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 -- Contents -- Control of Asymmetry in the Radical Addition Approach to Chiral Amine Synthesis -- 1 Background and Introduction -- 2 Intermolecular Radical Addition to Chiral N-Acylhydrazones -- 2.1 Use of Chiral Auxiliaries in Radical Additions to Imino Compounds -- 2.2 Design of Chiral N-Acylhydrazones -- 2.3 Preparation and Initial Reactivity Studies of Chiral N-Acylhydrazones -- 2.3.1 Additions of Secondary and Tertiary Radicals -- 2.3.2 Triethylborane-Mediated Radical Additions Without Tin -- 2.4 Manganese-Mediated Radical Addition: Discovery and Method Development -- 2.5 Hybrid Radical-Ionic Annulation -- 2.5.1 Pyrrolidine Synthesis -- 2.5.2 Stepwise Annulation in Piperidine Synthesis -- 2.5.3 Application to Formal Synthesis of Quinine -- 2.6 Applications in Amino Acid Synthesis -- 2.6.1 Synthesis of gamma-Amino Acids -- 2.6.2 Synthesis of α,α-Disubstituted α-Amino Acids -- 2.7 Considerations for Synthesis Design Using Mn-Mediated Radical Addition -- 2.7.1 Functional Group Compatibility -- 2.7.2 Stereoconvergence for Flexibility in Synthetic Application -- 3 Asymmetric Catalysis of Radical Addition -- 4 Summary -- References -- Stereoselective Formation of Amines by Nucleophilic Addition to Azomethine Derivatives -- 1 Introduction -- 2 1,2-Addition of Unstabilized Carbanions to Chiral Azomethine Derivatives -- 2.1 Electrophilicity of Azomethine Derivatives -- 2.2 Diastereoselective Addition to Chiral Imines -- 2.2.1 Chiral Imines and Derivatives Obtained from Chiral Carbonyl Derivatives -- 2.2.2 Chiral Imines and Derivatives Obtained from Chiral Amines -- 2.3 Stoichiometric Amounts of Chiral Reagents -- 2.4 Catalytic Asymmetric Nucleophilic Addition -- 2.4.1 Background: Lewis Base Activation of the Nucleophile vs Transition Metal Catalysis -- 2.4.2 Addition of Alkylmetals (sp3 Carbon). , 2.4.3 Addition of Alkenyl, Aryl, and Heteroarylmetals (sp2 Carbon) -- 2.4.4 Addition of Alkynylmetals (sp Carbon) -- 2.4.5 Addition of Allylmetal -- 3 Conclusion -- References -- Transition Metal-Catalyzed Enantioselective Hydrogenation of Enamides and Enamines -- 1 Introduction -- 2 Enantioselective Hydrogenation of Enamides -- 2.1 Acyclic beta-Unsubstituted Enamides -- 2.1.1 Acyclic α-Arylethenamides -- 2.1.2 Acyclic α-Alkylethenamides -- 2.1.3 Other Acyclic beta-Unsubstituted Enamides -- 2.2 Acyclic beta-Substituted Enamides -- 2.2.1 Acyclic beta-Alkyl Substituted α-Arylenamides -- 2.2.2 Acyclic beta-Methoxymethoxy Substituted α-Arylenamides -- 2.2.3 Acyclic beta-Substituted α-Alkylenamides -- 2.3 Cyclic Enamides -- 3 Enantioselective Hydrogenation of Enamines -- 3.1 Acyclic Enamines -- 3.1.1 Acyclic α-Arylethenamines -- 3.1.2 Acyclic beta-Substituted α-Arylethenamines -- 3.2 Cyclic Enamines -- 4 Conclusion and Outlook -- References -- Asymmetric Hydrogenation of Imines -- 1 Introduction -- 2 Transition Metal Catalysts for Asymmetric Hydrogenation of Imines -- 2.1 Rh-Catalyzed Asymmetric Hydrogenation of Imines -- 2.2 Ti-Catalyzed Asymmetric Hydrogenation of Imines -- 2.3 Ru-Catalyzed Asymmetric Hydrogenation of Imines -- 2.4 Ir-Catalyzed Asymmetric Hydrogenation of Imines -- 2.4.1 Diphosphine Ligands in Ir-Based Catalysts -- 2.4.2 P,N Ligands in Ir-Based Catalysts -- 2.4.3 Phosphite, Phosphinite, and Phosphoramidite Ligands in Ir-Based Catalysts -- 2.4.4 Other Ir Catalysts -- 2.4.5 Additive Effects and Mechanistic Perspectives -- 2.5 Pd-Catalyzed Asymmetric Hydrogenation of Imines -- 2.5.1 Pd-Based Catalysts for Asymmetric Hydrogenation of Imines -- 2.5.2 Inhibitory Effect of Amine Product and Activation Strategy -- 3 Asymmetric Hydrogenation of Acyclic Imines -- 3.1 Asymmetric Hydrogenation of Activated Acyclic Imines. , 3.2 Asymmetric Hydrogenation of Non-activated Acyclic Imines -- 3.3 Asymmetric Hydrogenation of N-H Imines -- 4 Asymmetric Hydrogenation of Cyclic Imines -- 4.1 Asymmetric Hydrogenation of Activated Cyclic Imines -- 4.2 Asymmetric Hydrogenation of Non-activated Cyclic Imine Substrates -- 5 Conclusion -- References -- Advances in Transition Metal-Catalyzed Asymmetric Hydrogenation of Heteroaromatic Compounds -- 1 Introduction -- 2 Asymmetric Hydrogenation of Quinolines -- 2.1 Chiral Diphosphine Ligands -- 2.2 Other Chiral Phosphorus-Containing Ligands -- 2.3 Chiral Diamine Ligands -- 3 Asymmetric Hydrogenation of Isoquinolines -- 4 Asymmetric Hydrogenation of Quinoxalines -- 4.1 Chiral Phosphorus-Containing Ligands -- 4.2 Chiral Phosphine-Free Ligands -- 4.3 Metal/Brønsted Acid Catalytic System -- 5 Asymmetric Hydrogenation of Pyridines -- 6 Asymmetric Hydrogenation of Indoles and Pyrroles -- 7 Asymmetric Hydrogenation of Furans and Benzofurans -- 7.1 Chiral Phosphorus-Containing Ligands -- 7.2 Chiral N-Heterocyclic Carbene Ligands -- 8 Asymmetric Hydrogenation of Thiophenes and Benzothiophenes -- 9 Asymmetric Hydrogenation of Imidazoles and Oxazoles -- 10 Catalyst Immobilization -- 10.1 Biphasic Catalytic Systems -- 10.2 Catalyst Immobilization with Soluble Linear Polymer -- 10.3 Chiral Dendrimeric Catalyst -- 10.4 Catalyst Immobilization in Ionic Liquid -- 10.5 Catalyst Immobilization with Magnetic Nanoparticles -- 11 Mechanistic Aspects -- 11.1 Mechanism for Asymmetric Hydrogenation of Quinolines -- 11.2 Mechanism for Asymmetric Hydrogenation of Quinoxalines -- 12 Summary and Perspectives -- References -- Asymmetric Hydroamination -- 1 Introduction -- 2 Hydroamination of Alkenes -- 2.1 Metal-Catalyzed Intermolecular Hydroamination of Alkenes -- 2.2 Enzymatic Intermolecular Hydroamination of Alkenes -- 2.3 Cope-Type Hydroamination. , 2.4 Intramolecular Hydroamination of Aminoalkenes -- 2.4.1 Rare Earth Metal-Based Catalysts -- 2.4.2 Alkali Metal-Based Catalysts -- 2.4.3 Alkaline Earth Metal-Based Catalysts -- 2.4.4 Group 4 Metal-Based Catalysts -- 2.4.5 Group 5 Metal-Based Catalysts -- 2.4.6 Late Transition Metal-Based Catalysts -- 2.4.7 Organocatalytic Asymmetric Hydroamination of Aminoalkenes -- 2.4.8 Cope-Type Intramolecular Hydroamination -- 3 Hydroamination of Dienes -- 3.1 Intermolecular Hydroamination of Dienes -- 3.2 Intramolecular Hydroamination of Aminodienes -- 4 Hydroamination of Allenes -- 4.1 Intermolecular Hydroamination of Allenes -- 4.2 Intramolecular Hydroamination of Aminoallenes -- 5 Hydroamination of Alkynes -- 6 Hydroamination with Enantiomerical Pure Amines -- 6.1 Hydroaminations Using Achiral Catalysts -- 6.2 Kinetic Resolution of Chiral Aminoalkenes and Aminoallenes -- 7 Synthesis of Chiral Amines via Reaction Sequences Involving Hydroamination -- 8 Conclusions -- References -- Asymmetric Reductive Amination -- 1 Introduction -- 2 Organometallic Catalysis -- 2.1 Metal Catalyzed Hydrogenation -- 2.2 Metal Catalyzed Transfer Hydrogenation -- 3 Organocatalysis -- 3.1 Hydrosilanes as Hydrogen Source -- 3.2 Hantzsch Esters as Hydrogen Source -- 4 Biocatalysis -- 4.1 ARA with Amino Acid Dehydrogenases -- 4.2 ARA with omega-Transaminases -- 5 Summary and Outlook -- References -- Index.

Note: Front Cover -- Negative Emissions Technologies for Climate Change Mitigation -- Negative Emissions Technologies for Climate Change Mitigation -- Copyright -- Dedication -- Contents -- Preface -- 1 - The climate challenge: climate change, mitigation, and negative emissions -- 1.1 First steps in climate science -- 1.1.1 Carbonic acid and the Earth's temperature -- 1.1.2 The CO2 balance -- 1.1.3 Signs of trouble in the greenhouse -- 1.2 Earth system models-from pencils to petaFLOPs -- 1.3 The long road to international consensus -- 1.4 The climate challenge-warming limits and the carbon budget -- 1.5 Responses to the climate challenge-getting to zero emissions -- 1.5.1 Carbon intensity of energy -- 1.5.2 Energy intensity of production and other demand-side responses -- 1.5.3 Closing the emissions gap -- 1.6 Conclusion -- References and resources -- bksec2_7 -- Further reading -- 2 - Overview of negative emissions technologies -- 2.1 Early discussion of negative emissions -- 2.2 Negative emissions: geoengineering or mitigation? -- 2.3 Introduction to negative emissions technologies -- 2.3.1 The negative emissions landscape -- 2.3.2 Afforestation and other land- and soil-based methods -- Afforestation/reforestation -- Bioenergy with carbon capture and storage -- Soil carbon sequestration -- Habitat restoration-peatlands and blue carbon -- Enhanced weathering -- 2.3.3 Direct air capture -- 2.3.4 Ocean based methods -- Ocean fertilization -- Ocean alkalinity enhancement -- Macroalgae cultivation -- Ocean storage -- 2.4 NETs as a portfolio of options -- 2.4.1 Negative emissions effectiveness -- Life cycle assessment -- Carbon cycle and climate feedbacks -- 2.4.2 Negative emissions potentials and costs -- 2.5 The process of technology innovation -- 2.5.1 Technology readiness level classification -- 2.5.2 The RD& -- D timescale. , 2.6 Negative emissions RD& -- D agenda -- References and resources -- bksec2_9 -- Further reading -- 3 - Ethics, risks, and governance of NETs -- 3.1 Introduction -- 3.2 Ethical concerns -- 3.2.1 Moral hazard and mitigation deterrence -- 3.2.2 Justice and equity -- 3.2.3 Hubris and tampering with nature -- 3.3 Managing NET uncertainties and risks -- 3.3.1 Uncertainties and risks -- 3.3.2 The process of risk management -- 3.3.3 Managing critical NET-related risks and uncertainties -- Further delayed and inadequate global emissions reductions -- Public acceptance of NETs -- Resource competition and conflict with other policy objectives -- Realistic inclusion of NETs in IAM scenarios -- Earth system response to NET deployment at scale -- 3.3.4 NET risk and benefit tradeoffs -- 3.4 NET governance -- 3.4.1 Governance principles -- 3.4.2 Governance needs -- 3.4.3 Governance levels -- International governance -- National governance -- Subnational and self-governance -- 3.4.4 Polycentric governance of NETs -- References and resources -- bksec2_12 -- bksec2_13 -- bksec2_14 -- 4 - The global carbon cycle -- 4.1 Carbon inventories -- 4.1.1 Atmospheric carbon inventory -- 4.1.2 Terrestrial carbon inventory -- 4.1.3 Oceanic carbon inventory -- 4.1.4 Carbon inventory of the lithosphere -- 4.2 Carbon fluxes -- 4.2.1 Atmosphere ↔ terrestrial biosphere fluxes -- 4.2.2 Atmosphere ↔ ocean fluxes -- 4.2.3 Atmosphere ↔ lithosphere fluxes -- 4.2.4 Impact of eCO2 and climate change on carbon fluxes -- 4.3 Carbon dioxide removal and the global carbon cycle -- 4.4 Current carbon cycle research challenges -- References and resources -- bksec2_9 -- bksec2_10 -- bksec2_11 -- 5 - Terrestrial carbon cycle processes -- 5.1 Introduction -- 5.2 Photosynthesis -- 5.2.1 C3 photosynthesis -- 5.2.2 C4 and CAM photosynthesis -- 5.2.3 Aboveground and belowground carbon allocation. , 5.3 Biomineralization -- 5.3.1 Calcite precipitation by cyanobacteria -- 5.3.2 Biocatalytic carbon mineralization -- 5.3.3 Carbon sequestration in phytoliths -- 5.4 Biogeochemical features and processes in soils -- 5.4.1 Soil microbiota -- 5.4.2 Humification -- 5.4.3 Soil structure -- 5.5 Modeling plant productivity and soil carbon processes -- 5.6 Impact of eCO2 and climate change on photosynthesis and soil carbon processes -- 5.6.1 Impact of changing atmospheric composition -- The impact of eCO2-FACE experiments -- eCO2 driven increase in water use efficiency -- 5.6.2 Impacts of climate change -- 5.6.3 Extreme disturbance effects -- References and resources -- bksec2_13 -- Further reading -- 6 - Ocean carbon cycle processes -- 6.1 Introduction -- 6.2 The solubility pump -- 6.3 The biological pump -- 6.3.1 Factors controlling ocean productivity -- 6.3.2 Calvin cycle alternatives in the ocean -- 6.4 Climate change and ocean carbon cycle processes -- 6.4.1 Climate change impact on marine physicochemical processes -- Thermohaline circulation -- Ocean acidification -- Ocean deoxygenation -- 6.4.2 Climate change impact on marine biological processes -- Net production and the food web -- Biomineralization -- 6.4.3 Ocean response to negative emissions -- References and resources -- bksec2_6 -- bksec2_7 -- bksec2_8 -- 7 - CO2 absorption -- 7.1 Chemical and physical fundamentals -- 7.1.1 Chemical CO2 absorption -- Amine-based CO2 absorption -- Aqueous carbonate-based CO2 absorption -- Enzyme-catalyzed chemical CO2 absorption -- Aqueous ammonia-based CO2 absorption -- Sodium hydroxide-based CO2 absorption -- Phase-change solvents -- "Solvent-free" chemical absorption -- 7.1.2 Physical CO2 absorption -- 7.2 CO2 absorption applications -- 7.2.1 Chemical absorption applications -- Amine-based CO2 chemical absorption -- Ammonia-based CO2 chemical absorption. , 7.2.2 Physical absorption applications -- Selexol process -- Rectisol process -- 7.3 CO2 absorption technology RD& -- D status -- 7.3.1 Improved amine-based systems -- 7.3.2 Enzyme-catalyzed aqueous carbonate solvents -- 7.3.3 Phase change solvents -- Bi-phasic liquid solvent R& -- D -- Precipitating solvent R& -- D -- 7.3.4 Ionic liquid solvents -- Task-specific ionic liquids -- Reversible ionic liquids -- 7.3.5 Solvent microencapsulation -- 7.3.6 Sodium and potassium hydroxide-based systems -- Flue gas CO2 capture using sodium hydroxide -- Direct air CO2 capture using sodium or potassium hydroxide solvents -- References and resources -- bksec2_11 -- Further reading -- 8 - CO2 adsorption -- 8.1 Physical and chemical fundamentals -- 8.1.1 Physical adsorption thermodynamics -- Sorption-desorption characteristics -- Chemical and physical CO2 adsorbents -- 8.1.2 Chemical sorbents -- Metal oxide sorbents -- Alkali metal carbonate sorbents -- Supported amine sorbents -- Hydrotalcites -- 8.1.3 Physical sorbents -- Zeolites -- Metal-organic frameworks -- 8.2 Adsorption process configurations -- 8.2.1 Fixed adsorption bed systems -- 8.2.2 Moving adsorption bed systems -- 8.2.3 Simulated moving beds -- 8.2.4 Fluidized beds -- Chemical looping -- 8.3 Sorbent regeneration processes -- 8.3.1 Temperature swing adsorption -- 8.3.2 Pressure swing adsorption -- Vacuum swing adsorption -- High-frequency pressure cycling -- 8.3.3 Moisture swing adsorption -- 8.3.4 Electromagnetic swing processes -- Charge modulated and supercapacitive swing adsorption -- 8.4 CO2 adsorption technology RD& -- D status -- 8.4.1 Advanced PSA/VPSA cycles -- Adsorption heat storage -- 8.4.2 Sorption-enhanced reactions -- 8.4.3 Novel sorbent materials -- High temperature sorbents -- Composite sorbents -- 8.4.4 Metal-organic frameworks. , Gate opening and breathing phenomena in flexible MOFs -- Phase change functionalized MOF sorbents -- 8.4.5 Chemical looping -- Chemical looping combustion -- Calcium looping postcombustion capture -- Hybrid combustion-gasification by chemical looping -- References and resources -- Further reading -- 9 - Membrane CO2 separation -- 9.1 Physical and chemical fundamentals -- 9.1.1 Porous membrane transport processes -- Viscous capillary flow -- Knudsen diffusion -- Surface diffusion and capillary condensation -- Molecular sieving -- 9.1.2 Solution-diffusion transport process -- 9.1.3 Mixed matrix membranes -- 9.1.4 Facilitated transport membranes -- 9.1.5 Ion transport membranes -- 9.1.6 Supported liquid membranes -- 9.1.7 Molecular gate membranes -- 9.2 Membrane configuration and preparation, and module construction -- 9.2.1 Membrane types -- 9.2.2 Membrane module configurations -- Spiral-wound modules -- Hollow-fiber modules -- Ceramic wafer stack modules -- 9.3 Membrane technology R& -- D status -- 9.4 Membrane separation applications -- 9.4.1 Membranes in IGCC applications -- 9.4.2 Oxygen ion transport membranes for syngas production -- 9.4.3 Membrane and molecular sieve applications in oxyfuel combustion -- Molecular sieves for oxygen production -- Ion transport membranes for oxygen production -- 9.4.4 Membrane applications in postcombustion CO2 separation -- High-temperature molten carbonate membrane -- Facilitated transport membranes -- Carbon molecular sieve membranes -- Gas-liquid membrane contactors -- References and resources -- bksec2_14 -- Further reading -- 10 - Carbon mineralization -- 10.1 Introduction -- 10.2 Mineral carbonation chemistry -- 10.3 Direct carbonation routes -- 10.3.1 Direct gas-solid carbonation -- 10.3.2 Direct aqueous carbonation -- 10.3.3 Carbonation in seawater and other brines. , 10.3.4 Direct electrochemical carbonation.

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.

Note: Includes bibliographical references and index