Note: Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Neural Networks and Statistical Learning - Neural Architecture Search -- A New Recurrent Neural Network with Fewer Neurons for Quadratic Programming Problems -- Abstract -- 1 Introduction -- 2 Mathematical Model -- 3 Convergence Analysis -- 4 Extensions -- 4.1 Discrete-Time Model -- 4.2 Irredundant Equality Constraint -- 5 Simulations -- 5.1 Continuous-Time Neural Network Model -- 5.2 Discrete-Time Model -- 5.3 Problem with Irredundant Equality Constraint -- 6 Conclusions -- Acknowledgements -- References -- Mutual-Information-SMOTE: A Cost-Free Learning Method for Imbalanced Data Classification -- Abstract -- 1 Introduction -- 2 Mutual Information Classifier -- 2.1 Normalized Mutual Information -- 2.2 Mutual-Information Classifier in Binary Classifications -- 3 SMOTE -- 4 Mutual-Information Classifier Based on SMOTE -- 5 Experimental Results and Conclusions -- 5.1 Evaluation Criteria -- 5.2 Experiment on Data Sets -- 5.3 Experiment on Medical Images -- 6 Conclusion -- Acknowledgements -- References -- Ontology Sparse Vector Learning Algorithm -- Abstract -- 1 Introduction -- 2 The Framework of Ontology Algorithm Based on Sparse Vector -- 3 New Algorithm Description -- 4 Experiment -- 4.1 Ontology Similarity Computation Experiment -- 4.2 Ontology Mapping Experiment -- 5 Conclusion -- Acknowledgements -- References -- Bacterial Foraging Algorithm Based on Reinforcement Learning for Continuous Optimizations -- Abstract -- 1 Introduction -- 2 Reinforcement Learning Bacterial Foraging Model -- 2.1 The Principle of the Proposed Model -- 2.2 Main Procedures of the Algorithm -- 3 Experiment Results -- 3.1 Test Function and Experimental Configure -- 3.2 Comparison with Other Algorithm in Numerical Value -- 3.3 Comparison with Other Algorithm in Convergence. , 3.4 Bacterial Movement Trajectory -- 4 Summary -- Acknowledgements -- References -- A Novel Attribute Reduction Approach Based on Improved Attribute Significance -- Abstract -- 1 Introduction -- 2 Analysis on the Original Attribute Significance -- 3 An Improved Attribute Significance -- 4 Reduction Algorithm Based on Improved Attribute Significance and Discernibility Matrix -- 5 Numerical Example -- 6 Conclusions -- Acknowledgements -- References -- Neural Networks and Statistical Learning - Transfer of knowledge -- Traffic Condition Assessment Based on Support Vectors Machine Using Intelligent Transportation System Data -- Abstract -- 1 Introduction -- 2 Support Vector Machines -- 2.1 Supervised and Unsupervised Learning -- 2.2 Nonlinear Support Vector Machines -- 2.3 Kernel Functions -- 3 Data Selection Design -- 3.1 Historical Database Selection -- 3.2 Road Network Database Selection -- 3.3 Time Serial Database Selection -- 4 SVM Modeling and Prediction -- 4.1 SVM Modeling Procedure -- 4.2 Cross Validation -- 4.3 Index of Evaluating Traffic Condition-Level of Service (LOS) -- 4.4 Traffic Condition Forecasting and Congestion Prediction -- 4.5 Prediction Result -- 5 Conclusion -- References -- Bidirectional Negative Correlation Learning -- 1 Introduction -- 2 Negative Correlation Learning with Two Learning Targets -- 3 Learning Performance -- 3.1 Results of NNEs with Two Architectures -- 3.2 Results of Small and Large NNs -- 3.3 Results of Similarity Ratios -- 4 Conclusions -- References -- Reflectance Estimation Based on Locally Weighted Linear Regression Methods -- Abstract -- 1 Introduction -- 2 Background and Methods -- 2.1 Global Regression Methods -- 2.2 Regularized Local Linear Model -- 2.3 Locally Weighted Linear Regression -- 3 Experimental Analysis -- 3.1 Datasets and Procedure -- 3.2 Experiments Between Different Methods -- 4 Conclusion. , Acknowledgements -- References -- A Multi-task Learning Approach for Mandarin-English Code-Switching Conversational Speech Recognition -- Abstract -- 1 Introduction -- 2 Proposed CSR-LID-MTL Approach -- 2.1 Pre-Selection Phase for the Primary Tasks -- 2.2 Multi-task Learning Phase for the CSR-LID-MTL -- 2.3 The Proposed CSR-LID-MTL Approach -- 3 Experimental Settings and Results -- 3.1 Dataset -- 3.2 Setting of the Baseline ASR System -- 3.3 Settings of the CSR-LID-MTL Approach -- 3.4 Experimental Results -- 4 Conclusions -- Acknowledgements -- References -- Feature Selection of Network Flow Based on Machine Learning -- Abstract -- 1 Introduction -- 2 Flow Identification and Machine Learning -- 2.1 Flow Identification Technique -- 2.1.1 Detection Based on Port Number -- 2.1.2 DPI (Deep Packet Inspection) -- 2.1.3 Flow Identification Based on Machine Learning -- 2.2 Concept of Feature Selection -- 2.3 Algorithm Evaluation Criteria -- 2.3.1 Confusion Matrix -- 2.3.2 Evaluation Method -- 3 Feature Selection Algorithm and Its Improvement -- 3.1 CFS Algorithm -- 3.2 Information Gain Algorithm -- 3.3 Improved Information Gain Algorithm Based on Symmetric Uncertainty -- 4 Research on Classification Performance of Improved Feature Selection Algorithm -- 4.1 Data Mining Tools WEKA -- 4.2 Moore Dataset -- 4.3 Experiment and Result Analysis -- 5 Conclusion -- References -- Evolutionary Multi-objective and Dynamic Optimization - Optimal Control and Design -- Multi-objective Optimal Scheduling of Valves and Hydrants for Sudden Drinking Water Pollution Incident -- 1 Introduction -- 2 System Model and Formulation -- 2.1 System Model -- 2.2 Formulation -- 3 Multi-objective Optimization Model for Drinking Water Pollution Incident -- 3.1 Objective Function f1 - Minimizing the Amount of Contaminant Exposure to Public. , 3.2 Objective Function f2 - Minimizing the Cost of Scheduling of Valves and Hydrants -- 4 A Customized NSGA-II Approach for Bi-criterion Scheduling Problem -- 4.1 Encoding and Initialization of Populations -- 4.2 Selection, Crossover and Mutation Operators -- 4.3 Evaluation of Fitness Function -- 5 Experiment Simulation and Analysis -- 5.1 Parameter Setting -- 5.2 Pareto Front with Different Generation -- 5.3 Impact of Hydrants Flow Rate -- 5.4 Impact of Monitoring Station Location -- 5.5 Impact of Contaminant Source -- 6 Conclusion -- References -- A Novel Mutation and Crossover Operator for Multi-objective Differential Evolution -- Abstract -- 1 Introduction -- 2 Relate Work -- 3 NMCO-MODE Algorithm -- 4 Performance Measures and Test Results -- 5 Conclusion -- Acknowledgement -- References -- Multi-objective Gene Expression Programming Based Automatic Clustering Method -- Abstract -- 1 Introduction -- 2 An Overview of GEP -- 2.1 Initialization -- 2.2 Decoding -- 2.3 Fitness Evaluation -- 2.4 Roulette Wheel Selection (RWS) -- 2.5 Reproduction with Modification -- 3 NSGA-II -- 4 Multi-objective GEP Based Automatic Clustering -- 4.1 Initialization -- 4.2 Objective Functions -- 4.3 Decoding the Chromosome -- 4.4 Selection and Variation -- 4.5 The Choice of Solution -- 5 Experiment and Analysis -- 5.1 Experimental Data -- 5.2 Parameter Setting -- 5.3 Experimental Comparison -- 6 Conclusion -- Acknowledgments -- References -- Multi-objective Firefly Algorithm Guided by Elite Particle -- Abstract -- 1 Introduction -- 2 The Multi-objective Optimization Problem and Standard Firefly Algorithm -- 2.1 The Multi-objective Optimization Problem -- 2.2 Standard Firefly Algorithm -- 3 The Multi-objective Firefly Algorithm and Its Improvement -- 3.1 The Multi-objective Firefly Algorithm -- 3.2 The Multi-objective Firefly Algorithm Guided by Elite Particle. , 4 Experiments and Results -- 4.1 Experimental Setup -- 4.1.1 Test Functions -- 4.1.2 Algorithm Comparison and Experimental Parameters -- 4.2 Experimental Results and Analysis -- 5 Conclusions -- Acknowledgment -- References -- Improving Energy Demand Estimation Using an Adaptive Firefly Algorithm -- Abstract -- 1 Introduction -- 2 Firefly Algorithm -- 3 Proposed Approach -- 3.1 Adaptive Firefly Algorithm (AFA) -- 3.2 Estimation Models -- 3.3 Fitness Evaluation Function -- 3.4 Data Normalization -- 4 Simulation Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusions -- Acknowledgement -- References -- Evolutionary Multi-objective and Dynamic Optimization - Hybrid Methods -- Firefly Algorithm with Elite Attraction -- Abstract -- 1 Introduction -- 2 A Brief Review of Firefly Algorithm -- 3 Our Proposed Firefly Algorithm -- 4 Experimental Study -- 4.1 Test Problems -- 4.2 Experimental Results -- 4.3 Comparison of EkFA with Other FA Variants -- 5 Conclusions -- Acknowledgments -- References -- A Hybrid Fireworks Explosion Algorithm -- Abstract -- 1 Introduction -- 2 AFAOL Algorithm -- 2.1 Opposition-Based Learning -- 2.2 Fireworks Algorithm Optimization -- 2.3 Adaptive Explosion Radius -- 2.4 Generating Explosion Sparks -- 3 Experiments Study -- 4 Conclusions -- Acknowledgement -- References -- An Improved Multi-objective Fireworks Algorithm -- Abstract -- 1 Introduction -- 2 Backgrounds -- 2.1 Basic Concepts -- 2.2 Basic Fireworks Algorithm -- 3 Improved Multi-objective Fireworks Algorithm -- 3.1 Initialization Approach -- 3.2 Fine-Grained Controlling Explosion Radius -- 3.3 Selection of Sparks -- 3.4 Maintain the Diversity of External Archive -- 3.5 Flow of iMOFA -- 4 Experimental Results -- 4.1 Test Problems -- 4.2 Performance Measure -- 4.3 Experimental Settings -- 5 Conclusions -- Acknowledgement -- References. , Evolutionary Design of a Crooked-Wire Antenna.

Note: Intro -- Preface -- Organization -- Contents -- Evolutionary Algorithms -- A Hybrid Group Search Optimizer with Opposition-Based Learning and Differential Evolution -- Abstract -- 1 Introduction -- 2 Background -- 2.1 Group Search Optimizer -- 2.2 Opposition-Based Learning -- 2.3 Differential Evolution -- 3 Hybrid GSO -- 4 Experimental Results and Analysis -- 5 Conclusion -- References -- A New Firefly Algorithm with Local Search for Numerical Optimization -- Abstract -- 1 Introduction -- 2 Firefly Algorithm -- 3 Proposed Approach -- 4 Experimental Results -- 4.1 Test Functions -- 4.2 Involved Algorithms and Parameter Settings -- 4.3 Results -- 5 Conclusion -- Acknowledgement -- References -- A New Trend Peak Algorithm with X-ray Image for Wheel Hubs Detection and Recognition -- 1 Introduction -- 2 The Main Procedure of the Automatic Detection of Automotive Wheel Hubs Defects -- 3 The Trend Peak Algorithm -- 3.1 The Theory of Peak Location Algorithm -- 3.2 Trend Peak Algorithm -- 4 Experimental Results -- 5 Conclusion -- References -- Community Detection Based on an Improved Genetic Algorithm -- Abstract -- 1 Introduction -- 2 Description of Algorithm -- 2.1 Encoded Mode -- 2.2 The Selection Operator Based on Immune -- 2.3 Algorithm Description -- 3 Simulation Experiments and Results Analysis -- 3.1 Artificial Network -- 4 Conclusion -- References -- Selecting Training Samples from Large-Scale Remote-Sensing Samples Using an Active Learning Algorithm -- Abstract -- 1 Introduction -- 2 Margin Sampling Algorithm -- 3 Selecting Large-Scale Training Samples Based on an Active Learning Algorithm -- 4 Data Sets and Experimental Design -- 4.1 Data Sets -- 4.2 Experiments Design -- 4.3 Experiments Results and Analysis -- 5 Conclusion -- References -- Coverage Optimization for Wireless Sensor Networks by Evolutionary Algorithm -- Abstract. , 1 Introduction -- 2 The Evaluation Standard of WSN Coverage -- 2.1 Barrier Coverage -- 2.2 Area Coverage -- 2.3 Point Coverage -- 3 Energy-Saving of Wireless Sensor Network -- 3.1 Deterministic Deployment -- 3.2 Random Deployment -- 4 The Evolution of the WSN Coverage Optimization Algorithm Design -- 4.1 Evolutionary Algorithm -- 4.2 Coding -- 4.3 Generation of Initial Population -- 4.4 The Improved Fitness Function -- 4.5 The Mutation Probability -- 5 Simulation Experiment -- 5.1 Optimization Experiment of Fitness Function -- 5.2 Contrast Experiment of the Lattice Parameters Inside the Circle -- 5.3 Contrast Experiment of Crossover Operator -- 5.4 The Mutation Probability -- 6 Conclusion -- References -- Combining Dynamic Constrained Many-Objective Optimization with DE to Solve Constrained Optimization Problems -- 1 Introduction -- 2 Convert COP to DCMOP -- 2.1 Convert COP to CMOP -- 2.2 Convert CMOP to DCMOP -- 3 Algorithm Description -- 4 Experiments and Results -- 4.1 Determination of Reference Points and Algorithm Parameters -- 4.2 Results and Comparison -- 5 Conclusion -- References -- Executing Time and Cost-Aware Task Scheduling in Hybrid Cloud Using a Modified DE Algorithm -- 1 Introduction -- 2 Related Work -- 3 Improved DE Algorithm for Hybrid Cloud Scheduling -- 3.1 Scheduling Modeling -- 3.2 Modifying jDE Algorithm - GaDE -- 3.3 Multi-objective jDE Algorithm Based on Non-dominated Sorting - NSjDE -- 4 Experimental Performance Results -- 4.1 Design of the Experiments -- 4.2 Experimental Results -- 5 Conclusions -- References -- A Novel Differential Evolution Algorithm Based on JADE for Constrained Optimization -- Abstract -- 1 Introduction -- 2 Previous Work -- 2.1 Constrained Optimization Problems -- 2.2 Constraint Handling -- 2.3 Adaptive Differential Evolution -- 3 Improved dE Algorithm -- 3.1 Initialization. , 3.2 An Improved Adaptive Tradeoff Model -- 3.3 Algorithmic Framework -- 4 Experimental Study -- 5 Conclusion and Future Work -- Acknowledgments -- References -- A New Ant Colony Classification Mining Algorithm -- Abstract -- 1 Introduction -- 2 The Traditional Ant Colony Classification Algorithms -- 3 A New Ant Colony Classification Mining Algorithm -- 3.1 General Description of Ant-MinerPAE -- 3.2 Rule Construction -- 3.2.1 The Method of Rule Construction -- 3.2.2 New Method for Calculating the Pheromone -- 3.2.3 Heuristic Function -- 3.3 Rule Pruning -- 3.4 The Improved Method of Pheromone Updating -- 3.4.1 The Pheromone Updating on the Attribute Node in the Rules -- 3.4.2 The Pheromone Updating on the Attribute Node Outside the Rules -- 4 Experiment and Result Analysis -- 4.1 Data Sets and Preprocessing -- 4.2 Ant-MinerPAE's Parameter Setting -- 4.3 Comparing Ant-MinerPAE with Other Several Algorithms -- 5 Conclusion -- Acknowledgments -- References -- A Dynamic Search Space Strategy for Swarm Intelligence -- 1 Introduction -- 2 Basic Ideal -- 2.1 Conditions -- 3 Specification -- 3.1 Decreasing and Expanding Space -- 3.2 Re-initializing -- 4 Algorithm Implementation -- 4.1 Dynamic Search Space GA -- 5 Conclusion -- References -- Adaptive Mutation Opposition-Based Particle Swarm Optimization -- Abstract -- 1 Introduction -- 2 Related Work -- 2.1 Particle Swarm Optimization -- 2.2 A Generalized Opposition-Based Learning (GOBL) -- 3 Adaptive Mutation Opposition-Based PSO -- 3.1 Global Adaptive Mutation Selection Operator -- 3.2 Adaptive Inertia Weight Operation -- 3.3 Adaptive Mutation Opposition-Based PSO -- 4 Experiments -- 4.1 Benchmark Problems -- 4.2 Parameter Settings -- 4.3 Comparison Between OBL-Based PSO -- 4.4 Parameter Sensitivity Study -- 5 Conclusion and Future Work -- Acknowledgment -- References. , Quick Convergence Algorithm of ACO Based on Convergence Grads Expectation -- Abstract -- 1 Introduction -- 2 Explanation of Symbols -- 3 Description of ACO-Based QoS Routing Problem -- 3.1 QoS Routing Constraint Mode -- 3.2 The ACO-Based QoS Routing Optimization Algorithm -- 3.2.1 Path Cost -- 3.2.2 Pheromone Update -- 4 CG Mode -- 4.1 Expectation Function -- 4.2 The Probability of Selecting Path -- 5 CG Convergence -- 6 Value Experiment and Result Analysis -- 7 Conclusion -- Acknowledgment -- References -- A New GEP Algorithm and Its Applications in Vegetable Price Forecasting Modeling Problems -- Abstract -- 1 Introduction -- 2 Gene Expression Programming (GEP) -- 2.1 Overview of Gene Expression Programming -- 2.2 Gene Expression Programming -- 2.2.1 Chromosome Structure -- 2.2.2 Fitness Function of GEP -- 2.2.3 Selection Function -- 2.2.4 Genetic Operators of GEP -- 3 Improved Gene Expression Programming -- 3.1 The Methods of Improved GEP -- 3.1.1 Inverse String Operator -- 3.1.2 Gene Extraction Operator -- 3.2 Improvement Effect -- 4 Examples and Assessment -- 4.1 Forecasting Method -- 4.2 Time Aeries Analysis -- 4.3 GEP Evolution -- 4.4 Comparative Analysis of Forecast Results -- 5 Conclusion -- References -- An Optimized Clustering Algorithm Using Improved Gene Expression Programming -- Abstract -- 1 Introduction -- 2 Related Work -- 2.1 Extended Traditional Gene Expression Programming Used in GEP-ADF -- 2.2 Standard Fuzzy C-Means Algorithm (FCM) -- 3 The Optimized Clustering Algorithm -- 3.1 The Novel Chromosome Representation -- 3.2 Pseudocode -- 4 Experiments and Results -- 5 Conclusion and Future Direction -- Acknowledgements -- References -- Predicting Acute Hypotensive Episodes Based on Multi GP -- Abstract -- 1 Introduction -- 2 Data Set -- 3 Methodology -- 3.1 Empirical Mode Decomposition -- 3.2 Multi GP. , 4 Experiment Verification and Discussion -- 5 Conclusion -- References -- Research on Evolution Mechanism in Different-Structure Module Redundancy Fault-Tolerant System -- Abstract -- 1 Introduction -- 2 Fault Tolerant System Architecture Based on Evolution Mechanism -- 3 Evolution Platform Design -- 4 Evolution Algorithm -- 4.1 Coding -- 4.2 Two-Stage Mutation Evolution Strategy (TMES) -- 4.3 Interactive Two-Stage Mutation Evolution Strategy (ITMES) -- 5 Analysis of the Experimental Results -- 5.1 Two-Bit Multipliers -- 5.2 Three-Bit Multipliers -- 5.3 Three-Bit Full Adders -- 6 Discussion -- References -- Intelligent Simulation Algorithms -- Application of Neural Network for Human Actions Recognition -- Abstract -- 1 Introduction -- 2 Material and Methods -- 2.1 Dataset and Features Selection -- 2.2 Classification with NN -- 3 Results -- 4 Discussion -- 5 Conclusions -- References -- The Improved Evaluation of Virtual Resources' Performance Algorithm Based on Computer Clusters -- Abstract -- 1 Introduction -- 2 Related Work -- 2.1 Definition of Cloud Computing -- 2.2 Architecture of Cloud Computing -- 2.3 Virtual Resource Nodes Index System -- 3 Maths Fuzzy Theory---Fuzzy Assessment Method -- 3.1 Principle of Fuzzy Comprehensive Assessment Method -- 3.2 Multi-level Comprehensive Evaluation Model -- 4 Examples and Assessment -- 5 Conclusion -- Acknowledgment -- References -- Bayesian Optimization Algorithm Based on Incremental Model Building -- Abstract -- 1 Introduction -- 2 Bayesian Optimization Algorithm -- 3 Incremental Construction of Bayesian Network Structure Using PBIL -- 4 Bayesian Optimization Algorithm Based on Incremental Model Building -- 5 Simulation and Results -- 5.1 Test Problem and Parameter Setting -- 5.2 Simulation Results -- 6 Summary -- Acknowledgement -- References -- An Improved DBOA Based on Estimation of Model Similarity. , Abstract.

Note: Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Swarm Intelligence - Cooperative Search -- Differential Opposition-Based Particle Swarm -- Abstract -- 1 Introduction -- 2 Related Work -- 2.1 The Basic PSO -- 2.2 A Generalized Opposition-Based Learning (GOBL) -- 3 DOPSO Algorithm -- 3.1 A New Update Equation of Velocity -- 3.2 Adaptive Elite Mutation Selection Strategy -- 3.3 DOPSO Algorithm -- 4 Experiments -- 4.1 Benchmark Problems -- 4.2 Parameter Settings -- 4.3 Performance Comparison Between OBL-Based PSO -- 4.4 Parameter Sensitivity Study -- 5 Conclusion and Future Work -- Acknowledgment -- References -- Research on Hierarchical Cooperative Algorithm Based on Genetic Algorithm and Particle Swarm Optimiz ... -- Abstract -- 1 Introduction -- 2 Introduction of Related Algorithm -- 2.1 Genetic Algorithm -- 2.2 Particle Swarm Algorithm -- 3 Hierarchical Cooperative Algorithm of Genetic Algorithm and Particle Swarm Optimization -- 3.1 Flow of Genetic Algorithm of the Underlying Subgroup -- 3.2 Velocity Initialization of Elite Particle Swarm -- 3.3 Convergence Analysis of HCGA-PSO Algorithm -- 4 Experimental Results and Analysis -- 4.1 Tests of Typical Function -- 4.2 Knapsack Problem Experiment -- 5 Conclusion -- References -- An Adaptive Particle Swarm Optimization Using Hybrid Strategy -- Abstract -- 1 Introduction -- 2 Related Works -- 2.1 Overview of PSO -- 2.2 PSO Based on Opposition-Based Learning -- 2.3 Extremal Optimization -- 3 The Adaptive Particle Swarm Optimization Using Hybrid Strategy -- 3.1 The Proposed Algorithm Design -- 3.2 Mutation -- 3.3 Integration with EO and UOBL -- 4 Numeric Experimental Results and Discussion -- 4.1 Parameters Setting and Benchmark Functions -- 4.2 Experimental Results and Analysis -- 5 Conclusions -- Acknowledgments -- References. , ITÖ Algorithm with Cooperative Coevolution for Large Scale Global Optimization -- 1 Introduction -- 2 ITÖ Algorithm -- 3 Proposed Approach -- 3.1 Variable Interactions Identification (VII) -- 3.2 ITÖ Algorithm -- 3.3 Reallocate Computational Resources (RCR) -- 3.4 Complexity Analysis -- 4 Experimental Studies -- 4.1 Experiment Settings -- 4.2 Compare with Other CC Algorithms -- 5 Conclusion and Future Work -- References -- A Conical Area Differential Evolution with Dual Populations for Constrained Optimization -- 1 Introduction -- 2 Dual-Population Scheme -- 2.1 Conical Sub-population and Biased Cone Decomposition -- 2.2 Feasible Sub-population and Tolerance-Based Sorting -- 3 Proposed Algorithm: CADE -- 3.1 Adaptive Hybrid DE Operator -- 3.2 Update of Sub-populations -- 3.3 Procedure of CADE -- 4 Empirical Results and Discussion -- 4.1 General Performance of CADE -- 4.2 Comparison with Some Other Popular DE-Based Methods -- 5 Conclusion -- References -- Swarm Intelligence - Swarm Optimization -- A Particle Swarm Clustering Algorithm Based on Tree Structure and Neighborhood -- Abstract -- 1 Introduction -- 2 Traditional Particle Swarm Clustering Mining Algorithm -- 2.1 Cluster Analyses and Algorithm -- 2.1.1 Euclidean Distance -- 2.1.2 Cluster Analysis -- 2.2 Particle Swarm Optimization -- 2.3 PSO Clustering Algorithm -- 2.3.1 Particle Swarm Algorithm Combined with K-means Algorithm -- 2.3.2 Particle Swarm Algorithm Combined with FCM Algorithm -- 3 Particle Swarm Mining Algorithm Based on Tree Structure and Neighborhood -- 3.1 Particle Swarm Optimization Based on the Tree Structure and Neighborhood -- 3.2 Improved Particle Swarm Algorithm Cluster Analysis -- 3.2.1 TPSO Combines with K-means Algorithm -- 3.2.2 TPSO Is Combined with FCM Algorithm -- 4 Experiment and Result Analysis -- 4.1 Data Set -- 4.2 Traditional Clustering Algorithm Experiment. , 4.3 Clustering Experiment Based on Improved Particle Swarm Optimization Algorithm -- 4.3.1 The Clustering Experiment of TPSO_K-means Algorithm -- 4.3.2 The Clustering Experiment of TPSO and FCM Algorithm -- 5 Conclusion and Future Work -- 5.1 Conclusion -- 5.2 Future Work -- Acknowledgements -- References -- Optimization of UWB Antenna Based on Particle Swarm Optimization Algorithm -- 1 Introduction -- 2 Ultra-Wideband and Particle Swarm Algorithm -- 2.1 Ultra-Wideband Technology Features -- 2.2 Particle Swarm Optimization Algorithm -- 3 Model Design and Joint Simulation Platform -- 3.1 Antenna Theory and Model Design -- 3.2 Build HFSS and MATLAB Platform -- 4 Experimental Results and Analysis -- 5 Conclusions and Future Work -- References -- A Divisive Multi-level Differential Evolution -- Abstract -- 1 Introduction -- 2 Related Study -- 2.1 Differential Evolution -- 2.2 CDE -- 3 DMDE Algorithm -- 3.1 Division -- 3.2 Multi-level -- 3.3 Parameter Tuning -- 4 Experimental Results -- 4.1 Benchmark Functions and Experimental Setup -- 4.2 Comparison Between CDE and DMDE -- 4.3 Influence of Cluster Centers -- 5 Conclusion and Future Work -- Acknowledgments -- References -- Complex Systems Modeling - System Dynamic -- A Comparative Summary of the Latest Version of MapReduce Parallel and Old Version from the Perspecti ... -- Abstract -- 1 Introduction -- 2 Researches on MapReduceV1 -- 2.1 Submit Jobs -- 2.2 Initialize Jobs -- 2.3 Assign Tasks -- 2.4 Execute Tasks -- 3 Researches on MapReduceV2 -- 4 Comparison of the Latest Version and the Old Version -- 4.1 Differences of the Two Version -- 4.2 Advantages of the New Framework -- 5 Experiment -- 5.1 Experimental Environment and Setup -- 5.2 Execution Performance Testing -- 5.3 Data Scalability Testing -- 6 Conclusion -- References -- A Third-Order Meminductor Chaos Circuit with Complicated Dynamics. , Abstract -- 1 Introduction -- 2 The Mathematic Model of Meminductor -- 3 Meminductor Based Chaotic Circuit -- 4 Dynamical Properties of the Chaotic System -- 4.1 Dissipativity and Equilibrium Point -- 4.2 Lyapunov Spectra and Bifurcation Diagrams -- 5 Conclusion -- Acknowledgements -- References -- Mathematical Model of Cellular Automata in Urban Taxi Network - Take GanZhou as an Example -- Abstract -- 1 Introduction -- 2 Data Preprocessing -- 3 Coarse-Grained Cellular Automata Model -- 3.1 Demands -- 3.2 Supply -- 3.3 Model -- 3.4 Parameter Turning -- 3.5 Incorporating Real Time Traffic Data -- 4 Results -- 5 Conclusions -- References -- Hybrid Colliding Bodies Optimization for Solving Emergency Materials Transshipment Model with Time Window -- Abstract -- 1 Introduction -- 2 Emergency Materials Transshipment Model with Time Window Constraints -- 2.1 Model Description -- 2.2 Parameters and Variable Settings -- 2.3 Mathematical Model -- 3 A Hybrid of Colliding Bodies Optimization and Genetic Algorithm -- 3.1 Colliding Bodies Optimization -- 3.2 Genetic Algorithm -- 3.3 CB Updating Mechanism and Corresponding Genetic Operations -- 3.4 Hybrid CBO and GA Algorithm for Solving Emergency Materials Transshipment with Time Window -- 4 Simulation Experiments and Result Analysis -- 5 Conclusions -- Acknowledgements -- References -- A Dual Internal Point Filter Algorithm Based on Orthogonal Design -- 1 Introduction -- 2 Related Work -- 2.1 Orthogonal Experimental Design -- 2.2 Existing Dual Internal Point Filter Algorithm -- 3 Generalizing the Dual Internal Point Filter Algorithm -- 4 DIPFA-OD -- 4.1 The Algorithm -- 4.2 Numerical Experiments -- 5 Conclusion -- References -- Complex Systems Modeling - Multimedia Simulation -- A Beam Search Approach Based on Action Space for the 2D Rectangular Packing Problem -- Abstract -- 1 Introduction. , 2 The Schemes of Beam Search -- 2.1 Basic Conceptions -- 2.2 Rule Vector -- 2.3 The Base Beam Search Algorithms -- 3 The Improved Beam Search Algorithm -- 4 Computational Results -- 5 Conclusions -- Acknowledgments -- References -- On the Innovation of Multimedia Technology to the Management Model of College Students -- Abstract -- 1 Introduction -- 2 Research Background -- 3 The Tradition Patterns and Research Status of College Student Management -- 3.1 The Traditional Model of Student Management -- 3.2 China's Information Technology Teaching Management Status Quo -- 3.2.1 Student Management Information Construction Overview -- 3.2.2 China's Student Management Information Model -- 4 The Main Problems in the Management of College Students -- 4.1 Multi-card Multi-purpose, Time-Consuming Trouble -- 4.2 System Independent, Resource Separation -- 4.3 A Large Number of Students, Management "Failure" -- 4.4 Monitoring Weakness, "Vulnerability" Frequency Out -- 5 The Specific Solution to the Management of Colleges and Universities -- 5.1 Smart Card System -- 5.2 Construction of Off-Campus Information Platform -- 5.3 Establish a Database System -- 6 Conclusion -- References -- Convenient Top-k Location-Text Publish/Subscribe Scheme -- Abstract -- 1 Introduction -- 2 Basic Concepts -- 3 Publish/Subscribe System -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Memory Usage -- 5 Conclusions -- Acknowledgment -- References -- Effects of Foliar Selenium Fertilizer on Agronomical Traits and Selenium, Cadmium Contents of Different Rape Varieties -- Abstract -- 1 Introduction -- 2 Materials and Methods -- 2.1 Test Materials -- 2.2 Experimental Design -- 2.3 Climate Characteristics at Experimental Area -- 2.4 Indoor Test Species -- 2.5 Determination of Se, Cd Contents -- 2.6 Data Processing -- 3 Results -- 3.1 Agronomic Traits -- 3.2 Grain Se Contents. , 3.3 Agronomic Traits.

Note: Intro -- Preface -- Organization -- Contents -- New Frontier in Evolutionary Algorithms -- Citrus Disease and Pest Recognition Algorithm Based on Migration Learning -- Abstract -- 1 Introduction -- 2 Deep Learning and Migration Algorithm -- 2.1 Machine Learning -- 2.2 Deep Learning -- 2.2.1 Deep Learning Network Structure -- 2.2.2 The Forward Propagation Process -- 2.2.3 The Back Propagation Process -- 2.3 Migration Learning -- 3 Citrus Pest and Diseases Identification Based on Deep Learning and Migration Learning -- 3.1 Problem Description -- 3.2 The Structure of Convolutional Neural Network -- 3.2.1 Convolutional Layer -- 3.2.2 Sampling Layer -- 3.2.3 Dropout Layer and Flatten Layer -- 3.2.4 Fully Connected Layer -- 3.3 The Forward Propagation of Convolution Neural Network -- 3.3.1 Symbols Definition of the Convolutional Neural Network -- 3.3.2 The Input Process of the CNN -- 3.3.3 The Convolution Process of CNN -- 3.3.4 The Sampling Process of CNN -- 3.3.5 The Flatten Process and Output Process of CNN -- 3.4 The Backward Propagation of Convolution Neural Network -- 3.5 Data Preprocessing -- 3.6 Convolutional Network Model -- 3.7 Migration Learning Model -- 3.7.1 The Structure of VGG16 -- 3.7.2 Fine-Tuning Method -- 3.7.3 Migration Model Construction -- 4 Experimental Simulation and Analysis -- 5 Conclusion -- Acknowledgements -- References -- Artificial Bee Colony Based on Adaptive Selection Probability -- Abstract -- 1 Introduction -- 2 Artificial Bee Colony -- 3 Proposed Approach -- 3.1 Adaptive Selection Probability -- 3.2 Modified Mean Center -- 4 Experiments on Benchmark Functions -- 5 Conclusions -- Acknowledgement -- References -- Average Convergence Rate of Evolutionary Algorithms II: Continuous Optimisation -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Convergence and Average Convergence Rate. , 3.2 Links Between Two Average Convergence Rates -- 3.3 Numerical Calculation of Average Convergence Rate -- 4 General Analyses -- 4.1 Landscape-Invariant and -Adaptive Generators -- 4.2 Analysis of Landscape-Invariant Generators -- 4.3 Analysis of Landscape-Adaptive Generators -- 5 Case Study -- 5.1 Positive-Adaptive Generator -- 5.2 Connection Between ACR and Problem Dimension -- 6 Discussion of Positive-Adaptive Generators -- 7 Conclusions -- References -- Optimization Design of Multi-layer Logistics Network Based on Self-Adaptive Gene Expression Programming -- Abstract -- 1 Introduction -- 2 Problem Description and Model Construction -- 2.1 Problem Description -- 2.2 Model Hypothesis -- 2.3 Parameter Definition -- 2.4 Mathematical Model Construction -- 3 Self-Adaptive Gene Expression Programming Algorithm Based on Prüfer Coding -- 3.1 Gene Coding and Decoding -- 3.2 Fitness Function -- 3.3 Genetic Operation Design for Logistics Network -- 3.4 Self-Adaptive Operator Design -- 3.5 Self-Adaptive Operator Design -- 4 Experimental Simulation and Analysis -- 4.1 Experimental Data and Algorithm Parameters -- 4.2 Experimental Results and Analysis -- 5 Conclusion -- Acknowledgement -- References -- Potential Well Analysis of Multi Scale Quantum Harmonic Oscillator Algorithms -- 1 Introduction -- 2 Multiscale Quantum Harmonic-Oscillator Algorithm -- 2.1 Core Concepts of MQHOA -- 2.2 Physical Model of MQHOA -- 2.3 Framework of MQHOA -- 3 Analysis of Different Potential Wells -- 3.1 Choice of the Potential Well -- 3.2 Proposed Algorithm with Different Potential Wells -- 4 Experimental Results and Discussion -- 4.1 Convergence Under Double Well Function -- 4.2 Comparison of Potential Well Models for Global Optimization -- 5 Conclusion -- References -- Design and Implementation of Key Extension and Interface Module Based on Quantum Circuit -- Abstract. , 1 Introduction -- 2 Relevant Technology -- 2.1 Quantum Gate -- 2.2 Quantum Circuit -- 2.3 Encryption Technology and Encryption System of Quantum Circuit -- 3 Design of Key Extension Module Based on Quantum Circuit -- 3.1 Key and Key Extension -- 3.2 Introduction of Key Extension Algorithms -- 3.3 Theory of Designing Key Extension Algorithms Based on Quantum Circuits -- 3.4 Implementation of Key Extension Algorithms Based on Quantum Circuit -- 4 The Design of Interface Module -- 4.1 Introduction of the SPI Interface -- 4.2 The Design of SPI Interface -- 5 Verification and Testing -- 5.1 Verification of the Key Extension Module -- 5.2 Transfer Test of Interface Module -- 6 Summary -- Acknowledgements -- References -- Research on Atmospheric Data Assimilation Algorithm Based on Parallel Time-Varying Dual Compression Factor Particle Swarm Optimization Algorithm with GPU Acceleration -- Abstract -- 1 Introduction -- 2 Particle Swarm Optimization -- 3 A Particle Swarm Optimization Algorithm with Time-Varying Compression Factor Based on GPU Acceleration -- 3.1 Introduction to Particle Swarm Optimization with Time-Varying Dual Compression Factors -- 3.2 Design Principle of Assimilation Algorithm of PSOTVCF Based on GPU Acceleration -- 3.3 PSOTVCF Algorithm Based on GPU Acceleration -- 4 Numerical Test Results and Analysis -- 4.1 Convergence Accuracy -- 4.2 Assimilation Time -- 5 Conclusions and Prospects -- Funding Information -- References -- A Parallel Gene Expression Clustering Algorithm Based on Producer-Consumer Model -- 1 Introduction -- 2 Clustering Algorithm Based on Gene Expression Programming -- 2.1 Gene Expression Programming -- 2.2 Cluster Analysis Based on Gene Expression Programming -- 3 Parallel Gene Expression Programming Clustering Algorithm Based on Population Migration Strategy (PGEPC/PCM). , 3.1 Inadequacies of Basic Gene Expression Programming Clustering Algorithm -- 3.2 Parallel GEP Clustering Algorithm Based on Producer-Consumer Model (PGEPC/PCM) -- 4 Experiment and Result Analysis -- 4.1 Data Sets -- 4.2 Parallel GEP Clustering Algorithm Based on Producer-Consumer Model (PGEPC/PCM) -- 5 Conclusion and Future Work -- References -- Evolutionary Multi-objective and Dynamic Optimization -- Decomposition-Based Dynamic Multi-objective Evolutionary Algorithm for Global Optimization -- 1 Introduction -- 2 Preliminary -- 2.1 Global Optimization Problem -- 2.2 Multi-objective Optimization Problem -- 3 Conversion of a Global Optimization Problem to a Dynamic Multi-objective Optimization Problem -- 4 The Proposed DMOEA/D-M2M Algorithm -- 5 Experimental Studies -- 5.1 Investigation of the Population Diversity -- 5.2 Benchmark Test Functions -- 5.3 Parameter Settings -- 5.4 Comparisons with State-of-the-Art Algorithms -- 6 Conclusion -- References -- A Novel Multi-objective Evolutionary Algorithm Based on Space Partitioning -- 1 Introduction -- 2 Related Work -- 3 MOEA Based on Space Partitioning -- 3.1 The Framework of MOEA-SP -- 3.2 Subspace-Oriented Domination and Sorting -- 3.3 Environmental Selection -- 3.4 Historical Archive Update -- 4 Experimental Studies -- 4.1 Benchmark Functions and Performance Metric -- 4.2 Peer Algorithms and Parameter Settings -- 4.3 Experimental Results -- 5 Conclusion -- References -- Neural Architecture Search Using Multi-objective Evolutionary Algorithm Based on Decomposition -- 1 Introduction -- 2 MOEA/D-Net Method -- 2.1 Search Space -- 2.2 Operation Encoding -- 2.3 Search Process -- 3 Experimental Results -- 3.1 Training Details -- 3.2 Result Analysis -- 4 Conclusion -- References -- A Collaborative Evolutionary Algorithm Based on Decomposition and Dominance for Many-Objective Knapsack Problems. , 1 Introduction -- 2 Decomposition-Dominance Collaboration Mechanism -- 2.1 Generation and Update of the Archive -- 2.2 Repair of the Population -- 3 MOEA/D-DDC -- 3.1 Basic Framework -- 3.2 Reproduction and Greedy Repair -- 3.3 Combining Population and Archive -- 4 Numerical Experiments -- 4.1 Performance Metric -- 4.2 Parameter Settings -- 4.3 Experimental Results -- 5 Conclusion -- References -- A Many-Objective Algorithm with Threshold Elite Selection Strategy -- 1 Introduction -- 2 Related Works -- 2.1 Balanceable Fitness Estimation Strategy (BFE) -- 2.2 Reference-Point Based Non-dominated Sorting Strategy (RNS) -- 3 The Proposed Algorithm -- 3.1 Many-Objective Evolutionary Algorithm Based on Threshold Elite Selection Strategy (MaOEA-TES) -- 3.2 Adaptive Penalty Distance Boundary Intersection Strategy (APDBI) -- 3.3 Dynamic Threshold Selection Strategy (DTS) -- 4 Experimental Results and Analysis -- 5 Conclusion -- References -- Multi-objective Optimization Algorithm Based on Uniform Design and Differential Evolution -- Abstract -- 1 Introduction -- 2 Multi-objective Evolutionary Algorithm -- 2.1 Problem Formulation of Multi-objective Optimization -- 2.2 Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D) -- 3 MOEA/D Based on Uniform Design and Differential Evolution -- 3.1 Generate a Weight Vector Using Uniform Design Methods -- 3.2 Differential Evolution Method -- 3.3 Algorithm Description -- 4 Experiment Results -- 4.1 Experimental Settings -- 4.2 Benchmark Problems -- 4.3 Experimental Results -- 5 Conclusions -- Acknowledgement -- References -- Research on Optimization of Multi-target Logistics Distribution Based on Hybrid Integer Linear Programming Model -- Abstract -- 1 Introduction -- 2 Model Construction Background -- 2.1 Description of the Problem -- 2.2 Basic Assumptions -- 3 Model Construction -- 3.1 Basic Thoughts. , 3.2 Model Descriptions.