Benchmarks and Hybrid Algorithms in Optimization and Applications - Info and Reading Options

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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

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