Benchmarks and hybrid algorithms in optimization and applications

Benchmarks and hybrid algorithms in optimization and applications / Xin-She Yang, editor.

This book is specially focused on the latest developments and findings on hybrid algorithms and benchmarks in optimization and their applications in sciences, engineering, and industries. The book also provides some comprehensive reviews and surveys on implementations and coding aspects of benchmark...

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Bibliographic Details
Other Authors: Yang, Xin-She
Format: eBook
Language:English
Published: Singapore : Springer, [2023]
Series:Springer Tracts in Nature-Inspired Computing
Subjects:
Mathematical optimization.
Mathematical optimization
Online Access:Click for online access
Table of Contents:
  • 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

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