Nature-inspired computing and optimization : theory and applications /: theory and applications. (2017)
- Record Type:
- Book
- Title:
- Nature-inspired computing and optimization : theory and applications /: theory and applications. (2017)
- Main Title:
- Nature-inspired computing and optimization : theory and applications
- Further Information:
- Note: Srikanta Patnaik, Xin-She Yang, Kazumi Nakamatsu, editors.
- Editors:
- Patnaik, Srikanta
Yang, Xin-She
Nakamatsu, Kazumi - Contents:
- 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. 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. 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; 5Preface; 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. 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. 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. 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. Hybridization of the Flower Pollination Algorithm -- A Case Study in the Problem of Generating Healthy Nutritional Meals for Older Adults. … (more)
- Publisher Details:
- Cham, Switzerland : Springer
- Publication Date:
- 2017
- Extent:
- 1 online resource (xxi, 494 pages), illustrations (some color)
- Subjects:
- 006.3/8
Engineering
Natural computation
Mathematical optimization
COMPUTERS -- General
COMPUTERS -- Mathematical & Statistical Software
Mathematical optimization
Natural computation
Mathematics -- Applied
Computers -- Intelligence (AI) & Semantics
Computers -- Computer Simulation
Technology & Engineering -- General
Optimization
Artificial intelligence
Computer modelling & simulation
Engineering: general
Artificial intelligence
Computer simulation
Engineering economy
Electronic books - Languages:
- English
- ISBNs:
- 9783319509204
3319509209
3319509195
9783319509198 - Related ISBNs:
- 9783319509198
- Notes:
- Note: Includes bibliographical references.
Note: Online resource; title from PDF title page (SpringerLink, viewed March 21, 2017). - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
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- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.357254
- Ingest File:
- 01_317.xml