Swarm Intelligence and Bio-Inspired Computation : Theory and Applications.

By: Yang, Xin-SheContributor(s): Cui, Zhihua | Gandomi, Amir Hossein | Karamanoglu, Mehmet | Xiao, RenbinPublisher: Oxford : Elsevier, 2013Copyright date: ©2013Description: 1 online resource (445 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 9780124051775Subject(s): Swarm intelligence.;Computational intelligenceGenre/Form: Electronic books. Additional physical formats: Print version:: Swarm Intelligence and Bio-Inspired Computation : Theory and ApplicationsDDC classification: 006.3 LOC classification: Q337.3 -- .S93 2013ebOnline resources: Click to View
Contents:
Front Cover -- Swarm Intelligence and Bio-Inspired Computation -- Copyright Page -- Contents -- List of Contributors -- Preface -- One. Theoretical Aspects of Swarm Intelligence and Bio-Inspired Computing -- 1 Swarm Intelligence and Bio-Inspired Computation: An Overview -- 1.1 Introduction -- 1.2 Current Issues in Bio-Inspired Computing -- 1.2.1 Gaps Between Theory and Practice -- 1.2.2 Classifications and Terminology -- 1.2.3 Tuning of Algorithm-Dependent Parameters -- 1.2.4 Necessity for Large-Scale and Real-World Applications -- 1.2.5 Choice of Algorithms -- 1.3 Search for the Magic Formulas for Optimization -- 1.3.1 Essence of an Algorithm -- 1.3.2 What Is an Ideal Algorithm? -- 1.3.3 Algorithms and Self-Organization -- 1.3.4 Links Between Algorithms and Self-Organization -- 1.3.5 The Magic Formulas -- 1.4 Characteristics of Metaheuristics -- 1.4.1 Intensification and Diversification -- 1.4.2 Randomization Techniques -- 1.5 Swarm-Intelligence-Based Algorithms -- 1.5.1 Ant Algorithms -- 1.5.2 Bee Algorithms -- 1.5.3 Bat Algorithm -- 1.5.4 Particle Swarm Optimization -- 1.5.5 Firefly Algorithm -- 1.5.6 Cuckoo Search -- 1.5.7 Flower Pollination Algorithm -- 1.5.8 Other Algorithms -- 1.6 Open Problems and Further Research Topics -- References -- 2 Analysis of Swarm Intelligence-Based Algorithms for Constrained Optimization -- 2.1 Introduction -- 2.2 Optimization Problems -- 2.3 Swarm Intelligence-Based Optimization Algorithms -- 2.3.1 Ant Colony Optimization -- 2.3.2 Particle Swarm Optimizer -- 2.3.3 ABC Algorithm -- 2.3.4 Glowworm Swarm Algorithm -- 2.3.5 Firefly Algorithm -- 2.3.6 Cuckoo Search Algorithm -- 2.3.7 Bat Algorithm -- 2.3.8 Hunting Search Algorithm -- 2.4 Numerical Examples -- 2.4.1 Example 1 -- 2.4.2 Example 2 -- 2.5 Summary and Conclusions -- References -- 3 Lévy Flights and Global Optimization -- 3.1 Introduction.
3.2 Metaheuristic Algorithms -- 3.3 Lévy Flights in Global Optimization -- 3.3.1 The Lévy Probability Distribution -- 3.3.2 Simulation of Lévy Random Numbers -- 3.3.3 Diversification and Intensification -- 3.3.3.1 Diversification -- 3.3.3.2 Intensification -- 3.4 Metaheuristic Algorithms Based on Lévy Probability Distribution: Is It a Good Idea? -- 3.4.1 Evolutionary Programming Using Mutations Based on the Lévy Probability Distribution -- 3.4.2 Lévy Particle Swarm -- 3.4.3 Cuckoo Search -- 3.4.4 Modified Cuckoo Search -- 3.4.5 Firefly Algorithm -- 3.4.5.1 Attractiveness -- 3.4.5.2 Distance -- 3.4.5.3 Movement -- 3.4.6 Eagle Strategy -- 3.5 Discussion -- 3.6 Conclusions -- References -- 4 Memetic Self-Adaptive Firefly Algorithm -- 4.1 Introduction -- 4.2 Optimization Problems and Their Complexity -- 4.3 Memetic Self-Adaptive Firefly Algorithm -- 4.3.1 Self-Adaptation of Control Parameters -- 4.3.2 Population Model -- 4.3.3 Balancing Between Exploration and Exploitation -- 4.3.4 The Local Search -- 4.3.5 Scheme of the MSA-FFA -- 4.4 Case Study: Graph 3-Coloring -- 4.4.1 Graph 3-Coloring -- 4.4.2 MSA-FFA for Graph 3-Coloring -- 4.4.2.1 Hybrid Genotype-Phenotype Mapping -- 4.4.2.2 The Heuristic Swap Local Search -- 4.4.3 Experiments and Results -- 4.4.3.1 Test Suite -- 4.4.3.2 Influence of the Edge Probability -- 4.4.3.3 Influence of the Fitness Diversity Metric -- 4.4.3.4 Influence of the Inertia Diversity Metric -- 4.4.3.5 Convergence Graphs -- 4.4.3.6 Discussion -- 4.5 Conclusions -- References -- 5 Modeling and Simulation of Ant Colony's Labor Division: A Problem-Oriented Approach -- 5.1 Introduction -- 5.2 Ant Colony's Labor Division Behavior and its Modeling Description -- 5.2.1 Ant Colony's Labor Division -- 5.2.2 Ant Colony's Labor Division Model -- 5.2.2.1 Group Dynamics Model -- 5.2.2.2 Fixed Response Threshold Model.
5.2.2.3 Time-Dependent Response Threshold Model -- 5.2.3 Some Analysis -- 5.3 Modeling and Simulation of Ant Colony's Labor Division with Multitask -- 5.3.1 Background Analysis -- 5.3.2 Design and Implementation of Ant Colony's Labor Division Model with Multitask -- 5.3.2.1 Design of Ant Colony's Labor Division Model with Multitask -- Environmental Stimuli -- Agent Attributes -- Probability of Participation and Exit -- Simulation Principle -- 5.3.2.2 Implementation of Ant Colony's Labor Division Model with Multitask -- 5.3.3 Supply Chain Virtual Enterprise Simulation -- 5.3.3.1 Simulation Example and Parameter Settings -- 5.3.3.2 Simulation Results and Analysis -- 5.3.4 Virtual Organization Enterprise Simulation -- 5.3.4.1 Simulation Example and Parameter Settings -- 5.3.4.2 Simulation Results and Analysis -- 5.3.5 Discussion -- 5.4 Modeling and Simulation of Ant Colony's Labor Division with Multistate -- 5.4.1 Background Analysis -- 5.4.2 Design and Implementation of Ant Colony's Labor Division Model with Multistate -- 5.4.2.1 Design of Ant Colony's Labor Division Model with Multistate -- Stimulus Values in Multitask Environment -- Relative Environment Stimulus Value sαβ and Relative Threshold θαβ -- Agent State Transformation -- 5.4.2.2 Implementation of Ant Colony's Labor Division Model with Multistate -- 5.4.3 Simulation Example of Ant Colony's Labor Division Model with Multistate -- 5.4.3.1 Simulation and Experiment Environment -- 5.4.3.2 Parameters of the Simulation Model -- 5.4.3.3 Simulation Results -- 5.4.3.4 Analysis of Results -- 5.5 Modeling and Simulation of Ant Colony's Labor Division with Multiconstraint -- 5.5.1 Background Analysis -- 5.5.2 Design and Implementation of Ant Colony's Labor Division Model with Multiconstraint -- 5.5.2.1 Design of Ant Colony's Labor Division Model with Multiconstraint.
5.5.2.2 Implementation of Ant Colony's Labor Division Model with Multiconstraint -- 5.5.3 Simulation Results and Analysis -- 5.6 Concluding Remarks -- Acknowledgment -- References -- 6 Particle Swarm Algorithm: Convergence and Applications -- 6.1 Introduction -- 6.2 Convergence Analysis -- 6.2.1 Individual Trajectory -- 6.2.2 Probabilistic Analysis -- 6.3 Performance Illustration -- 6.3.1 Dataflow Application -- 6.3.1.1 Problem Formulation -- Particle Swarm Heuristic for FDSP -- Algorithm Performance Demonstration -- 6.3.2 NS Application -- NS Problem in P2P Networks -- Particle Swarm Heuristic for NS -- 6.4 Application in Hidden Markov Models -- 6.4.1 Parameters Weighted HMM -- 6.4.2 PSO-Viterbi for Parameters Weighted HMMs -- 6.4.3 POS Tagging Problem and Solution -- 6.4.4 Experiment -- 6.5 Conclusions -- References -- 7 A Survey of Swarm Algorithms Applied to Discrete Optimization Problems -- 7.1 Introduction -- 7.2 Swarm Algorithms -- 7.2.1 Particle Swarm Optimization -- 7.2.2 Roach Infestation Optimization -- 7.2.3 Cuckoo Search Algorithm -- 7.2.4 Firefly Algorithm -- 7.2.5 Gravitational Search Algorithm -- 7.2.6 Bat Algorithm -- 7.2.7 Glowworm Swarm Optimization Algorithm -- 7.2.8 Artificial Fish School Algorithm -- 7.2.9 Bacterial Evolutionary Algorithm -- 7.2.10 Bee Algorithm -- 7.2.11 Artificial Bee Colony Algorithm -- 7.2.12 Bee Colony Optimization -- 7.2.13 Marriage in Honey-Bees Optimization Algorithm -- 7.3 Main Concerns to Handle Discrete Problems -- 7.3.1 Discretization Methods -- 7.3.1.1 Sigmoid Function -- 7.3.1.2 Random-Key -- 7.3.1.3 Smallest Position Value -- 7.3.1.4 Modified Position Equation -- 7.3.1.5 Great Value Priority -- 7.3.1.6 Nearest Integer -- 7.4 Applications to Discrete Problems -- 7.4.1 Particle Swarm Optimization -- 7.4.2 Roach Infestation Optimization -- 7.4.3 Cuckoo Search Algorithm -- 7.4.4 Firefly Algorithm.
7.4.5 Bee Algorithm -- 7.4.6 Artificial Bee Colony -- 7.4.7 Bee Colony Optimization -- 7.4.8 Marriage in Honey-Bees Optimization Algorithm -- 7.4.9 Other Swarm Intelligence Algorithms -- 7.5 Discussion -- 7.6 Concluding Remarks and Future Research -- References -- 8 Test Functions for Global Optimization: A Comprehensive Survey -- 8.1 Introduction -- 8.2 A Collection of Test Functions for GO -- 8.2.1 Unimodal Test Functions -- 8.2.2 Multimodal Function -- 8.3 Conclusions -- References -- Two. Applications and Case Studies -- 9 Binary Bat Algorithm for Feature Selection -- 9.1 Introduction -- 9.2 Bat Algorithm -- 9.3 Binary Bat Algorithm -- 9.4 Optimum-Path Forest Classifier -- 9.4.1 Background Theory -- 9.5 Binary Bat Algorithm -- 9.6 Experimental Results -- 9.7 Conclusions -- References -- 10 Intelligent Music Composition -- 10.1 Introduction -- 10.2 Unsupervised Intelligent Composition -- 10.2.1 Unsupervised Composition with Cellular Automata -- 10.2.2 Unsupervised Composition with L-Systems -- 10.3 Supervised Intelligent Composition -- 10.3.1 Supervised Composition with Genetic Algorithms -- 10.3.2 Supervised Composition Genetic Programming -- 10.4 Interactive Intelligent Composition -- 10.4.1 Composing with Swarms -- 10.4.2 Interactive Composition with GA and GP -- 10.5 Conclusions -- References -- 11 A Review of the Development and Applications of the Cuckoo Search Algorithm -- 11.1 Introduction -- 11.2 Cuckoo Search Algorithm -- 11.2.1 The Analogy -- 11.2.2 Cuckoo Breeding Behavior -- 11.2.3 Lévy Flights -- 11.2.4 The Algorithm -- 11.2.5 Validation -- 11.3 Modifications and Developments -- 11.3.1 Algorithmic Modifications -- 11.3.2 Hybridization -- 11.4 Applications -- 11.4.1 Applications in Machine Learning -- 11.4.2 Applications in Design -- 11.5 Conclusion -- References -- 12 Bio-Inspired Models for Semantic Web -- 12.1 Introduction.
12.2 Semantic Web.
Summary: Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers. Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.
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Front Cover -- Swarm Intelligence and Bio-Inspired Computation -- Copyright Page -- Contents -- List of Contributors -- Preface -- One. Theoretical Aspects of Swarm Intelligence and Bio-Inspired Computing -- 1 Swarm Intelligence and Bio-Inspired Computation: An Overview -- 1.1 Introduction -- 1.2 Current Issues in Bio-Inspired Computing -- 1.2.1 Gaps Between Theory and Practice -- 1.2.2 Classifications and Terminology -- 1.2.3 Tuning of Algorithm-Dependent Parameters -- 1.2.4 Necessity for Large-Scale and Real-World Applications -- 1.2.5 Choice of Algorithms -- 1.3 Search for the Magic Formulas for Optimization -- 1.3.1 Essence of an Algorithm -- 1.3.2 What Is an Ideal Algorithm? -- 1.3.3 Algorithms and Self-Organization -- 1.3.4 Links Between Algorithms and Self-Organization -- 1.3.5 The Magic Formulas -- 1.4 Characteristics of Metaheuristics -- 1.4.1 Intensification and Diversification -- 1.4.2 Randomization Techniques -- 1.5 Swarm-Intelligence-Based Algorithms -- 1.5.1 Ant Algorithms -- 1.5.2 Bee Algorithms -- 1.5.3 Bat Algorithm -- 1.5.4 Particle Swarm Optimization -- 1.5.5 Firefly Algorithm -- 1.5.6 Cuckoo Search -- 1.5.7 Flower Pollination Algorithm -- 1.5.8 Other Algorithms -- 1.6 Open Problems and Further Research Topics -- References -- 2 Analysis of Swarm Intelligence-Based Algorithms for Constrained Optimization -- 2.1 Introduction -- 2.2 Optimization Problems -- 2.3 Swarm Intelligence-Based Optimization Algorithms -- 2.3.1 Ant Colony Optimization -- 2.3.2 Particle Swarm Optimizer -- 2.3.3 ABC Algorithm -- 2.3.4 Glowworm Swarm Algorithm -- 2.3.5 Firefly Algorithm -- 2.3.6 Cuckoo Search Algorithm -- 2.3.7 Bat Algorithm -- 2.3.8 Hunting Search Algorithm -- 2.4 Numerical Examples -- 2.4.1 Example 1 -- 2.4.2 Example 2 -- 2.5 Summary and Conclusions -- References -- 3 Lévy Flights and Global Optimization -- 3.1 Introduction.

3.2 Metaheuristic Algorithms -- 3.3 Lévy Flights in Global Optimization -- 3.3.1 The Lévy Probability Distribution -- 3.3.2 Simulation of Lévy Random Numbers -- 3.3.3 Diversification and Intensification -- 3.3.3.1 Diversification -- 3.3.3.2 Intensification -- 3.4 Metaheuristic Algorithms Based on Lévy Probability Distribution: Is It a Good Idea? -- 3.4.1 Evolutionary Programming Using Mutations Based on the Lévy Probability Distribution -- 3.4.2 Lévy Particle Swarm -- 3.4.3 Cuckoo Search -- 3.4.4 Modified Cuckoo Search -- 3.4.5 Firefly Algorithm -- 3.4.5.1 Attractiveness -- 3.4.5.2 Distance -- 3.4.5.3 Movement -- 3.4.6 Eagle Strategy -- 3.5 Discussion -- 3.6 Conclusions -- References -- 4 Memetic Self-Adaptive Firefly Algorithm -- 4.1 Introduction -- 4.2 Optimization Problems and Their Complexity -- 4.3 Memetic Self-Adaptive Firefly Algorithm -- 4.3.1 Self-Adaptation of Control Parameters -- 4.3.2 Population Model -- 4.3.3 Balancing Between Exploration and Exploitation -- 4.3.4 The Local Search -- 4.3.5 Scheme of the MSA-FFA -- 4.4 Case Study: Graph 3-Coloring -- 4.4.1 Graph 3-Coloring -- 4.4.2 MSA-FFA for Graph 3-Coloring -- 4.4.2.1 Hybrid Genotype-Phenotype Mapping -- 4.4.2.2 The Heuristic Swap Local Search -- 4.4.3 Experiments and Results -- 4.4.3.1 Test Suite -- 4.4.3.2 Influence of the Edge Probability -- 4.4.3.3 Influence of the Fitness Diversity Metric -- 4.4.3.4 Influence of the Inertia Diversity Metric -- 4.4.3.5 Convergence Graphs -- 4.4.3.6 Discussion -- 4.5 Conclusions -- References -- 5 Modeling and Simulation of Ant Colony's Labor Division: A Problem-Oriented Approach -- 5.1 Introduction -- 5.2 Ant Colony's Labor Division Behavior and its Modeling Description -- 5.2.1 Ant Colony's Labor Division -- 5.2.2 Ant Colony's Labor Division Model -- 5.2.2.1 Group Dynamics Model -- 5.2.2.2 Fixed Response Threshold Model.

5.2.2.3 Time-Dependent Response Threshold Model -- 5.2.3 Some Analysis -- 5.3 Modeling and Simulation of Ant Colony's Labor Division with Multitask -- 5.3.1 Background Analysis -- 5.3.2 Design and Implementation of Ant Colony's Labor Division Model with Multitask -- 5.3.2.1 Design of Ant Colony's Labor Division Model with Multitask -- Environmental Stimuli -- Agent Attributes -- Probability of Participation and Exit -- Simulation Principle -- 5.3.2.2 Implementation of Ant Colony's Labor Division Model with Multitask -- 5.3.3 Supply Chain Virtual Enterprise Simulation -- 5.3.3.1 Simulation Example and Parameter Settings -- 5.3.3.2 Simulation Results and Analysis -- 5.3.4 Virtual Organization Enterprise Simulation -- 5.3.4.1 Simulation Example and Parameter Settings -- 5.3.4.2 Simulation Results and Analysis -- 5.3.5 Discussion -- 5.4 Modeling and Simulation of Ant Colony's Labor Division with Multistate -- 5.4.1 Background Analysis -- 5.4.2 Design and Implementation of Ant Colony's Labor Division Model with Multistate -- 5.4.2.1 Design of Ant Colony's Labor Division Model with Multistate -- Stimulus Values in Multitask Environment -- Relative Environment Stimulus Value sαβ and Relative Threshold θαβ -- Agent State Transformation -- 5.4.2.2 Implementation of Ant Colony's Labor Division Model with Multistate -- 5.4.3 Simulation Example of Ant Colony's Labor Division Model with Multistate -- 5.4.3.1 Simulation and Experiment Environment -- 5.4.3.2 Parameters of the Simulation Model -- 5.4.3.3 Simulation Results -- 5.4.3.4 Analysis of Results -- 5.5 Modeling and Simulation of Ant Colony's Labor Division with Multiconstraint -- 5.5.1 Background Analysis -- 5.5.2 Design and Implementation of Ant Colony's Labor Division Model with Multiconstraint -- 5.5.2.1 Design of Ant Colony's Labor Division Model with Multiconstraint.

5.5.2.2 Implementation of Ant Colony's Labor Division Model with Multiconstraint -- 5.5.3 Simulation Results and Analysis -- 5.6 Concluding Remarks -- Acknowledgment -- References -- 6 Particle Swarm Algorithm: Convergence and Applications -- 6.1 Introduction -- 6.2 Convergence Analysis -- 6.2.1 Individual Trajectory -- 6.2.2 Probabilistic Analysis -- 6.3 Performance Illustration -- 6.3.1 Dataflow Application -- 6.3.1.1 Problem Formulation -- Particle Swarm Heuristic for FDSP -- Algorithm Performance Demonstration -- 6.3.2 NS Application -- NS Problem in P2P Networks -- Particle Swarm Heuristic for NS -- 6.4 Application in Hidden Markov Models -- 6.4.1 Parameters Weighted HMM -- 6.4.2 PSO-Viterbi for Parameters Weighted HMMs -- 6.4.3 POS Tagging Problem and Solution -- 6.4.4 Experiment -- 6.5 Conclusions -- References -- 7 A Survey of Swarm Algorithms Applied to Discrete Optimization Problems -- 7.1 Introduction -- 7.2 Swarm Algorithms -- 7.2.1 Particle Swarm Optimization -- 7.2.2 Roach Infestation Optimization -- 7.2.3 Cuckoo Search Algorithm -- 7.2.4 Firefly Algorithm -- 7.2.5 Gravitational Search Algorithm -- 7.2.6 Bat Algorithm -- 7.2.7 Glowworm Swarm Optimization Algorithm -- 7.2.8 Artificial Fish School Algorithm -- 7.2.9 Bacterial Evolutionary Algorithm -- 7.2.10 Bee Algorithm -- 7.2.11 Artificial Bee Colony Algorithm -- 7.2.12 Bee Colony Optimization -- 7.2.13 Marriage in Honey-Bees Optimization Algorithm -- 7.3 Main Concerns to Handle Discrete Problems -- 7.3.1 Discretization Methods -- 7.3.1.1 Sigmoid Function -- 7.3.1.2 Random-Key -- 7.3.1.3 Smallest Position Value -- 7.3.1.4 Modified Position Equation -- 7.3.1.5 Great Value Priority -- 7.3.1.6 Nearest Integer -- 7.4 Applications to Discrete Problems -- 7.4.1 Particle Swarm Optimization -- 7.4.2 Roach Infestation Optimization -- 7.4.3 Cuckoo Search Algorithm -- 7.4.4 Firefly Algorithm.

7.4.5 Bee Algorithm -- 7.4.6 Artificial Bee Colony -- 7.4.7 Bee Colony Optimization -- 7.4.8 Marriage in Honey-Bees Optimization Algorithm -- 7.4.9 Other Swarm Intelligence Algorithms -- 7.5 Discussion -- 7.6 Concluding Remarks and Future Research -- References -- 8 Test Functions for Global Optimization: A Comprehensive Survey -- 8.1 Introduction -- 8.2 A Collection of Test Functions for GO -- 8.2.1 Unimodal Test Functions -- 8.2.2 Multimodal Function -- 8.3 Conclusions -- References -- Two. Applications and Case Studies -- 9 Binary Bat Algorithm for Feature Selection -- 9.1 Introduction -- 9.2 Bat Algorithm -- 9.3 Binary Bat Algorithm -- 9.4 Optimum-Path Forest Classifier -- 9.4.1 Background Theory -- 9.5 Binary Bat Algorithm -- 9.6 Experimental Results -- 9.7 Conclusions -- References -- 10 Intelligent Music Composition -- 10.1 Introduction -- 10.2 Unsupervised Intelligent Composition -- 10.2.1 Unsupervised Composition with Cellular Automata -- 10.2.2 Unsupervised Composition with L-Systems -- 10.3 Supervised Intelligent Composition -- 10.3.1 Supervised Composition with Genetic Algorithms -- 10.3.2 Supervised Composition Genetic Programming -- 10.4 Interactive Intelligent Composition -- 10.4.1 Composing with Swarms -- 10.4.2 Interactive Composition with GA and GP -- 10.5 Conclusions -- References -- 11 A Review of the Development and Applications of the Cuckoo Search Algorithm -- 11.1 Introduction -- 11.2 Cuckoo Search Algorithm -- 11.2.1 The Analogy -- 11.2.2 Cuckoo Breeding Behavior -- 11.2.3 Lévy Flights -- 11.2.4 The Algorithm -- 11.2.5 Validation -- 11.3 Modifications and Developments -- 11.3.1 Algorithmic Modifications -- 11.3.2 Hybridization -- 11.4 Applications -- 11.4.1 Applications in Machine Learning -- 11.4.2 Applications in Design -- 11.5 Conclusion -- References -- 12 Bio-Inspired Models for Semantic Web -- 12.1 Introduction.

12.2 Semantic Web.

Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers. Focuses on the introduction and analysis of key algorithms Includes case studies for real-world applications Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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