This book is a collection of various methodologies that make it possible for metaheuristics and hyper-heuristics to solve problems that occur in the real world. This book contains chapters that make use of metaheuristics techniques. The application fields range from image processing to transmission power control, and case studies and literature reviews are included to assist the reader. Furthermore, some chapters present cutting-edge methods for load frequency control and IoT implementations. In this sense, the book offers both theoretical and practical contents in the form of metaheuristic algorithms. The researchers used several stochastic optimization methods in this book, including evolutionary algorithms and Swarm-based algorithms. The chapters were written from a scientific standpoint. As a result, the book is primarily aimed at undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics, but it can also be used in courses on Artificial Intelligence, among other things. Similarly, the material may be beneficial to research in evolutionary computation and artificial intelligence communities.
Author(s): Taymaz Akan, Ahmed M. Anter, A. Şima Etaner-Uyar, Diego Oliva
Series: Studies in Computational Intelligence, 1069
Publisher: Springer
Year: 2022
Language: English
Pages: 208
City: Cham
Contents
Empirical Comparison of Heuristic Optimisation Methods for Automated Car Setup
1 Introduction
2 The Open Racing Car Simulator (TORCS)
3 Heuristics, Metaheuristics and Hyper-Heuristics
3.1 Selection Hyper-Heuristics
3.2 Covariance Matrix Adaptation Evolutionary Strategy
3.3 Closest Best Closest Worst Particle Swarm Optimisation
4 Experimental Design
5 Results and Discussion
5.1 Comparison of Heuristic Selection Methods
5.2 Comparison of Individual Low Level Heuristics
5.3 Comparison with Other Methods
6 Conclusion
References
Metaheuristic Algorithms in IoT: Optimized Edge Node Localization
1 Introduction
2 Related Works
3 Proposed Method
3.1 The Grey Wolf Optimizer (GWO)
3.2 Moth-Flame Optimization (MFO)
3.3 Hybrid Algorithm (GWOMFO)
4 Results Analysis
4.1 Benchmark Functions (CEC2015)
4.2 Benchmark Functions (CEC2019)
4.3 Edge Node Localization Problem
5 Conclusion
References
JAYA Algorithm Versus Differential Evolution: A Comparative Case Study on Optic Disc Localization in Eye Fundus Images
1 Introduction
2 JAYA Algorithm for Optic Disc Localization
2.1 Fitness Evaluation
3 Performance Analysis
4 Conclusion
References
Minimum Transmission Power Control for the Internet of Things with Swarm Intelligence Algorithms
1 Introduction
1.1 Main Contributions of the Study
2 Literature Review
3 Material and Methods
3.1 Minimum Transmission Power Control
3.2 Particle Swarm Optimization (PSO)
3.3 Artificial Bee Colony (ABC)
3.4 Salp Swarm Algorithm (SSA)
3.5 Tree-Seed Algorithm (TSA)
4 Experimental Setup
5 Results and Discussions
6 Conclusion
References
An Enhanced Gradient Based Optimized Controller for Load Frequency Control of a Two Area Automatic Generation Control System
1 Introduction
2 Mathematical Model of the System
2.1 Governor Model
2.2 Turbine Model
2.3 Generator-Load Model
2.4 Tie-Line
3 Optimization Techniques
3.1 Gradient-Based Optimizer
3.2 Enhanced Gradient-Based Optimizer
4 Controller Structure with Problem Formulation
5 Study Results and Discussions
5.1 Case Studies
5.2 Convergence Comparison of the Optimizers
5.3 Comparative Statistical Analysis
6 Conclusion
Appendix A
Appendix B
Appendix C
Appendix D
References
A Meta-Heuristic Algorithm Based on the Happiness Model
1 Introduction
2 Happiness Optimizer
2.1 Inspiration
3 Comparative Study
3.1 Statistical Discussion
3.2 Real Problem
4 Conclusions
References
Application of Metaheuristic Techniques for Enhancing the Financial Profitability of Wind Power Generation Systems
1 Introduction
2 Objective Formulation
2.1 Annual Profit Calculation
2.2 Wind Flow Pattern
2.3 Terrain Conditions
3 Optimization Algorithms
3.1 Binary Particle Swarm Optimization Algorithm (BPSOA)
3.2 Genetic Algorithm (GA)
3.3 Proposed Enhanced GA
4 Results and Discussion
5 Conclusion
References
Optimization of Demand Response
1 Introduction
2 Demand Response
3 Loads
4 Renewable Energy Sources and Storage Systems
4.1 Renewable Energy Sources
4.2 Storage Systems
5 Tariff
6 User Comfort
7 Problem Formulation
8 Optimization Techniques
References
Fitting Curves of Ruminal Degradation Using a Metaheuristic Approach
1 Introduction
2 Materials and Methods
2.1 Data Collection
2.2 Particle Swarm Optimization (PSO)
2.3 Curve Fitting
3 Proposed Algorithm
3.1 Fitness Function
4 Results and Discussion
5 Conclusions
References
Optimizing a Real Case Assembly Line Balancing Problem Using Various Techniques
1 Introduction
2 Literature Review
3 Problem Definition and Formulation
4 Methodology
5 Computational Results
5.1 Straight Line
5.2 U-Shaped Line
6 Conclusion
References
Multi-circle Detection Using Multimodal Optimization
1 Introduction
2 Particle Swarm Optimization
2.1 Multimodal PSO
2.2 Local Search PSO
3 Proposed Method
4 Experimental Results
4.1 EPSO Based Multiple Circle Detection Application Results
4.2 EPSO Based Multiple Circle Detection Application Performance Results
4.3 Comparison of Standard Hough Transform with EPSO Based Multiple Circle Detection
5 Conclusions
References
