Analysis and comparison of metaheuristics / / Erik Cuevas, Omar Avalos, Jorge Gálvez

• Record Nr.: UNINA9910629280803321
• Autore: Cuevas Erik
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2023]; ©2023
• ISBN: 3-031-20105-1
• Descrizione fisica: 1 online resource (230 pages)
• Collana: Studies in computational intelligence ; ; Volume 1063
• Disciplina: 005.1
• Soggetti: Metaheuristics; Computer algorithms
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: 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.