Metaheuristic computation with MATLAB
Metaheuristic computation with MATLAB:
Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: Cuevas, Erik Valdemar (VerfasserIn), Rodríguez, Alma (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Boca Raton ; London ; New York CRC Press 2021
Ausgabe:First edition
Online-Zugang:TUM01
Beschreibung:Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- Authors -- Chapter 1 Introduction and Main Concepts -- 1.1 Introduction -- 1.2 Classical Optimization Methods -- 1.2.1 The Gradient Descent Method -- 1.2.2 Gradient Computation -- 1.2.3 Computational Example in MATLAB -- 1.3 Metaheuristic Methods -- 1.3.1 The Generic Procedure of a Metaheuristic Algorithm -- 1.4 Exploitation and Exploration -- 1.5 Probabilistic Decision and Selection -- 1.5.1 Probabilistic Decision -- 1.5.2 Probabilistic Selection -- 1.6 Random Search -- 1.6.1 Computational Implementation in MATLAB -- 1.7 Simulated Annealing -- 1.7.1 Computational Example in MATLAB -- Exercises -- References -- Chapter 2 Genetic Algorithms (GA) -- 2.1 Introduction -- 2.2 Binary Ga -- 2.2.1 Selection Operator -- 2.2.2 Binary Crossover Operator -- 2.2.3 Binary Mutation -- 2.2.4 Computational Procedure -- 2.3 Ga With Real Parameters -- 2.3.1 Real-Parameter Crossover Operator -- 2.3.2 Real-Parameter Mutation Operator -- 2.3.3 Computational Procedure -- References -- Chapter 3 Evolutionary Strategies (ES) -- 3.1 Introduction -- 3.2 The (1 + 1) ES -- 3.2.1 Initialization -- 3.2.2 Mutation -- 3.2.3 Selection -- 3.3 Computational Procedure of the (1 + 1) ES -- 3.3.1 Description of the Algorithm (1 + 1) ES -- 3.4 Matlab Implementation of Algorithm (1 + 1) ES -- 3.5 ES Variants -- 3.5.1 Adaptive (1 + 1) ES -- 3.5.2 (μ +1) ES -- 3.5.3 (μ + &amp -- #955 -- ) ES -- 3.5.4 (μ, &amp -- #955 -- ) ES -- 3.5.5 (μ, α, &amp -- #955 -- β, ) ES -- 3.5.6 Adaptive (μ + &amp -- #955 -- ) ES and (μ, &amp -- #955 -- ) ES -- References -- Chapter 4 Moth-Flame Optimization (MFO) Algorithm -- 4.1 MFO Metaphor -- 4.2 MFO Search Strategy -- 4.2.1 Initialization -- 4.2.2 Cross Orientation -- 4.2.3 Other Mechanisms for the Balance of Exploration-Exploitation
4.2.4 MFO Variants -- 4.3 MFO Computation Procedure -- 4.3.1 Algorithm Description -- 4.4 Implementation of MFO in Matlab -- 4.5 Applications of MFO -- 4.5.1 Application of the MFO to Unconstrained Problems -- 4.5.2 Application of the MFO to Problems with Constrained -- References -- Chapter 5 Differential Evolution (DE) -- 5.1 Introduction -- 5.2 DE Search Strategy -- 5.2.1 Population Structure -- 5.2.2 Initialization -- 5.2.3 Mutation -- 5.2.4 Crossover -- 5.2.5 Selection -- 5.3 Computational Process of DE -- 5.3.1 Implementation of the DE Scheme -- 5.3.2 The General Process of DE -- 5.4 Matlab Implementation of DE -- 5.5 Spring Design Using the DE Algorithm -- References -- Chapter 6 Particle Swarm Optimization (PSO) Algorithm -- 6.1 INTRODUCTION -- 6.2 PSO Search Strategy -- 6.2.1 Initialization -- 6.2.2 Particle Velocity -- 6.2.3 Particle Movement -- 6.2.4 PSO Analysis -- 6.2.5 Inertia Weighting -- 6.3 Computing Procedure of PSO -- 6.3.1 Algorithm Description -- 6.4 Matlab Implementation of the PSO Algorithm -- 6.5 Applications of the PSO Method -- 6.5.1 Application of PSO without Constraints -- 6.5.2 Application of the PSO to Problems with Constraints -- References -- Chapter 7 Artificial Bee Colony (ABC) Algorithm -- 7.1 Introduction -- 7.2 Artificial Bee Colony -- 7.2.1 Initialization of the Population -- 7.2.2 Sending Worker Bees -- 7.2.3 Selecting Food Sources by Onlooker Bees -- 7.2.4 Determining the Exploring Bees -- 7.2.5 Computational Process ABC -- 7.2.6 Computational Example in MATLAB -- 7.3 Recent Applications of the ABC Algorithm in Image Processing -- 7.3.1 Applications in the Area of Image Processing -- 7.3.1.1 Image Enhancement -- 7.3.1.2 Image Compression -- 7.3.1.3 Border Detection -- 7.3.1.4 Clustering -- 7.3.1.5 Image Classification -- 7.3.1.6 Fusion in Images -- 7.3.1.7 Scene Analysis -- 7.3.1.8 Pattern Recognition
7.3.1.9 Object Detection -- References -- Chapter 8 Cuckoo Search (CS) Algorithm -- 8.1 Introduction -- 8.2 CS Strategy -- 8.2.1 Lévy Flight (A) -- 8.2.2 Replace Some Nests by Constructing New Solutions (B) -- 8.2.3 Elitist Selection Strategy (C) -- 8.2.4 Complete CS Algorithm -- 8.3 CS Computational Procedure -- 8.4 The Multimodal Cuckoo Search (MCS) -- 8.4.1 Memory Mechanism (D) -- 8.4.1.1 Initialization Phase -- 8.4.1.2 Capture Phase -- 8.4.1.3 Significant Fitness Value Rule -- 8.4.1.4 Non-Significant Fitness Value Rule -- 8.4.2 New Selection Strategy (E) -- 8.4.3 Depuration Procedure (F) -- 8.4.4 Complete MCS Algorithm -- 8.5 Analysis of CS -- 8.5.1 Experimental Methodology -- 8.5.2 Comparing MCS Performance for Functions f[sub(1)]f[sub(7)] -- 8.5.3 Comparing MCS Performance for Functions f[sub(8)]f[sub(14)] -- References -- Chapter 9 Metaheuristic Multimodal Optimization -- 9.1 Introduction -- 9.2 Diversity Through Mutation -- 9.3 Preselection -- 9.4 Crowding Model -- 9.5 Sharing Function Model -- 9.5.1 Numerical Example for Sharing Function Calculation -- 9.5.2 Computational Example in MATLAB -- 9.5.3 Genetic Algorithm without Multimodal Capacities -- 9.5.4 Genetic Algorithm with Multimodal Capacities -- 9.6 Firefly Algorithm -- 9.6.1 Computational Example in MATLAB -- Exercises -- References -- Index
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ISBN:9781000096514
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