Intelligent computing theories and application . Part I : 18th International Conference, ICIC 2022, Xi'an, China, August 7-11, 2022, proceedings / / editors, De-Shuang Huang [and five others]
Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Contents - Part III -- Evolutionary Computing and Learning -- Evolutionary Game Analysis of Suppliers Considering Quality Supervision of the Main Manufacturer -- 1 Introduction -- 2 Related Work -- 3 Two-Party Evolutionary Game Between Suppliers -- 3.1 Model Assumptions and Payoff Matrix -- 3.2 Evolutionary Game and Stability Analysis Between Supplier a and Supplier B -- 4 Simulation Analysis -- 5 Conclusion -- References -- Multi-party Evolution Stability Analysis of Electric Vehicles- Microgrid Interaction Mechanism -- 1 Introduction -- 2 Multi-party Evolutionary Game of EV-MG Interaction -- 3 Designing the Game Model -- 3.1 Describing the Game Strategy -- 3.2 Game Solution Analysis -- 3.3 Building the Simulation Model Based on System Dynamics -- 4 Discussion -- 5 Conclusion -- References -- An Efficient Multi-objective Evolutionary Algorithm for a Practical Dynamic Pickup and Delivery Problem -- 1 Introduction -- 2 Preliminary -- 2.1 A Brief Review of DPDPs -- 3 Problem Definition -- 3.1 Objective Functions -- 3.2 Constraints -- 4 The Proposed MOEA/D-ES -- 4.1 Framework -- 4.2 The Crossover Operator -- 4.3 The Local Search Procedure -- 5 Experimental Study -- 5.1 Benchmark Problems and Performance Metrics -- 5.2 The Compared Algorithms and Experimental Settings -- 5.3 The Comparison Experiments
on the HW Benchmarks -- 6 Conclusions and Future Work -- References -- An Efficient Evaluation Mechanism for Evolutionary Reinforcement Learning -- 1 Introduction -- 2 Preliminary -- 2.1 Related Works of the Combination of EA and RL -- 3 The Proposed Evaluation Mechanism and E-ERL -- 3.1 The Proposed Evaluation Mechanism -- 3.2 The Proposed E-ERL -- 3.3 Discussions on E-ERL -- 4 Experimental Studies -- 4.1 Benchmark Problems and Performance Metrics.
4.2 The Compared Algorithms and Experimental Settings -- 4.3 Comparisons Between E-ERL and Other Algorithms -- 5 Conclusions and Future Work -- References -- A Mixed-Factor Evolutionary Algorithm for Multi-objective Knapsack Problem -- 1 Introduction -- 2 Background -- 2.1 Multi-objective Evolutionary Algorithm -- 2.2 Multi-objective Knapsack Problem -- 3 Mixed-Factor Evolutionary Algorithm -- 3.1 Definitions -- 3.2 Framework of MFEA -- 3.3 Offspring Creation -- 4 Experiments and Results -- 4.1 Experiment Methodology -- 4.2 Constraints Handling -- 4.3 Experimental Results and Comments -- 5 Conclusion -- References -- NSLS with the Clustering-Based Entropy Selection for Many-Objective Optimization Problems -- 1 Introduction -- 2 Proposed Algorithm NSLS-CE -- 2.1 NSLS -- 2.2 Clustering-Based Entropy Selection -- 2.3 NSLS-CE -- 3 Experimental Result and Discussion -- 3.1 Test Problems -- 3.2 Performance Measures -- 3.3 Parameter Setting -- 3.4 Discussion of Results -- 4 Conclusions -- References -- Tunicate Swarm Algorithm Based Difference Variation Flower Pollination Algorithm -- 1 Introduction -- 2 Flower Pollination Algorithm -- 3 Tunicate Swarm Algorithm Based Differential Variation Flower Pollination Algorithm -- 3.1 Strategy of Simplified TSA -- 3.2 Differential Variation Strategy of Local Pollination -- 3.3 Dynamic Switching Probability Strategy -- 3.4 Procedure of TSA-DVFPA -- 3.5 Time Complexity Analysis of TSA-DVFPA -- 4 Simulation Experiment and Results Analysis -- 4.1 Experimental Parameter Settings -- 4.2 Comparative Analysis of Benchmark Test Function Experiment Results -- 4.3 Ablation Experiments -- 5 Conclusions and Future Works -- References -- A Multi-strategy Improved Fireworks Optimization Algorithm -- 1 Introduction -- 2 Related Work -- 2.1 Fireworks Algorithm -- 2.2 Multi-strategy Fireworks Algorithm.
3 Experimental Simulation and Analysis -- 3.1 Parameter Setting and Sensitivity Analysis -- 3.2 Experimental Results and Analysis -- 3.3 Statistical Test -- 4 Application of MSFWA in Engineering Constrained Optimization Problem -- 5 Concluding Remarks -- Appendix -- References -- A New Fitness-Landscape-Driven Particle Swarm Optimization -- 1 Introduction -- 2 Related Works -- 2.1 Fitness Distance Correlation -- 2.2 Basic Particle Swarm Optimization -- 3 Proposed FLDPSO Algorithm -- 3.1 Fitness-Landscape-Driven Strategy -- 3.2 Variants of PSO -- 3.3 Selection Strategy -- 4 Simulation -- 4.1 Evaluation Function and Parameter Setting -- 4.2 Experimental Results -- 5 Conclusions -- References -- Neighborhood Combination Strategies for Solving the Bi-objective Max-Bisection Problem -- 1 Introduction -- 2 Bi-objective Optimization -- 3 Neighborhood Combination Strategies -- 3.1 One-Flip Move -- 3.2 Two-Flip Move -- 4 Experiments -- 4.1 Parameters Settings -- 4.2 Performance Assessment Protocol -- 4.3 Computational Results -- 5 Conclusions -- References -- Neural Networks -- Rolling Bearing Fault Diagnosis Based on Model Migration -- 1 Introduction -- 2 Introduction of Transfer Learning -- 2.1 Transfer Learning -- 2.2 Methods of Transfer Learning -- 2.3 1D CNN Model and Parameters -- 2.4 Fault Diagnosis Procedure Based on Model Migration -- 3 Bearing Fault Diagnosis Based on Model Migration -- 3.1 Experimental Data -- 3.2
Network Training -- 3.3 Feature Visualization -- 3.4 Performance Comparison -- 4 Conclusion -- References -- Yak Management Platform Based on Neural Network and Path Tracking -- 1 Introduction -- 2 Theoretical Basis -- 2.1 Global Positioning System (GPS) -- 2.2 Neural Network (NN) -- 3 Research Content -- 3.1 Yak Counting Based on Subareas -- 3.2 Individual Analysis Based on Path Tracking -- 4 Function Design and Implementation.
4.1 Functions -- 4.2 Test of Functions -- 5 Conclusion -- References -- Stability Analysis of Hopfield Neural Networks with Conformable Fractional Derivative: M-matrix Method -- 1 Introduction -- 2 Preliminaries -- 3 Main Results -- 4 A Numerical Example -- 5 Conclusions -- References -- Artificial Neural Networks for COVID-19 Forecasting in Mexico: An Empirical Study -- 1 Introduction -- 2 Theoretical Framework -- 2.1 MLP -- 2.2 CNN -- 2.3 LSTM -- 2.4 LSTM-CNN -- 3 Experimental Set Up -- 3.1 Dataset -- 3.2 Free Parameters Specification -- 3.3 Performance of the DL Models -- 4 Results and Discussion -- 5 Conclusion -- References -- Multi-view Robustness-Enhanced Weakly Supervised Semantic Segmentation -- 1 Introduction -- 2 Related Work -- 3 The Proposed Approach -- 3.1 Overview -- 3.2 Network Framework -- 3.3 Loss Function -- 4 Experiments -- 4.1 Dataset and Evaluation Metric -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparison with Other SOTA Methods -- 5 Conclusions -- References -- Rolling Bearing Fault Diagnosis Based on Graph Convolution Neural Network -- 1 Introduction -- 2 Basic Theory -- 2.1 Graph Fourier Transform and Graph Convolution -- 2.2 Rolling Bearing Data Set and Graph Transformation -- 3 Graph Convolution Neural Network -- 3.1 Symbols and Problem Descriptions -- 3.2 Graph Convolution Neural Network Model Selection -- 3.3 Overall Neural Network Structure -- 4 Experimental Verification -- 4.1 Data Sample Division of Experiment 1 -- 4.2 Graph Convolution Neural Network Training of Experiment 1 -- 4.3 Experimental Comparison of Experiment 1 -- 4.4 Data Sample Division of Experiment 2 -- 4.5 Experimental Comparison of Experiment 2 -- 5 Conclusion -- References -- Research on Bearing Fault Feature Extraction Based on Graph Wavelet -- 1 Introduction -- 2 Graph Wavelet Based Bearing Fault Feature Extraction.
2.1 Graph Signal Processing -- 2.2 Graph Wavelet [14] -- 3 Graph Wavelet Based Bearing Fault Feature Extraction -- 3.1 Experimental Data -- 3.2 Analysis of Experimental Results -- 4 Performance Comparison of GWNN and GCN -- 4.1 Experimental Results Analysis -- 4.2 Comparative Analysis of Experimental Results -- 5 Conclusion -- References -- Correntrogram: A Robust Method for Optimal Frequency Band Selection to Bearing Fault Detection -- 1 Introduction -- 2 Correntrogram for Bearing Fault Detection -- 2.1 The Basic Theory of Correntropy -- 2.2 Definition of Spectral L2/L1 Norm and Signal Decomposition -- 2.3 Main Steps of Correntrogram for Bearing Fault Detection -- 3 Simulative Signal Analysis -- 4 Experimental Verification of Bearing Fault Detection -- 5 Conclusions -- References -- Semidefinite Relaxation Algorithm for Source Localization Using Multiple Groups of TDOA Measurements with Distance Constraints -- 1 Introduction -- 2 Proposed Method -- 2.1 Problem Formulation -- 2.2 Maximum Likelihood Estimation of Source Positions -- 2.3 Complexity Analysis -- 2.4 Constrained Cramér-Rao Lower Bound -- 3 Experiment and Analysis -- 4 Conclusion -- References -- Pattern Recognition -- Quasi Fourier Descriptor for Affine Invariant Features -- 1 Introduction -- 2 Preliminary -- 2.1 Affine Transform -- 2.2 Traditional FD -- 3 Affine Invariant Features with QFD -- 3.1 Definition of QFD -- 3.2 Properties of QFD -- 4 Experimental Results -- 4.1 Testing
of Invariance -- 4.2 Pattern Classification -- 4.3 Robustness to Noise -- 5 Conclusions -- References -- A New PM2.5 Concentration Predication Study Based on CNN-LSTM Parallel Integration -- 1 Introduction -- 2 A New Parallel Integrated Prediction Method of CNN and LSTM -- 2.1 Parallel Integrated Prediction Model Framework -- 2.2 CNN Model -- 2.3 LSTM Model -- 2.4 LSTM Model -- 3 Experiments and Results.