LEADER 10543nam 22004573 450 001 9910864187603321 005 20240524080320.0 010 $a3-031-55684-4 035 $a(CKB)32138393400041 035 $a(MiAaPQ)EBC31352124 035 $a(Au-PeEL)EBL31352124 035 $a(EXLCZ)9932138393400041 100 $a20240524d2024 uy 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNew Horizons for Fuzzy Logic, Neural Networks and Metaheuristics 205 $a1st ed. 210 1$aCham :$cSpringer International Publishing AG,$d2024. 210 4$d©2024. 215 $a1 online resource (422 pages) 225 1 $aStudies in Computational Intelligence Series ;$vv.1149 311 $a3-031-55683-6 327 $aIntro -- 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. 327 $aComputer 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. 327 $aNovel 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. 327 $a2.1 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. 327 $a2.3 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. 327 $a4 Experiments and Results. 410 0$aStudies in Computational Intelligence Series 700 $aCastillo$b Oscar$0762265 701 $aMelin$b Patricia$0762263 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910864187603321 996 $aNew Horizons for Fuzzy Logic, Neural Networks and Metaheuristics$94166441 997 $aUNINA