Metaheuristic Computation: A Performance Perspective

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
		5.4.8 Statistical Analysis
	5.5 Conclusions and Further Research
	References
6 A States of Matter Search-Based Scheme to Solve the Problem of Power Allocation in Plug-in Electric Cars
	6.1 Introduction
	6.2 Problem Formulation
	6.3 The States of Matter Search (SMS) Algorithm
		6.3.1 States of Matter Transition
		6.3.2 Molecule Movement Operators
	6.4 SMS-Based Smart Power Allocation for PHEVs
	6.5 Experimental Results
	6.6 Conclusions
	References
7 Locus Search Method for Power Loss Reduction on Distribution Networks
	7.1 Introduction
	7.2 Capacitor Allocation Problem Formulation
		7.2.1 Power Loss Calculation
		7.2.2 Voltage Constrains
	7.3 The Locust Search Algorithm
		7.3.1 LS Solitary Phase
		7.3.2 LS Social Phase
	7.4 Optimal Capacitor Allocation Based on LS-Algorithm
	7.5 Experimental Results
		7.5.1 Statistical Results of Test Cases
		7.5.2 Comparative Results for IEEE’s 10-Bus Distribution System
		7.5.3 Comparative Results for IEEE’s 33-Bus Distribution System
		7.5.4 Comparative Results for IEEE’s 69-Bus Distribution System
	7.6 Conclusions
	Appendix
	References
8 Blood Vessel and Optic Disc Segmentation Based on a Metaheuristic Method
	8.1 Introduction
	8.2 Preliminary Concepts
		8.2.1 Lateral Inhibition
		8.2.2 Cross Entropy
		8.2.3 Differential Evolution Algorithm
	8.3 Methodology
		8.3.1 Pre-processing
		8.3.2 Processing
		8.3.3 Post-processing
	8.4 Experimental Results
	8.5 Conclusions
	References
9 Detection of White Blood Cells with Metaheuristic Computation
	9.1 Introduction
	9.2 Differential Evolution
	9.3 Ellipse Detection Under an Optimization Perspective
	9.4 Ellipse Detector Process
	9.5 Experimental Results
	9.6 Conclusions
	References
10 Experimental Analysis Between Exploration and Exploitation
	10.1 Introduction
	10.2 Exploration and Exploitation
	10.3 Exploration-Exploitation Evaluation
	10.4 Experimental Results
	10.5 Discussion
	10.6 Conclusion
	References