Intro
Preface
Contents
1 Nature-Inspired Algorithms in Optimization: Introduction, Hybridization, and Insights
1 Introduction
2 Optimization and Algorithms
2.1 Components of Optimization
2.2 Gradients and Optimization
3 Nature-Inspired Algorithms
3.1 Recent Nature-Inspired Algorithms
3.2 Other Nature-inspired Algorithms
4 Hybridization
4.1 Hybridization Schemes
4.2 Issues and Warnings
5 Insights and Recommendations
References
2 Ten New Benchmarks for Optimization
1 Introduction
2 Role of Benchmarks
3 New Benchmark Functions

3.1 Noisy Functions
3.2 Non-differentiable Functions
3.3 Functions with Isolated Domains
4 Benchmarks with Multiple Optimal Solutions
4.1 Function on a Hyperboloid
4.2 Non-smooth Multi-layered Functions
5 Parameter Estimation as Benchmarks
6 Integrals as Benchmarks
7 Benchmarks of Infinite Dimensions
7.1 Shortest Path Problem
7.2 Shape Optimization
8 Conclusions
References
3 Review of Parameter Tuning Methods for Nature-Inspired Algorithms
1 Introduction
2 Parameter Tuning
2.1 Schematic Representation of Parameter Tuning

2.2 Different Types of Optimality
2.3 Approaches to Parameter Tuning
3 Review of Parameter Tuning Methods
3.1 Generic Methods for Parameter Tuning
3.2 Online and Offline Tunings
3.3 Self-Parametrization and Fuzzy Methods
3.4 Machine Learning-Based Methods
4 Discussions and Recommendations
References
4 QOPTLib: A Quantum Computing Oriented Benchmark for Combinatorial Optimization Problems
1 Introduction
2 Description of the Problems
2.1 Traveling Salesman Problem
2.2 Vehicle Routing Problem
2.3 Bin Packing Problem
2.4 Maximum Cut Problem

3 Introducing the Generated QOPTLib Benchmarks
4 Preliminary Experimentation
5 Conclusions and Further Work
References
5 Benchmarking for Discrete Cuckoo Search: Three Case Studies
1 Introduction
2 COPs Statements
2.1 Studied COPs
2.2 Formal Definitions
3 DCS Common Resolution
3.1 General Algorithm
3.2 Main Functions
4 Studied Case Resolutions
4.1 Solutions
4.2 Moves
5 Experimental Tests
5.1 Parameters
5.2 Instances
5.3 Statistic Tests
6 Conclusion
References

6 Metaheuristics for Feature Selection: A Comprehensive Comparison Using Opytimizer
1 Introduction
2 Literature Review
3 Hands-on Opytimizer: A Python Implementation for Metaheuristic Optimization
4 Case Study: Feature Selection
4.1 Methodology
4.2 Experiments
5 Conclusions
References
7 AL4SLEO: An Active Learning Solution for the Semantic Labelling of Earth Observation Satellite Images-Part 1
1 Introduction
2 State of the Art
3 Data Set Description
4 Active Learning
5 Semantic Labelling
6 Conclusions
References