Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics [[electronic resource] ] : Theory and Applications / / edited by Oscar Castillo, Patricia Melin

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2015
• Edizione: 1st ed. 2015.
• Descrizione fisica: 1 online resource (VIII, 195 p. 98 illus.)
• Disciplina: 511.3
• Soggetto topico: Computational intelligence; Artificial intelligence; Computational Intelligence; Artificial Intelligence
• Persona (resp. second.): CastilloOscar; MelinPatricia
• Note generali: Bibliographic Level Mode of Issuance: Monograph
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- Preface -- Contents -- Part I Theory -- 1 Fuzzy Logic for Dynamic Parameter Tuning in ACO and Its Application in Optimal Fuzzy Logic Controller Design -- Abstract -- 1 Introduction -- 2 Ant Colony Optimization (ACO) -- 3 Performance Analysis of ACO -- 4 Fuzzy Logic Convergence Controller -- 5 Simulation in TSP Problems -- 6 Fuzzy Trajectory Controller for a Unicycle Mobile Robot -- 7 ACO for Membership Functions Optimization -- 8 ASRank + ConvCont for Membership Functions Optimization -- 9 Simulation in Membership Functions Optimization Problem -- 10 ASRank + ConvCont vs. S-ACO -- 11 Conclusions -- References -- 2 Fuzzy Classification System Design Using PSO with Dynamic Parameter Adaptation Through Fuzzy Logic -- Abstract -- 1 Introduction -- 2 Methodology for Parameter Adaptation -- 3 Experimentation with the Fuzzy Systems and the Benchmark Mathematical Functions -- 4 Statistical Comparison -- 5 Fuzzy Classifier Design -- 6 Methodology for Designing Fuzzy Classifiers -- 7 Experimentation in the Design of Fuzzy Classifiers -- 8 Statistical Comparison for Fuzzy Classifiers -- 9 Conclusions -- References -- 3 Differential Evolution with Dynamic Adaptation of Parameters for the Optimization of Fuzzy Controllers -- Abstract -- 1 Introduction -- 2 Differential Evolution -- 2.1 Population Structure -- 2.2 Initialization -- 2.3 Mutation -- 2.4 Crossover -- 2.5 Selection -- 3 Proposed Method -- 4 Benchmark Function -- 5 Fuzzy System -- 6 Experiments and Methodology -- 7 Simulation Results -- 8 Conclusions -- References -- 4 A New Bat Algorithm with Fuzzy Logic for Dynamical Parameter Adaptation and Its Applicability to Fuzzy Control Design -- Abstract -- 1 Introduction -- 2 Bat Algorithm -- 2.1 Rules of Bats -- 2.2 Pseudocode for the Bat Algorithm -- 2.3 Movements in the Bat Algorithm -- 2.4 Loudness and Pulse Rates -- 3 Genetic Algorithms. ; 3.1 Representation -- 3.2 Crossover Operations -- 3.3 Mutation -- 4 Benchmark Mathematical Functions -- 5 Results Between GA and the Bat Algorithm -- 6 Proposed Method -- 7 Bat Algorithm Apply to the Inverted Pendulum -- 8 Conclusions -- Acknowledgments -- References -- 5 Optimization of Benchmark Mathematical Functions Using the Firefly Algorithm with Dynamic Parameters -- Abstract -- 1 Introduction -- 2 Firefly Algorithm -- 2.1 Attractiveness -- 2.2 Distance -- 2.3 Movement -- 3 Methodology for Parameter Adaptation -- 4 Experimentation with Benchmark Mathematical Functions -- 5 Conclusion -- 6 Cuckoo Search via L00E9vy Flights and a Comparison with Genetic Algorithms -- Abstract -- 1 Introduction -- 2 Cuckoo Search Algorithm -- 2.1 Variants -- 2.2 L00E9vy Flights -- 2.3 Pseudo Code for Cuckoo Search Algorithm -- 2.4 Generate a New Solution -- 3 Genetic Algorithms -- 3.1 Representation---the Chromosome -- 3.2 Crossover Operations -- 3.3 Mutation -- 4 Benchmark Mathematical Functions -- 5 Simulation Results -- 5.1 Simulation Results with the Cuckoo Search Algorithm -- 5.2 Simulation Results with The Genetic Algorithm (GA) -- 6 Conclusions -- Acknowledgment -- References -- 7 A Harmony Search Algorithm Comparison with Genetic Algorithms -- Abstract -- 1 Introduction -- 2 Harmony Search Algorithm -- 2.1 Memory in Harmony Search Algorithm -- 2.2 Pitch Adjustment -- 2.3 Randomization -- 2.4 Pseudo Code for Harmony Search Algorithm -- 2.5 Variants -- 2.6 Application Areas -- 3 Genetic Algorithms -- 3.1 Representation -- 3.2 Crossover Operations -- 3.3 Mutation -- 4 Benchmark Mathematical Functions -- 5 Optimization Problems -- 6 Simulation Results -- 6.1 Simulation Results with Harmony Search Algorithm -- 6.2 Simulation Results with the Genetic Algorithm -- 7 Conclusions -- Acknowledgments -- References -- Part II Applications. ; 8 A Gravitational Search Algorithm for Optimization of Modular Neural Networks in Pattern Recognition -- Abstract -- 1 Introduction -- 2 Basic Concepts -- 2.1 Modular Neural Networks -- 2.2 The Law of Gravity and Second Motion Law -- 2.3 Gravitational Search Algorithm -- 2.4 Parameters Settings in the Gravitational Search Algorithm -- 3 Modular Neural Network Architecture -- 3.1 Database of Echocardiograms Recognition -- 4 Experimental Results -- 5 Conclusions -- References -- 9 Ensemble Neural Network Optimization Using the Particle Swarm Algorithm with Type-1 and Type-2 Fuzzy Integration for Time Series Prediction -- Abstract -- 1 Introduction -- 2 Optimization -- 3 Particle Swarm Optimization -- 4 Fuzzy Systems as Methods of Integration -- 5 Problem Statement and Proposed Method -- 6 Simulation Results -- 7 Conclusions -- Acknowledgment -- References -- 10 Clustering Bin Packing Instances for Generating a Minimal Set of Heuristics by Using Grammatical Evolution -- Abstract -- 1 Introduction -- 2 Bin Packing Problem -- 2.1 Instances -- 2.2 Fitness Measure -- 2.3 Heuristics -- 2.4 Clustering -- 3 Grammatical Evolution -- 3.1 Mapping Process -- 4 Experiments -- 5 Results -- 6 Conclusions and Future Work -- Acknowledgement -- References -- 11 Comparative Study of Particle Swarm Optimization Variants in Complex Mathematics Functions -- Abstract -- 1 Introduction -- 2 Standard PSO Algorithm -- 3 Variants of PSO -- 3.1 Inertia Weight -- 4 Fuzzy Logic -- 4.1 Fuzzy System -- 5 Simulation Results for Mathematical Functions -- 5.1 F1 Function -- 5.2 F2 Function -- 5.3 F3 Function -- 5.4 F4 Function -- 6 Conclusions -- References -- 12 Optimization of Modular Network Architectures with a New Evolutionary Method Using a Fuzzy Combination of Particle Swarm Optimization and Genetic Algorithms -- Abstract -- 1 Introduction -- 2 Genetic Algorithm for Optimization. ; 3 Particle Swarm Optimization -- 4 FPSO + FGA Method -- 5 Full Model of FPSO + FGA -- 5.1 FPSO (Fuzzy Particle Swarm Optimization) -- 5.2 FGA (Fuzzy Genetic Algorithm) -- 5.3 Definition of the Fuzzy Systems Used in FPSO + FGA -- 6 Simulation Results for Modular Neural Network for Optimization -- 7 Conclusions -- Acknowledgments -- Reference.
• Sommario/riassunto: This book describes recent advances on fuzzy logic augmentation of nature-inspired optimization metaheuristics and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in two main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic augmentation of nature-inspired optimization metaheuristics, which basically consists of papers that propose new optimization algorithms enhanced using fuzzy systems. The second part contains papers with the main theme of application of optimization algorithms, which are basically papers using nature-inspired techniques to achieve optimization of complex optimization problems in diverse areas of application.
• Titolo autorizzato: Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics
• ISBN: 3-319-10960-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910299854803321
• Lo trovi qui: Univ. Federico II
• Serie: Studies in Computational Intelligence, . 1860-949X ; ; 574