control of Wheeled Mobile Robots Using Neural Networks and Robust Control Techniques -- 1 Introduction -- 2 Trajectory Generation -- 3 Kinematic Model and Control Design -- 4 Numerical Results -- 5 Conclusion -- References -- Generative Models for Class Imbalance Problem on BreakHis Dataset: A Case Study -- 1 Introduction -- 2 Background -- 2.1 Generative Models -- 2.2 Discriminative Models -- 3 Methodology -- 4 Results and Statistical Analysis -- 4.1 Generated Images -- 4.2 Classification Metrics Results -- 4.3 Statistical Analysis -- 5 Conclusions and Future Work -- References -- Prediction Using a Fuzzy Inference System in the Classification Layer of a Convolutional Neural Network Replacing the Softmax Function -- 1 Introduction -- 2 Literature Review -- 2.1 The Convolutional Neural Networks or CNN -- 2.2 The Softmax Function -- 3 Proposed Method -- 4 Results and Discussion -- 5 Conclusions -- References -- Optimization -- Optimization of Lithium‐Ion Batteries Using Boltzmann Metaheuristics Systems: Towards a Green Artificial Intelligence -- 1 Introduction -- 2 Methodology -- 2.1 Lithium-Ion Model -- 2.2 Lithium Battery in Boltzmann System -- 3 Boltzmann Optimization Algorithm -- 4 Results -- 4.1 Experimental Stup -- 4.2 Optimization of a Lithium Battery by BOA -- 5 Conclusions -- References. ; Novel Decomposition-Based Multi-objective Evolutionary Algorithm Using Reinforcement Learning Adaptive Operator Selection (MOEA/D-QL) -- 1 Introduction -- 2 Adaptive Operator Selection -- 2.1 Probability-Based -- 2.2 Based on Multi-armed Bandits -- 3 Adaptive Operator Selection Based on Dynamic Thompson Sampling (DYTS) -- 3.1 Credit Assignment -- 3.2 Operator Selection Mechanism -- 4 Reinforcement Learning -- 4.1 Q-learning -- 5 Proposed MOEA/D-QL Algorithm -- 5.1 Choose an Action -- 5.2 Take an Action -- 5.3 Get Reward -- 6 Update Q Table -- 6.1 Set of Available Actions -- 7 Computational Experiments -- 8 Results -- 8.1 Hypervolume -- 8.2 Generalized Spread -- 8.3 Inverted Generational Distance -- 9 Conclusions -- References -- Multiobjective Particle Swarm Optimization for the Hydro-Thermal Power Scheduling Problem -- 1 Introduction -- 2 Dynamic Multiobjective Optimization Problem Definitions -- 3 Problem Formalization -- 3.1 Objective Functions -- 3.2 Constraints -- 3.3 Case Study -- 4 Solution Methodology -- 4.1 Multiobjective Particle Swarm Optimization -- 4.2 Initial Feasible Solutions -- 4.3 Mutation Operator -- 4.4 Constraint Handling -- 5 Computational Experience -- 6 Conclusions and Further Work -- References -- Comparative Analysis of Metaheuristic Algorithms for Standard Dynamic Multiobjective Optimization Problems -- 1 Introduction -- 2 Dynamic Multiobjective Optimization Problem Definitions -- 3 FDA Test Suite -- 3.1 FDA1 -- 3.2 FDA2 -- 3.3 FDA3 -- 3.4 FDA4 -- 3.5 FDA5 -- 4 Metaheuristics for DMOPs -- 4.1 DNSGA-II -- 4.2 DSPEA-II -- 5 Computational Experience -- 5.1 Experimental Design -- 5.2 Experimental Results -- 6 Conclusions and Further Work -- References -- Hypervolume Indicator as an Estimator for Adaptive Operator Selection in an On-Line Multi-objective Hyper-heuristic -- 1 Introduction -- 2 Relevant Concepts. ; 2.1 On-Line Hyper-heuristic -- 2.2 Adaptive Operator Selection -- 2.3 MOEA/D-DRA -- 2.4 Hypervolume Indicator -- 3 Methodology -- 3.1 High-Level MOEA/D-DRA Strategy -- 3.2 HyperVolume Indicator as an Operator Quality Metric -- 4 Experiments -- 4.1 Test Problems -- 4.2 Algorithms and Parameter Settings -- 5 Results and Discussion -- 6 Conclusions -- References -- Metaheuristics: Theory and Applications -- A New Breeding Crossover Approach for Evolutionary Algorithms -- 1 Introduction -- 2 Proposal -- 2.1 Crossover Proposal -- 3 Experiments -- 3.1 Experimental Configuration -- 3.2 Experimental Results -- 4 Discussion -- 5 Conclusions -- References -- Dragonfly Algorithm for Benchmark Mathematical Functions Optimization -- 1 Introduction -- 2 Nature Inspiration -- 3 Study of the Literature -- 4 Dragonfly Algorithm (DA) -- 5 Results and Comparison -- 6 Conclusions -- References -- Fuzzy Dynamic Adaptation of a Whale Algorithm for the Optimization of Benchmark Functions -- 1 Introduction -- 2 Related Works -- 3 Whale Optimization Algorithm -- 4 Original WOA -- 5 Surround Prey -- 6 Bubble-Net Attacking Method (Exploitation Phase) -- 7 Search for Prey (Exploration Phase) -- 8 Fuzzy Whale Optimization Algorithm -- 9 Sets of Benchmark Functions -- 10 Experimental Results -- 11 Analysis of the Results -- 12 Conclusions -- References -- A New Variant of the Multiverse Optimizer Using Multiple Chaotic Maps and Fuzzy Logic for Optimization in CEC-2017 Benchmark Suite -- 1 Introduction -- 2 Multiverse Optimizer and Variants -- 3 Fuzzy Chaotic Multiverse Optimizer and Chaotic Maps -- 4 Comparison -- 5 Conclusions -- References -- A Comparison of Single-Based Versus Population-Based Search Algorithms in the Optimization of Fuzzy Systems -- 1 Introduction -- 2 Optimization Algorithms -- 2.1 Generalized Pattern Search -- 2.2 Simulated Annealing Algorithm. ; 2.3 Genetic Algorithm -- 2.4 Particle Swarm Optimization -- 3 Fuzzy System Optimization -- 3.1 Mamdani Fuzzy Systems -- 3.2 Design and Optimization of a Mamdani Fuzzy System -- 4 Testing and Results -- 5 Conclusions and Future Work -- References -- Applications of Intelligent Systems -- A Comprehensive Review of Task Scheduling Problem in Cloud Computing: Recent Advances and Comparative Analysis -- 1 Introduction -- 2 Cloud Computing: Importance, Classification and Architecture -- 3 Task Scheduling Problem -- 4 Task Scheduling Algorithms and Performance Metrics -- 4.1 Performance Metrics -- 4.2 Heuristic Techniques -- 4.3 Metaheuristic Techniques -- 5 Relevant Optimization Approaches -- 6 Comparison of Results -- 6.1 Instances -- 6.2 Results -- 7 Conclusion -- References -- Routing Design Methodology for Collaborative Robots in the Car Painting Process Using Perturbative Heuristics -- 1 Introduction -- 2 Related Work -- 2.1 Car Painting Problem -- 2.2 Collaborative Robotic Problem -- 2.3 Health Risks During the Car Painting Process -- 2.4 Optimization Techniques Applied to Collaborative Robotic Car-Painting Problem (CRCP) -- 3 Background -- 3.1 Collaborative Robotic Problem -- 3.2 Car Sequencing Problem -- 3.3 Car-Painting Problem -- 3.4 Collaborative Robotic Car- Painting Problem (CRCP) -- 3.5 Heuristics and Metaheuristics -- 4 Methodology -- 5 Results -- 5.1 Heuristics Results -- 6 Conclusions and Future work -- References -- Building an Open-Source Hydronic Heating System Simulator -- 1 Introduction -- 2 Proposal -- 2.1 HydronicPy -- 2.2 Simulator Thermal Dynamics -- 2.3 Simulator Core Models -- 3 Experiments -- 4 Conclusions -- References -- Analyzing the Impact of the Low Level Heuristics of a Hyperheuristic for the Master Bay Planning Problem -- 1 Introduction -- 2 Hyperheuristic -- 2.1 Low Level Heuristics -- 3 Proposed Methodology. ; 4 Experiments and Results.
• Sommario/riassunto: This book contains a collection of papers focused on hybrid intelligent systems based on soft computing techniques. In this book, new horizons on the theoretical developments of fuzzy logic, neural networks and optimization algorithms are envisioned. In addition, the abovementioned methods are discussed in application areas such as control and robotics, pattern recognition, medical diagnosis, decision-making, prediction and optimization of complex problems. There are a group of papers with the main theme of type-1, type-2 and type-3 fuzzy systems, which basically consists of papers that propose new concepts and algorithms based on type-1, type-2 and type-3 fuzzy logic and their applications. There is also a group of papers that offer theoretical concepts and applications of meta-heuristics in different areas. Another group of papers outlines diverse applications of hybrid intelligent systems in real problems. There are also a group papers that present theory and practice of neural networks in different applications. Finally, there are papers that offer theory and practice of optimization and evolutionary algorithms in different application areas.
• Titolo autorizzato: New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics
• ISBN: 3-031-55684-4
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910864187603321
• Lo trovi qui: Univ. Federico II
• Serie: Studies in Computational Intelligence, . 1860-9503 ; ; 1149