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001445487 001__ 1445487
001445487 003__ OCoLC
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001445487 020__ $$a9789811912566$$q(electronic bk.)
001445487 020__ $$a9811912564$$q(electronic bk.)
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001445487 0247_ $$a10.1007/978-981-19-1256-6$$2doi
001445487 035__ $$aSP(OCoLC)1306066009
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001445487 049__ $$aISEA
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001445487 08204 $$a006.3/82$$223
001445487 1112_ $$aInternational Conference on Bio-inspired Computing, Theories and Applications$$n(16th :$$d2021 :$$cTaiyuan, China)
001445487 24510 $$aBio-inspired computing :$$b16th international conference, BIC-TA 2021, Taiyuan, China, December 17-19, 2021 : revised selected papers.$$nPart I /$$cLinqiang Pan, Zhihua Cui, Jianghui Cai, Lianghao Li (eds.).
001445487 24630 $$aBIC-TA 2021
001445487 264_1 $$aSingapore :$$bSpringer,$$c[2022]
001445487 264_4 $$c©2022
001445487 300__ $$a1 online resource (478 pages) :$$billustrations (some color).
001445487 336__ $$atext$$btxt$$2rdacontent
001445487 337__ $$acomputer$$bc$$2rdamedia
001445487 338__ $$aonline resource$$bcr$$2rdacarrier
001445487 4901_ $$aCommunications in computer and information science ;$$v1565
001445487 500__ $$aInternational conference proceedings.
001445487 500__ $$aIncludes author index.
001445487 500__ $$aDescription based upon print version of record.
001445487 5050_ $$aIntro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Evolutionary Computation and Swarm Intelligence -- An Optimization Task Scheduling Model for Multi-robot Systems in Intelligent Warehouses -- 1 Introduction -- 2 Related Work -- 3 Intelligent Warehouses -- 3.1 Problem Statement -- 3.2 BHM -- 4 Improved Intelligent Warehouses -- 4.1 New Model -- 4.2 New Model -- 5 Simulated Experiments -- 5.1 Performance Metrics -- 5.2 Parameter Settings -- 5.3 Analysis of Results -- 6 Conclusion -- References -- A Multi-objective Optimization Algorithm for Wireless Sensor Network Energy Balance Problem in Internet of Things -- Abstract -- 1 Introduction -- 2 WSN Energy Balance Problem -- 2.1 Problem Analysis -- 2.2 Problem Model -- 3 Algorithm Principle -- 3.1 NSGA-II -- 3.2 Clustering Mechanism -- 3.3 Framework of CMNSGA-II -- 4 Experimental Simulation -- 4.1 Parameter Setting -- 4.2 Result Analysis -- 5 Conclusion -- References -- Improved AODV Routing Protocol Based on Multi-objective Simulated Annealing Algorithm -- Abstract -- 1 Introduction -- 2 Ad Hoc Network and AODV Protocol -- 3 Multi-objective Simulated Annealing -- 3.1 Single-objective Simulated Annealing -- 3.2 Multi-objective Simulated Annealing -- 3.3 Main Process of Multi-target Annealing -- 4 Multi-objective Optimization AODV Routing Protocol -- 4.1 Network Model -- 4.2 Fitness Function -- 4.3 Perturbation Function -- 4.4 Multi-objective Simulated Annealing Optimization Algorithm -- 5 Simulation and Analysis -- 5.1 Simulation Scenario Description -- 5.2 Simulation Parameter Setting -- 5.3 Simulation Experiment Results and Comparative Analysis -- 6 Conclusion -- Acknowledgement -- References -- Solving Satellite Range Scheduling Problem with Learning-Based Artificial Bee Colony Algorithm -- Abstract -- 1 Introduction -- 2 Problem Description.
001445487 5058_ $$a3 Learning-Based Artificial Bee Colony Algorithm -- 3.1 Traditional Artificial Bee Colony Algorithm -- 3.2 Algorithm Overall Framework -- 3.3 Position-Based Learning Strategy -- 3.4 Error-Based Learning Strategy -- 3.5 Parameter Analysis -- 4 Experiment Analysis -- 4.1 Analysis of Algorithm -- 4.2 Comparison to State-Of-The-Art Algorithms -- 5 Conclusion -- Acknowledgement -- References -- Black Widow Spider Algorithm Based on Differential Evolution and Random Disturbance -- Abstract -- 1 Introduction -- 2 Black Widow Spider Algorithm -- 3 Improved Black Widow Spider Algorithm -- 3.1 Population Reproduction for Global Optimization -- 3.2 Population Mutation Based on Differential Evolution Algorithm -- 3.3 Random Disturbance Strategy -- 4 Algorithm Implementation -- 5 Simulation Experiment -- 5.1 Test Function -- 5.2 Design Problems of Tension Spring -- 6 Summary -- Acknowledgments -- References -- Attribute Selection Method Based on Artificial Bee Colony Algorithm and Neighborhood Discrimination Matrix Optimization -- 1 Introduction -- 2 Related Knowledge -- 2.1 Basic Knowledge of Neighborhood Rough Set -- 2.2 Artificial Bee Colony and Its Improved Algorithm -- 3 Improved Attribute Selection Algorithm for Artificial Bee Colony and Neighborhood Discrimination Matrix -- 3.1 Definition of Attribute Importance of Neighborhood Discernibility Matrix -- 3.2 Fitness Function Construction -- 3.3 Neighborhood Discernibility Matrix Importance and Artificial Bee Colony Feature Selection Algorithm -- 4 Experiment Analysis -- 4.1 Selection of -- 4.2 Algorithm Comparison Results -- 4.3 Algorithm Performance Comparison -- 5 Concluding Remarks -- References -- A Cuckoo Quantum Evolutionary Algorithm for the Graph Coloring Problem -- Abstract -- 1 Introduction -- 2 Problem Description -- 3 The Cuckoo Quantum Evolutionary Algorithm for GCP.
001445487 5058_ $$a3.1 Representation of the Solution to the Graph Coloring Problem -- 3.2 Quantum Matrix in the Cuckoo Quantum Evolutionary Algorithm -- 3.3 Framework of the Cuckoo Quantum Evolutionary Algorithm -- 3.4 Initialization of the CQEA -- 3.5 Local Search in the Solution Space -- 3.6 The Cuckoo Search -- 3.7 The Perturbance Strategy -- 4 Experimental Results -- 5 Conclusion -- References -- Feature Selection Algorithm Based on Discernibility Matrix and Fruit Fly Optimization -- 1 Introduction -- 2 Related Knowledge -- 2.1 Rough Set Theory -- 2.2 Fruit Fly Optimization Algorithm -- 3 Feature Selection Algorithm Based on Discernibility Matrix and Fruit Fly Optimization -- 3.1 Method of Defining Attribute Importance Based on Discernibility Matrix -- 3.2 Rough Set Fitness Function -- 3.3 Feature Selection Algorithm Based on Discernibility Matrix and Improved Fruit Fly Optimization -- 3.4 Algorithm Time Complexity -- 4 Experimental Data Analysis -- 4.1 Instance Verification -- 4.2 UCI Data Sets Experimental Data Analysis -- 5 Concluding Remarks -- References -- Feature Selection Method Based on Ant Colony Optimization Algorithm and Improved Neighborhood Discernibility Matrix -- 1 Introduction -- 2 Related Knowledge -- 2.1 Neighborhood Rough Set Theory -- 2.2 Ant Colony Algorithm and Its Optimized Feature Selection Method -- 3 Feature Selection Method Based on ACO and Improved Neighborhood Discernibility Matrix -- 3.1 The Definition of Attribute Importance of Neighborhood Discernibility Matrix -- 3.2 Description of Algorithm Steps -- 3.3 Analysis of Algorithm Time Complexity -- 4 Analysis of Experimental Data -- 4.1 Case Analysis -- 4.2 Analysis of UCI Data Set -- 5 Concluding Remarks -- References -- Implementation and Application of NSGA-III Improved Algorithm in Multi-objective Environment -- Abstract -- 1 Introduction.
001445487 5058_ $$a2 Many-Objective Problems and EMO Methodologies -- 2.1 Potential Difficulties in Dealing with Multi-objective Problems -- 2.2 Two Strategies to Face These Difficulties -- 3 NSGA-III Algorithm and Its Improvement -- 3.1 Determines the Reference Point on the Hyperplane -- 3.2 Normalization of Population Members -- 3.3 Association Operation -- 3.4 Inheritance of Population Offspring -- 3.5 Evaluation of Service Performance -- 4 Experiment -- 4.1 Experimental Environment Setting -- 4.2 Analysis of Experimental Results -- 5 Conclusion -- References -- A Differential Evolution Algorithm for Multi-objective Mixed-Variable Optimization Problems -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Overview -- 3.1 Review of NSGA-II -- 3.2 Review of MCDEmv -- 4 The Proposed MO-MCDEmv -- 4.1 Selection of the Optimal Individual -- 4.2 The Modifications of Statistics-Based Local Search -- 4.3 The Framework of MO-MCDEmv -- 5 Numerical Experiment -- 5.1 Set Relevant Parameters -- 5.2 The Results of Two Practical MO-MVOPs -- 6 Conclusion -- Acknowledgments -- References -- An Effective Data Balancing Strategy Based on Swarm Intelligence Algorithm for Malicious Code Detection and Classification -- 1 Introduction -- 2 Swarm Intelligence Optimization Model -- 3 Dynamic Sampling Strategy Based on Swarm Intelligence Algorithm -- 3.1 Dynamic Sampling Model -- 3.2 Dynamic Sampling Based on Swarm Intelligence Algorithm -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Type of Unbalanced Data -- 4.3 Data Sets and Models -- 4.4 Result Analysis -- 5 Conclusion -- References -- Adaptive Multi-strategy Learning Particle Swarm Optimization with Evolutionary State Estimation -- 1 Introduction -- 2 Related Works -- 2.1 Canonical PSO -- 2.2 Variants of PSO -- 3 Proposed AMSLPSO Algorithm -- 3.1 Evolutionary State Estimation.
001445487 5058_ $$a3.2 Random Elite and Mainstream Learning Exemplars -- 3.3 Choose Learning Strategy -- 3.4 Framework of AMSLPSO -- 4 Experimental Results and Analysis -- 4.1 Benchmark Functions and Comparison Algorithms -- 4.2 Experimental Results -- 5 Conclusion -- References -- Water Wave Optimization with Distributed-Learning Refraction -- 1 Introduction -- 2 The Basic WWO Algorithm -- 2.1 Propagation -- 2.2 Refraction -- 2.3 Breaking -- 2.4 The Algorithmic Framework of WWO -- 3 The Proposed DLWWO Algorithm -- 3.1 Nonlinear Dimension Reduction -- 3.2 Distributed-Learning Refraction -- 3.3 The Framework of DLWWO -- 3.4 Runtime Complexity of DLWWO -- 4 Numerical Experiments -- 4.1 Experimental Parameter Setting -- 4.2 Comparative Experiments -- 5 Conclusion -- References -- Adaptive Differential Privacy Budget Allocation Algorithm Based on Random Forest -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Algorithm-Related Definitions -- 3.1 Solving for Feature Weights and Decision Tree Weights. -- 3.2 Filtering of Feature Sets -- 3.3 Adaptive Allocation of Privacy Protection Budgets -- 4 Algorithm Implementation and Analysis -- 4.1 Algorithm Flow -- 4.2 Algorithm Description -- 5 Experimental Results and Analysis -- 5.1 Experimental Design -- 6 Experimental Results and Analysis -- 7 Conclusion and Discussion -- References -- A Node Influence Based Memetic Algorithm for Community Detection in Complex Networks -- Abstract -- 1 Introduction -- 2 Background Knowledge -- 2.1 Modularity -- 2.2 Transition Probability Matrix -- 3 Proposed Algorithm -- 3.1 Representation -- 3.2 Framework -- 3.3 Initialization -- 3.4 Selection -- 3.5 Crossover -- 3.6 Mutation -- 3.7 Multi-level Greedy Search -- 4 Experimental Study and Results Analysis -- 4.1 Experimental Settings -- 4.2 Experimental Results and Analysis -- 5 Conclusions -- Acknowledgements -- References.
001445487 506__ $$aAccess limited to authorized users.
001445487 520__ $$aThis two-volume set (CCIS 1565 and CCIS 1566) constitutes selected and revised papers from the 16th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2021, held in Taiyuan, China, in December 2021. The 67 papers presented were thoroughly reviewed and selected from 211 submissions. The papers are organized in the following topical sections: evolutionary computation and swarm intelligence; DNA and molecular computing; machine learning and computer vision.
001445487 650_0 $$aNatural computation$$vCongresses.
001445487 650_6 $$aCalcul naturel$$vCongrès.
001445487 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001445487 655_7 $$aConference papers and proceedings.$$2lcgft
001445487 655_7 $$aActes de congrès.$$2rvmgf
001445487 655_0 $$aElectronic books.
001445487 7001_ $$aPan, Linqiang,$$eeditor.
001445487 7001_ $$aCui, Zhihua,$$eeditor.
001445487 7001_ $$aCai, Jianghui,$$eeditor.
001445487 7001_ $$aLi, Lianghao,$$eeditor.
001445487 77608 $$iPrint version:$$aPan, Linqiang$$tBio-Inspired Computing: Theories and Applications$$dSingapore : Springer Singapore Pte. Limited,c2022$$z9789811912559
001445487 830_0 $$aCommunications in computer and information science ;$$v1565.
001445487 852__ $$bebk
001445487 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-981-19-1256-6$$zOnline Access$$91397441.1
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