Nature-inspired computing and optimization : theory and applications
The text provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimisation. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications.
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| Main Authors | , , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Cham
Springer
2017
Springer International Publishing AG Springer International Publishing |
| Edition | 1 |
| Series | Modeling and Optimization in Science and Technologies |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783319509198 3319509195 |
| ISSN | 2196-7326 2196-7334 |
| DOI | 10.1007/978-3-319-50920-4 |
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| Abstract | The text provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimisation. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. |
|---|---|
| AbstractList | The text provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimisation. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. |
| Author | Yang, Xin-She Patnaik, Srikanta 中松, 和巳 |
| Author_FL | ナカマツ, カズミ |
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| Author_xml | – sequence: 1 fullname: Patnaik, Srikanta – sequence: 2 fullname: Yang, Xin-She – sequence: 3 fullname: 中松, 和巳 |
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| Copyright | Springer International Publishing AG 2017 |
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| DOI | 10.1007/978-3-319-50920-4 |
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| Discipline | Computer Science Applied Sciences Mathematics Engineering |
| EISBN | 9783319509204 3319509209 |
| EISSN | 2196-7334 |
| Edition | 1 |
| Editor | Yang, Xin-She Patnaik, Srikanta Nakamatsu, Kazumi |
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| ISSN | 2196-7326 |
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| Notes | Includes bibliographical references and index |
| OCLC | 974947287 |
| PQID | EBC4820330 |
| PageCount | 506 |
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| PublicationDate | 2017 20170309 2017-03-07 |
| PublicationDateYYYYMMDD | 2017-01-01 2017-03-09 2017-03-07 |
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| PublicationSeriesTitle | Modeling and Optimization in Science and Technologies |
| PublicationSeriesTitleAlternate | Modeling and Optimization in Science and Technologies |
| PublicationYear | 2017 |
| Publisher | Springer Springer International Publishing AG Springer International Publishing |
| Publisher_xml | – name: Springer – name: Springer International Publishing AG – name: Springer International Publishing |
| RelatedPersons | Patnaik, Srikanta Sethi, Ishwar K. Li, Xiaolong |
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| Snippet | The text provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimisation. The approach... |
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| SubjectTerms | Artificial Intelligence Computational Intelligence Engineering Engineering Economics, Organization, Logistics, Marketing Engineering economy Natural computation Optimization Simulation and Modeling |
| TableOfContents | 4 Experiments -- 5 Conclusion with Future Ideas -- References -- 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 Problem -- 2.2 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 -- 1 Introduction -- 2 Background -- 2.1 Optimization Problems -- 2.2 Meta-Heuristic Algorithms -- 3 Literature Review -- 4 Problem Definition -- 4.1 Search Space and Solution Representation -- 4.2 Fitness Function -- 4.3 Constraints -- 5 Hybridizing the Flower Pollination Algorithm for Generating Personalized Menu Recommendations -- 5.1 Hybrid Flower Pollination-Based Model -- 5.2 Flower Pollination-Based Algorithms for Generating Personalized Menu Recommendations 5.3 The Iterative Stage of the Hybrid Flower Pollination-Based Algorithm for Generating Healthy Menu Recommendations -- 6 Performance Evaluation -- 6.1 Experimental Prototype -- 6.2 Test Scenarios -- 6.3 Setting the Optimal Values of the Algorithms' Adjustable Parameters -- 6.4 Comparison Between the Classical and Hybrid Flower Pollination-Based Algorithms -- 7 Conclusions -- References -- Nature-inspired Algorithm-based Optimization for Beamforming of Linear Antenna Array System -- 1 Introduction -- 2 Problem Formulation -- 3 Flower Pollination Algorithm [55] -- 3.1 Global Pollination: -- 3.2 Local Pollination: -- 3.3 Pseudo-code for FPA: -- 4 Simulation Results -- 4.1 Optimization of Hyper-Beam by Using FPA -- 4.2 Comparisons of Accuracies Based on t test -- 5 Convergence Characteristics of Different Algorithms -- 6 Conclusion -- 7 Future Research Topics -- References -- Multi-Agent Optimization of Resource-Constrained Project Scheduling Problem Using Nature-Inspired Computing -- 1 Introduction -- 1.1 Multi-agent System -- 1.2 Scheduling -- 1.3 Nature-Inspired Computing -- 2 Resource-Constrained Project Scheduling Problem -- 3 Various Nature-Inspired Computation Techniques for RCPSP -- 3.1 Particle Swarm Optimization (PSO) -- 3.2 Particle Swarm Optimization (PSO) for RCPSP -- 3.3 Ant Colony Optimization (ACO) -- 3.4 Ant Colony Optimization (ACO) for RCPSP -- 3.5 Shuffled Frog-Leaping Algorithm (SFLA) -- 3.6 Shuffled Frog-Leaping Algorithm (SFLA) for RCPSP -- 3.7 Multi-objective Invasive Weed Optimization -- 3.8 Multi-objective Invasive Weed Optimization for MRCPSP -- 3.9 Discrete Flower Pollination -- 3.10 Discrete Flower Pollination for RCPSP -- 3.11 Discrete Cuckoo Search -- 3.12 Discrete Cuckoo Search for RCPSP -- 3.13 Multi-agent Optimization Algorithm (MAOA) -- 4 Proposed Approach -- 4.1 RCPSP for Retail Industry 7 Interpretation and Intuitive Understanding of Morphological Filters in Multivalued Function Domain Intro -- 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 ACO -- 6.5 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 Problems -- 2.2 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 4.2 Cooperative Hunting Behaviour of Lion Pride -- 5 A Lion Pride-Inspired Multi-Agent System-Based Approach for RCPSP -- 6 Conclusion -- References -- Application of Learning Classifier Systems to Gene Expression Analysis in Synthetic Biology -- 1 Introduction -- 2 Learning Classifier Systems: Creating Rules that Describe Systems -- 2.1 Basic Components -- 2.2 Michigan- and Pittsburgh-style LCS -- 3 Examples of LCS -- 3.1 Minimal Classifier Systems -- 3.2 Zeroth-level Classifier Systems -- 3.3 Extended Classifier Systems -- 4 Synthetic Biology: Designing Biological Systems -- 4.1 The Synthetic Biology Design Cycle -- 4.2 Basic Biological Parts -- 4.3 DNA Construction -- 4.4 Future Applications -- 5 Gene Expression Analysis with LCS -- 6 Optimization of Artificial Operon Structure -- 7 Optimization of Artificial Operon Construction by Machine Learning -- 7.1 Introduction -- 7.2 Artificial Operon Model -- 7.3 Experimental Framework -- 7.4 Results -- 7.5 Conclusion -- 8 Summary -- References -- Ant Colony Optimization for Semantic Searching of Distributed Dynamic Multiclass Resources -- 1 Introduction -- 2 P2p Search Strategies -- 3 Nature-Inspired Ant Colony Optimization -- 4 Nature-Inspired Strategies in Dynamic Networks -- 4.1 Network Dynamism Inefficiency -- 4.2 Solution Framework -- 4.3 Experimental Evaluation -- 5 Nature-Inspired Strategies of Semantic Nature -- 5.1 Semantic Query Inefficiency -- 5.2 Solution Framework -- 5.3 Experimental Evaluation -- 6 Conclusions and Future Developments -- References -- Adaptive Virtual Topology Control Based on Attractor Selection -- 1 Introduction -- 2 Related Work -- 3 Attractor Selection -- 3.1 Concept of Attractor Selection -- 3.2 Cell Model -- 3.3 Mathematical Model of Attractor Selection -- 4 Virtual Topology Control Based on Attractor Selection -- 4.1 Virtual Topology Control 4.2 Overview of Virtual Topology Control Based on Attractor Selection -- 4.3 Dynamics of Virtual Topology Control -- 4.4 Attractor Structure -- 4.5 Dynamic Reconfiguration of Attractor Structure -- 5 Performance Evaluation -- 5.1 Simulation Conditions -- 5.2 Dynamics of Virtual Topology Control Based on Attractor Selection -- 5.3 Adaptability to Node Failures -- 5.4 Effects of Noise Strength -- 5.5 Effects of Activity -- 5.6 Effects of Reconfiguration Methods of Attractor Structure -- 6 Conclusion -- References -- CBO-Based TDR Approach for Wiring Network Diagnosis -- 1 Introduction -- 2 The Proposed TDR-CBO-Based Approach -- 2.1 Problem Formulation -- 2.2 The Forward Model -- 2.3 Colliding Bodies Optimization (CBO) -- 3 Applications and Results -- 3.1 The Y-Shaped Wiring Network -- 3.2 The YY-shaped Wiring Network -- 4 Conclusion -- References -- Morphological Filters: An Inspiration from Natural Geometrical Erosion and Dilation -- 1 Natural Geometrical Inspired Operators -- 2 Mathematical Morphology -- 2.1 Morphological Filters -- 3 Morphological Operators and Set Theory -- 3.1 Sets and Corresponding Operators -- 3.2 Basic Properties for Morphological Operators -- 3.3 Set Dilation and Erosion -- 3.4 A Geometrical Interpretation of Dilation and Erosion Process -- 3.5 Direct Effect of Edges and Borders on the Erosion and Dilation -- 3.6 Closing and Opening -- 3.7 A Historical Review to Definitions and Notations -- 4 Practical Interpretation of Binary Opening and Closing -- 5 Morphological Operators in Grayscale Domain -- 5.1 Basic Morphological Operators in Multivalued Function Domain -- 5.2 Dilation and Erosion of Multivalued Functions -- 5.3 Two Forms of Presentation for Dilation and Erosion Formula -- 6 Opening and Closing of Multivalued Functions |
| Title | Nature-inspired computing and optimization : theory and applications |
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