Nature-inspired computing and optimization : theory and applications
Nature-inspired computing and optimization : theory and applications
Srikanta Patnaik, Xin-She Yang, Kazumi Nakamatsu, editors.
- 1 recurso en línea (xxi, 494 páginas) ilustraciones (algunas a color)
- Modeling and optimization in science and technologies volume 10 2196-7326 .
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Preface; Contents; Contributors; Members of Review Board; The Nature of Nature: Why Nature-Inspired Algorithms Work; 1 Introduction: How Nature Works; 2 The Nature of Nature; 2.1 Fitness Landscape; 2.2 Graphs and Phase Changes; 3 Nature-Inspired Algorithms; 3.1 Genetic Algorithm; 3.2 Ant Colony Optimization; 3.3 Simulated Annealing; 3.4 Convergence; 4 Dual-Phase Evolution; 4.1 Theory; 4.2 GA; 4.3 Ant Colony Optimization; 4.4 Simulated Annealing; 5 Evolutionary Dynamics; 5.1 Markov Chain Models; 5.2 The Replicator Equation; 6 Generalized Local Search Machines; 6.1 The Model; 6.2 SA; 6.3 GA. 2.1 Bin Packing Problem2.2 Graph Colouring Problem; 2.3 Travelling Salesman Problem; 3 Previous Work on Small Travelling Salesman Problem Instances; 4 Algorithms; 4.1 2-Opt; 4.2 Lin -- Kernighan; 4.3 Genetic Algorithm Variations; 4.4 Representation; 5 Experimental Design; 5.1 Bin Packing Problem; 5.2 Graph Colouring Problem; 5.3 Travelling Salesman Problem; 6 Results and Discussion; 6.1 Bin Packing Problem; 6.2 Graph Colouring Problem; 6.3 Travelling Salesman Problem; 7 Conclusions; References. 2.1 Combinatorial Optimisation Problems2.2 Multi-objective Combinatorial Optimisation Problems; 2.3 Pareto Optimality; 2.4 Decision-Making; 2.5 Solving Combinatorial Optimisation Problems; 3 Multi-objective Ant Colony Optimisation; 3.1 Origins; 3.2 Multi-objective Ant Colony Optimisation; 4 Applications of MOACO Algorithms in WSNs; 5 Conclusion; References; Generating the Training Plans Based on Existing Sports Activities Using Swarm Intelligence; 1 Introduction; 2 Artificial Sports Trainer; 3 Generating the Training Plans; 3.1 Preprocessing; 3.2 Optimization Process; 4 Experiments. 5 Conclusion with Future IdeasReferences; Limiting Distribution and Mixing Time for Genetic Algorithms; 1 Introduction; 2 Preliminaries; 2.1 Random Search and Markov Chains; 2.2 Boltzmann Distribution and Simulated Annealing; 3 Expected Hitting Time as a Means of Comparison; 3.1 ``No Free Lunch'' Considerations; 4 The Holland Genetic Algorithm; 5 A Simple Genetic Algorithm; 6 Shuffle-Bit GA; 6.1 Results; 6.2 Estimate of Expected Hitting Time; 7 Discussion and Future Work; References; Permutation Problems, Genetic Algorithms, and Dynamic Representations; 1 Introduction; 2 Problem Descriptions. 6.4 ACO6.5 Discussion; 7 Conclusion; References; Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environments; 1 Introduction; 2 Improved Bat Algorithm; 3 IBA for Multimodal Problems; 3.1 Parameter Settings; 3.2 Test Functions; 3.3 Numerical Results; 4 Performance Comparison of IBA with Other Algorithms; 5 IBA Performance in AWGN; 5.1 Numerical Results; 6 Conclusions; References; Multi-objective Ant Colony Optimisation in Wireless Sensor Networks; 1 Introduction; 2 Multi-objective Combinatorial Optimisation Problems. Hybridization of the Flower Pollination Algorithm -- A Case Study in the Problem of Generating Healthy Nutritional Meals for Older Adults.
The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals.
3319509195 3319509209 9783319509198 9783319509204
Optimización matemática
QA76.9.N37 / N388 2017 EB
SpringerLink
Incluye referencias bibliográficas
Preface; Contents; Contributors; Members of Review Board; The Nature of Nature: Why Nature-Inspired Algorithms Work; 1 Introduction: How Nature Works; 2 The Nature of Nature; 2.1 Fitness Landscape; 2.2 Graphs and Phase Changes; 3 Nature-Inspired Algorithms; 3.1 Genetic Algorithm; 3.2 Ant Colony Optimization; 3.3 Simulated Annealing; 3.4 Convergence; 4 Dual-Phase Evolution; 4.1 Theory; 4.2 GA; 4.3 Ant Colony Optimization; 4.4 Simulated Annealing; 5 Evolutionary Dynamics; 5.1 Markov Chain Models; 5.2 The Replicator Equation; 6 Generalized Local Search Machines; 6.1 The Model; 6.2 SA; 6.3 GA. 2.1 Bin Packing Problem2.2 Graph Colouring Problem; 2.3 Travelling Salesman Problem; 3 Previous Work on Small Travelling Salesman Problem Instances; 4 Algorithms; 4.1 2-Opt; 4.2 Lin -- Kernighan; 4.3 Genetic Algorithm Variations; 4.4 Representation; 5 Experimental Design; 5.1 Bin Packing Problem; 5.2 Graph Colouring Problem; 5.3 Travelling Salesman Problem; 6 Results and Discussion; 6.1 Bin Packing Problem; 6.2 Graph Colouring Problem; 6.3 Travelling Salesman Problem; 7 Conclusions; References. 2.1 Combinatorial Optimisation Problems2.2 Multi-objective Combinatorial Optimisation Problems; 2.3 Pareto Optimality; 2.4 Decision-Making; 2.5 Solving Combinatorial Optimisation Problems; 3 Multi-objective Ant Colony Optimisation; 3.1 Origins; 3.2 Multi-objective Ant Colony Optimisation; 4 Applications of MOACO Algorithms in WSNs; 5 Conclusion; References; Generating the Training Plans Based on Existing Sports Activities Using Swarm Intelligence; 1 Introduction; 2 Artificial Sports Trainer; 3 Generating the Training Plans; 3.1 Preprocessing; 3.2 Optimization Process; 4 Experiments. 5 Conclusion with Future IdeasReferences; Limiting Distribution and Mixing Time for Genetic Algorithms; 1 Introduction; 2 Preliminaries; 2.1 Random Search and Markov Chains; 2.2 Boltzmann Distribution and Simulated Annealing; 3 Expected Hitting Time as a Means of Comparison; 3.1 ``No Free Lunch'' Considerations; 4 The Holland Genetic Algorithm; 5 A Simple Genetic Algorithm; 6 Shuffle-Bit GA; 6.1 Results; 6.2 Estimate of Expected Hitting Time; 7 Discussion and Future Work; References; Permutation Problems, Genetic Algorithms, and Dynamic Representations; 1 Introduction; 2 Problem Descriptions. 6.4 ACO6.5 Discussion; 7 Conclusion; References; Multimodal Function Optimization Using an Improved Bat Algorithm in Noise-Free and Noisy Environments; 1 Introduction; 2 Improved Bat Algorithm; 3 IBA for Multimodal Problems; 3.1 Parameter Settings; 3.2 Test Functions; 3.3 Numerical Results; 4 Performance Comparison of IBA with Other Algorithms; 5 IBA Performance in AWGN; 5.1 Numerical Results; 6 Conclusions; References; Multi-objective Ant Colony Optimisation in Wireless Sensor Networks; 1 Introduction; 2 Multi-objective Combinatorial Optimisation Problems. Hybridization of the Flower Pollination Algorithm -- A Case Study in the Problem of Generating Healthy Nutritional Meals for Older Adults.
The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based optimization algorithm, the flower pollination algorithm, multi-agent systems and particle swarm optimization. This timely book is intended as a practice-oriented reference guide for students, researchers and professionals.
3319509195 3319509209 9783319509198 9783319509204
Optimización matemática
QA76.9.N37 / N388 2017 EB