This book presents a comparative perspective of current metaheuristic developments, which have proved to be effective in their application to several complex problems. The study of biological and social entities such as animals, humans, or insects that manifest a cooperative behavior has produced several computational models in metaheuristic methods. Although these schemes emulate very different processes or systems, the rules used to model individual behavior are very similar. Under such conditions, it is not clear to identify which are the advantages or disadvantages of each metaheuristic technique. The book is compiled from a teaching perspective. For this reason, the book is primarily intended for undergraduate and postgraduate students of Science, Electrical Engineering, or Computational Mathematics. It is appropriate for courses such as Artificial Intelligence, Electrical Engineering, Evolutionary Computation. The book is also useful for researchers from the evolutionary and engineering communities. Likewise, engineer practitioners, who are not familiar with metaheuristic computation concepts, will appreciate that the techniques discussed are beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise in engineering areas.
Author(s): Erik Cuevas, Omar Avalos, Jorge Gálvez
Series: Studies in Computational Intelligence, 1063
Publisher: Springer
Year: 2022
Language: English
Pages: 229
City: Cham
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
