| Description |
1 online resource (281 pages) |
| Series |
Intelligent Systems Reference Library ; volume195 |
|
Intelligent systems reference library ; v. 195.
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| Contents |
Intro -- Preface -- Contents -- 1 Introductory Concepts of Metaheuristic Computation -- 1.1 Formulation of an Optimization Problem -- 1.2 Classical Optimization Methods -- 1.3 Metaheuristic Computation Schemes -- 1.3.1 Generic Structure of a Metaheuristic Method -- References -- 2 An Enhanced Swarm Method Based on the Locust Search Algorithm -- 2.1 Introduction -- 2.2 The Locust Search Algorithm -- 2.2.1 LS Solitary Phase -- 2.2.2 LS Social Phase -- 2.3 The LS-II Algorithm -- 2.3.1 Selecting Between Solitary and Social Phases -- 2.3.2 Modified Social Phase Operator |
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2.4 Experiments and Results -- 2.4.1 Benchmark Test Functions -- 2.4.2 Engineering Optimization Problems -- 2.5 Conclusions -- Appendix A -- Appendix B -- B2.1 Pressure Vessel Design Problem -- B2.2 Gear Train Design Problem -- B2.3 Tension/Compression Spring Design Problem -- B2.4 Three-Bar Truss Design Problem -- B2.5 Welded Beam Design Problem -- B2.6. Parameter Estimation for FM Synthesizers -- B2.7 Optimal Capacitor Placement for the IEEE's 69-Bus Radial Distribution Networks -- References -- 3 A Metaheuristic Methodology Based on Fuzzy Logic Principles -- 3.1 Introduction |
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3.2 Fuzzy Logic and Reasoning Models -- 3.2.1 Fuzzy Logic Concepts -- 3.2.2 The Takagi-Sugeno (TS) Fuzzy Model -- 3.3 The Proposed Methodology -- 3.3.1 Optimization Strategy -- 3.3.2 Computational Procedure -- 3.4 Discussion About the Proposed Methodology -- 3.4.1 Optimization Algorithm -- 3.4.2 Modeling Characteristics -- 3.5 Experimental Study -- 3.5.1 Performance Evaluation with Regard to Its Own Tuning Parameters -- 3.5.2 Comparison with Other Optimization Approaches -- 3.6 Conclusions -- Appendix A. List of Benchmark Functions -- References |
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4 A Metaheuristic Computation Scheme to Solve Energy Problems -- 4.1 Introduction -- 4.2 Crow Search Algorithm (CSA) -- 4.3 The Proposed Improved Crow Search Algorithm (ICSA) -- 4.3.1 Dynamic Awareness Probability (DAP) -- 4.3.2 Random Movement-Lévy Flight -- 4.4 Motor Parameter Estimation Formulation -- 4.4.1 Approximate Circuit Model -- 4.4.2 Exact Circuit Model -- 4.5 Capacitor Allocation Problem Formulation -- 4.5.1 Load Flow Analysis -- 4.5.2 Mathematical Approach -- 4.5.3 Sensitivity Analysis and Loss Sensitivity Factor -- 4.6 Experiments -- 4.6.1 Motor Parameter Estimation Test |
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4.6.2 Capacitor Allocation Test -- 4.7 Conclusions -- Appendix A: Systems Data -- References -- 5 ANFIS-Hammerstein Model for Nonlinear Systems Identification Using GSA -- 5.1 Introduction -- 5.2 Background -- 5.2.1 Hybrid ANFIS Models -- 5.2.2 Adaptive Neuro-Fuzzy Inference System (ANFIS) -- 5.2.3 Gravitational Search Algorithm (GSA) -- 5.3 Hammerstein Model Identification by Using GSA -- 5.4 Experimental Study -- 5.4.1 Experiment I -- 5.4.2 Experiment II -- 5.4.3 Experiment III -- 5.4.4 Experiment IV -- 5.4.5 Experiment V -- 5.4.6 Experiment VI -- 5.4.7 Experiment VII |
| Summary |
This book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. Metaheuristic search methods are so numerous and varied in terms of design and potential applications; however, for such an abundant family of optimization techniques, there seems to be a question which needs to be answered: Which part of the design in a metaheuristic algorithm contributes more to its better performance? Several works that compare the performance among metaheuristic approaches have been reported in the literature. Nevertheless, they suffer from one of the following limitations: (A)Their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. (B) Their conclusions consider only the comparison of their final results which cannot evaluate the nature of a good or bad balance between exploration and exploitation. The objective of this book is to compare the performance of various metaheuristic techniques when they are faced with complex optimization problems extracted from different engineering domains. The material has been compiled from a teaching perspective |
| Bibliography |
Includes bibliographical references |
| Notes |
Online resource; title from digital title page (viewed on December 11, 2020) |
| Subject |
Metaheuristics.
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Metaheuristics
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| Form |
Electronic book
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| Author |
Diaz, Primitivo, author
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Camarena, Octavio, author
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| ISBN |
9783030581008 |
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3030581004 |
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