Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource (XIV, 220 p. 47 illus., 29 illus. in color)

Edition: 1st ed. 2023

ISBN: 9783031201059

Series Statement: Studies in Computational Intelligence 1063

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-20104-2

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-20106-6

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-20107-3

Language: English

Subjects: Computer Science

DOI: 10.1007/978-3-031-20105-9

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: XIV, 220 p. 47 illus., 29 illus. in color. , online resource.

Edition: 1st ed. 2023.

ISBN: 9783031201059

Series Statement: Studies in Computational Intelligence, 1063

Content: This book presents a comparative perspective of current metaheuristic developments, which have proved to be effective in their application to several complex problems. The study of biological and social entities such as animals, humans, or insects that manifest a cooperative behavior has produced several computational models in metaheuristic methods. Although these schemes emulate very different processes or systems, the rules used to model individual behavior are very similar. Under such conditions, it is not clear to identify which are the advantages or disadvantages of each metaheuristic technique. The book is compiled from a teaching perspective. For this reason, the book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. It is appropriate for courses such as Artificial Intelligence, Electrical Engineering, Evolutionary Computation. The book is also useful for researchers from the evolutionary and engineering communities. Likewise, engineer practitioners, who are not familiar with metaheuristic computation concepts, will appreciate that the techniques discussed are beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise in engineering areas.

Note: Fundamentals of Metaheuristic Computation -- A Comparative Approach for Two-Dimensional Digital IIR Filter Design Applying Different Evolutionary Computational Techniques -- Comparison of Metaheuristics for Chaotic Systems Estimation -- Comparison Study of Novel Evolutionary Algorithms for Elliptical Shapes in Images -- IIR System Identification using Several Optimization Techniques: A Review Analysis -- Fractional-order Estimation using Locust Search Algorithm -- Comparison of Optimization Techniques for Solar Cells Parameter Identification -- Comparison of Metaheuristics Techniques and Agent-Based Approaches.

In: Springer Nature eBook

Additional Edition: Printed edition: ISBN 9783031201042

Additional Edition: Printed edition: ISBN 9783031201066

Additional Edition: Printed edition: ISBN 9783031201073

Language: English

DOI: 10.1007/978-3-031-20105-9

URL: https://doi.org/10.1007/978-3-031-20105-9

UID:

Format: 1 online resource (230 pages)

ISBN: 3-031-20105-1

Series Statement: Studies in computational intelligence ; Volume 1063

Note: Intro -- Preface -- Contents -- 1 Fundamentals of Metaheuristic Computation -- 1.1 Formulation of an Optimization Problem -- 1.2 Classical Optimization Methods -- 1.3 Metaheuristic Computation Schemes -- 1.4 Generic Structure of a Metaheuristic Method -- References -- 2 A Comparative Approach for Two-Dimensional Digital IIR Filter Design Applying Different Evolutionary Computational Techniques -- 2.1 Introduction -- 2.2 Evolutionary Computation Algorithms -- 2.2.1 Particle Swarm Optimization (PSO) -- 2.2.2 Artificial Bee Colony (ABC) -- 2.2.3 Differential Evolution (DE) -- 2.2.4 Harmony Search (HS) -- 2.2.5 Gravitational Search Algorithm (GSA) -- 2.2.6 Flower Pollination Algorithm (FPA) -- 2.3 2D-IIR Filter Design Procedure -- 2.3.1 Comparative Parameter Setting -- 2.4 Experimental Results -- 2.4.1 Accuracy Comparison -- 2.4.2 Convergence Study -- 2.4.3 Computational Cost -- 2.4.4 Comparison with Different Bandwidth Sizes -- 2.4.5 Filter Performance Features -- 2.4.6 Statistical Non-parametrical Analysis -- 2.4.7 Filter Design Study in Images -- 2.5 Conclusions -- References -- 3 Comparison of Metaheuristics for Chaotic Systems Estimation -- 3.1 Introduction -- 3.2 Evolutionary Computation Techniques (ECT) -- 3.2.1 Particle Swarm Optimization (PSO) -- 3.2.2 Artificial Bee Colony (ABC) -- 3.2.3 Cuckoo Search (CS) -- 3.2.4 Harmony Search (HS) -- 3.2.5 Differential Evolution (DE) -- 3.2.6 Gravitational Search Algorithm (GSA) -- 3.3 Parameter Estimation for Chaotic Systems (CS) -- 3.4 Experimental Results -- 3.4.1 Chaotic System Parameter Estimation -- 3.4.2 Statistical Analysis -- 3.5 Conclusions -- References -- 4 Comparison Study of Novel Evolutionary Algorithms for Elliptical Shapes in Images -- 4.1 Introduction -- 4.2 Problem Definition -- 4.2.1 Multiple Ellipse Detection -- 4.3 Evolutionary Optimization Techniques. , 4.3.1 Grey Wolf Optimizer (GWO) Algorithm -- 4.3.2 Whale Optimizer Algorithm (WOA) -- 4.3.3 Crow Search Algorithm (CSA) -- 4.3.4 Gravitational Search Algorithm (GSA) -- 4.3.5 Cuckoo Search (CS) Method -- 4.4 Comparative Perspective of the Five Metaheuristic Methods -- 4.5 Experimental Simulation Results -- 4.5.1 Performance Metrics -- 4.5.2 Experimental Comparison Study -- 4.6 Conclusions -- References -- 5 IIR System Identification Using Several Optimization Techniques: A Review Analysis -- 5.1 Introduction -- 5.2 Evolutionary Computation (EC) Algorithms -- 5.2.1 Particle Swarm Optimization (PSO) -- 5.2.2 The Artificial Bee Colony (ABC) -- 5.2.3 The Electromagnetism-Like (EM) Technique -- 5.2.4 Cuckoo Search (CS) Technique -- 5.2.5 Flower Pollination Algorithm (FPA) -- 5.3 Formulation of IIR Model Identification -- 5.4 Experimental Results -- 5.4.1 Results of IIR Model Identification -- 5.4.2 Statistical Study -- 5.5 Conclusions -- References -- 6 Fractional-Order Estimation Using via Locust Search Algorithm -- 6.1 Introduction -- 6.2 Fractional Calculus -- 6.3 Locust Search (LS) Algorithm -- 6.3.1 Solitary Phase (A) -- 6.3.2 Social Phase (B) -- 6.4 Fractional-Order Van der Pol Oscillator -- 6.5 Problem Formulation -- 6.6 Experimental Results -- 6.7 Conclusions -- References -- 7 Comparison of Optimization Techniques for Solar Cells Parameter Identification -- 7.1 Introduction -- 7.2 Evolutionary Computation (EC) Techniques -- 7.2.1 Artificial Bee Colony (ABC) -- 7.2.2 Differential Evolution (DE) -- 7.2.3 Harmony Search (HS) -- 7.2.4 Gravitational Search Algorithm (GSA) -- 7.2.5 Particle Swarm Optimization (PSO) -- 7.2.6 Cuckoo Search (CS) Technique -- 7.2.7 Differential Search Algorithm (DSA) -- 7.2.8 Crow Search Algorithm (CSA) -- 7.2.9 Covariant Matrix Adaptation with Evolution Strategy (CMA-ES) -- 7.3 Solar Cells Modeling Process. , 7.4 Experimental Results -- 7.5 Conclusions -- References -- 8 Comparison of Metaheuristics Techniques and Agent-Based Approaches -- 8.1 Introduction -- 8.2 Agent-Based Approaches -- 8.2.1 Fire Spreading -- 8.2.2 Segregation -- 8.3 Heroes and Cowards Concept -- 8.4 An Agent-Based Approach as a Metaheuristic Method -- 8.4.1 Problem Formulation -- 8.4.2 Heroes and Cowards as a Metaheuristic Method -- 8.4.3 Computational Procedure -- 8.5 Comparison with Metaheuristic Methods -- 8.5.1 Performance Evaluation with Regard to Its Own Tuning Parameters -- 8.5.2 Performance Comparison -- 8.5.3 Convergence -- 8.5.4 Engineering Design Problems -- 8.6 Conclusions -- Appendix 8.1: List of Benchmark Functions -- Appendix 8.2: Engineering Design Problems -- References.

Additional Edition: Print version: Cuevas, Erik Analysis and Comparison of Metaheuristics Cham : Springer International Publishing AG,c2022 ISBN 9783031201042

Language: English

UID:

Format: xiv, 220 Seiten : , Illustrationen, Diagramme (überwiegend farbig).

ISBN: 978-3-031-20104-2

Series Statement: Studies in computational intelligence volume 1063

Additional Edition: Erscheint auch als Online-Ausgabe ISBN 978-3-031-20105-9

Language: English

Subjects: Computer Science