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

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

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

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

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