Analysis and Comparison of Metaheuristics

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