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.