Advances in Swarm Intelligence 8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 - August 1, 2017, Proceedings, Part II
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| Main Authors | , , , |
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| Format | eBook Conference Proceeding |
| Language | English |
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Cham
Springer International Publishing AG
2017
Springer International Publishing |
| Edition | 1 |
| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
| ISBN | 3319618326 9783319618326 |
| ISSN | 0302-9743 1611-3349 |
| DOI | 10.1007/978-3-319-61833-3 |
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| Author | Tan, Ying Niu, Ben Takagi, Hideyuki Shi, Yuhui |
|---|---|
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| DOI | 10.1007/978-3-319-61833-3 |
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| Editor | Tan, Ying Niu, Ben Takagi, Hideyuki Shi, Yuhui |
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| RelatedPersons | Kleinberg, Jon M. Mattern, Friedemann Naor, Moni Mitchell, John C. Terzopoulos, Demetri Steffen, Bernhard Pandu Rangan, C. Kanade, Takeo Kittler, Josef Weikum, Gerhard Hutchison, David Tygar, Doug |
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| SubjectTerms | Algorithm Analysis and Problem Complexity Artificial Intelligence Computer Science Data Mining and Knowledge Discovery Numeric Computing Simulation and Modeling Swarm intelligence-Congresses |
| Subtitle | 8th International Conference, ICSI 2017, Fukuoka, Japan, July 27 - August 1, 2017, Proceedings, Part II |
| TableOfContents | 3.2.1 Encoding -- 3.2.2 Four Key Mechanisms of MOCLBFO -- 3.2.2.1 Health Evaluation -- 3.2.2.2 Non-dominance Choice -- 3.2.2.3 Comprehensive Learning Mechanism -- 3.2.2.4 Constrained Boundary Control -- 3.2.3 Computational Steps of MOCLBFO Algorithm for PO Model -- 3.3 Experimental Data -- 4 Experimental Results and Analyses -- 5 Conclusions -- Acknowledgment -- References -- Metaheuristics for Portfolio Optimization -- Abstract -- 1 Introduction -- 2 Problem Description -- 3 Cuckoo Search Metaheuristic -- 4 Experimental Results and Discussion -- 4.1 Experimental Procedure and Datasets -- 4.2 Results Based on Markowitz and Sharpe Models -- 5 Conclusion -- References -- Community Detection -- Community Detection Under Exponential Random Graph Model: A Metaheuristic Approach -- 1 Introduction -- 2 Exponential Random Graph Model -- 2.1 Basic Properties -- 2.2 Erdös-Rényi Model -- 2.3 ERGM with Independent Communities -- 3 The Methodology -- 3.1 Objective Function -- 3.2 Swam Intelligence Based Method -- 3.3 Modifications for Community Detection -- 4 Simulation Study -- 4.1 One Community with Poisson Assumption -- 4.2 Two Communities with the Power-Law Assumption: LFR Benchmark Graphs -- 5 Conclusion -- References -- An Enhanced Particle Swarm Optimization Based on Physarum Model for Community Detection -- 1 Introduction -- 2 Related Work -- 2.1 Community Detection -- 2.2 PSO for Community Detection -- 3 Physarum-inspired PSO for Community Detection -- 3.1 The Physarum-based network mathematical model -- 3.2 Physarum-Inspired Network Model for Community Detection -- 4 Experiments and Results -- 4.1 Results on Benchmark Networks -- 4.2 Results on Real-World Networks -- 5 Conclusion -- References -- The Design and Development of the Virtual Learning Community for Teaching Resources Personalized Recommendation -- Abstract -- 1 Introduction -- 2 Spark 3 Implementation of Personalized Recommendation System of Teaching Resources in Virtual Learning Community -- 3.1 System Structure -- 3.2 Key Technology Design -- 4 Recommendation Algorithm Design -- 4.1 ALS Algorithm -- 4.2 ALS Algorithm Problems -- 4.3 Improved Design of ALS Algorithm -- 4.4 ALS Algorithm Evaluation Index -- 4.5 Experiment and Result Analysis -- 5 Conclusion -- Acknowledgments -- References -- Effects of Event Sentiment on Product Recommendations in a Microblog Platform -- Abstract -- 1 Introduction -- 2 Literature Review -- 2.1 Special Events Versus Emotional Effects -- 2.2 Social Networking Sites Versus Social Marketing -- 2.3 Emotional and Psychological Theory -- 2.4 Sentiment Analysis Versus Emotional Lexicon -- 3 Research Methodology -- 3.1 Plurk Versus Data Collection -- 3.2 Product Recommendation Method -- 3.3 Hypothesis -- 3.4 Variables and Analytical Methods -- 3.4.1 Explanation of Variables -- 3.4.2 Analytical Method -- 4 Research and Implementation -- 4.1 Data Preprocessing -- 4.2 Experimental Results -- 4.2.1 Control Variable: Positive Event (Christmas) -- 4.2.2 Control Variable: Negative Event (Political Election) -- 5 Conclusions -- References -- Multi-agent Systems and Swarm Robotics -- Solar Irradiance Forecasting Based on the Multi-agent Adaptive Fuzzy Neuronet -- Abstract -- 1 Introduction -- 2 Clear-Sky Irradiance Modeling -- 3 The Multi-agent Adaptive Fuzzy Neuronet for Hourly Solar Irradiance Forecasting -- 4 Results -- 5 Conclusions -- Acknowledgements -- References -- Passive Field Dynamics Method: An Advanced Physics-Based Approach for Formation Control of Robot Swarm -- 1 Introduction -- 2 Related Work -- 3 Passive Field Dynamics Method -- 3.1 Standard Physics-Based Approach -- 3.2 The Development of Passive Field Dynamics Method -- 3.3 Optimization of the Force Field -- 4 Simulations and Analysis Improved Interval Multi-objective Evolutionary Optimization Algorithm Based on Directed Graph -- Abstract -- 1 Introduction -- 2 Multi-objective Evolutionary Algorithm with Directed Graph and Individual Prediction -- 2.1 Framework -- 2.2 Construction of the Directed Graph -- 2.3 Individual Prediction Based on PSO -- 2.4 Simulation Binary Crossover (SBX) Strategy Based on the Predicted Individuals -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Experimental Results and Analysis -- 3.2.1 Dominant Proportion of the Newly Generated Individuals -- 3.2.2 Analysis on Convergence -- 4 Conclusion -- Acknowledgments -- References -- A Novel Linear Time Invariant Systems Order Reduction Approach Based on a Cooperative Multi-objective Genetic Algorithm -- Abstract -- 1 Introduction -- 2 Cooperative Multi-objective Genetic Algorithm -- 3 Order Reduction Problem for Linear Time Invariant Systems -- 4 Experimental Results -- 5 Conclusion -- Acknowledgements -- References -- Solving Constrained Multi-objective Optimization Problems with Evolutionary Algorithms -- 1 Introduction -- 2 Background -- 2.1 Constrained Multi-objective Optimization -- 2.2 Algorithms -- 3 Experimental Setup -- 3.1 Algorithms -- 3.2 Benchmark Functions -- 3.3 Performance Measures -- 3.4 Statistical Analysis -- 4 Results -- 4.1 Inverted Generational Distance -- 4.2 Hypervolume and -metric -- 5 Conclusion -- References -- Portfolio Optimization -- Multi-objective Comprehensive Learning Bacterial Foraging Optimization for Portfolio Problem -- Abstract -- 1 Introduction -- 2 Multi-objective Comprehensive Learning Bacterial Foraging Optimization -- 2.1 Bacterial Foraging Optimization -- 2.2 Multi-objective Comprehensive Learning Bacterial Foraging Optimization -- 3 MOCLBFO for Portfolio Optimization Problem -- 3.1 Portfolio Optimization Model -- 3.2 MOCLBFO for Portfolio Optimization Model 5 Conclusion and Discussion -- References -- Adaptive Potential Fields Model for Solving Distributed Area Coverage Problem in Swarm Robotics -- 1 Introduction -- 2 Related Work -- 3 Problem Formulation -- 4 Adaptive Potential Fields Model -- 4.1 Lennard-Jones Potential Fields -- 4.2 Guided Growth Potential Field Model -- 5 Simulation Results and Discussions -- 5.1 Algorithms for Comparison -- 5.2 Simulation Results and Discussion -- 6 Conclusion -- References -- Swarm-Based Spreading Points -- Abstract -- 1 Introduction -- 2 Proposed Approach -- 2.1 Optimization Models -- 2.2 Swarm-Based Spreading Points Algorithm -- 2.3 Computing a Feasible Direction -- 3 Computation Results -- 4 Conclusions and Future Work -- Acknowledgments -- Appendix -- References -- A Survivability Enhanced Swarm Robotic Searching System Using Multi-objective Particle Swarm Optimization -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Problem, Approach, Algorithm -- 3.1 System Settings -- 3.2 Particle Swarm Optimization in Multi-robot Searching -- 3.3 Energy-Optimized MOPSO -- 4 Experiments and Results -- 4.1 Setup -- 4.2 Results and Discussion -- 5 Conclusion -- References -- Autonomous Coordinated Navigation of Virtual Swarm Bots in Dynamic Indoor Environments by Bat Algorithm -- 1 Introduction -- 2 The Bat Algorithm -- 3 Bat Algorithm Method for Dynamic Indoor Navigation -- 4 Experimental Results -- 5 Conclusions and Future Work -- References -- Building Fractals with a Robot Swarm -- 1 Motivation and Related Work -- 2 Model and Assumptions -- 3 Algorithms -- 3.1 Tree-Based Fractals -- 3.2 Curve-Based Fractals -- 3.3 Space-Filling Fractals -- 4 Simulation Results -- 5 Conclusion -- References -- A Stigmergy Based Search Method for Swarm Robots -- 1 Introduction -- 2 Overall Principle -- 2.1 RFID System and Search Area -- 2.2 Stigmergy and Pheromone -- 3 Search Method 3.1 Velocity and Position Update Intro -- Preface -- Organization -- Contents - Part II -- Contents -- Part I -- Multi-objective Optimization -- A Parametric Study of Crossover Operators in Pareto-Based Multiobjective Evolutionary Algorithm -- 1 Introduction -- 2 Background -- 2.1 Related Work -- 2.2 Crossover Operator -- 3 Computational Condition -- 4 Results and Discussion -- 5 Conclusion -- References -- Non-dominated Sorting and Crowding Distance Based Multi-objective Chaotic Evolution -- 1 Introduction -- 2 An Overview on Chaotic Evolution and NSGA-II -- 2.1 Chaotic Evolution -- 2.2 NSGA-II -- 3 Multi-objective Chaotic Evolution Using Non-dominated Sorting and Tournament Selection with Crowding Distance -- 4 Numerical Evaluations -- 5 Discussions and Analyses -- 5.1 Discussion on the Number of Pareto Frontier Solution -- 5.2 Discussion on Comparison of Chaotic and Random Generators -- 6 Conclusion -- References -- On Performance Improvement Based on Restart Meta-Heuristic Implementation for Solving Multi-objective Optimization Problems -- Abstract -- 1 Introduction -- 2 PICEA-g and Restart Meta-Heuristic for Multi-objective Optimization Algorithms -- 2.1 Preference-Inspired Co-evolutionary Algorithm with Goal Vectors -- 2.2 Restarting Operator Meta-Heuristic -- 3 Performance Assessment -- 3.1 Test Multi-objective Problems -- 3.2 Experimental Results -- 4 Conclusion -- Acknowledgements -- References -- Using Multi-objective Evolutionary Algorithm to Solve Dynamic Environment and Economic Dispatch with EVs -- Abstract -- 1 Introduction -- 2 DEED of Power System with Electric Vehicles Modeling -- 2.1 Objective Functions -- 2.2 System Constrains -- 3 Constraints Handing Method and Populations Set -- 3.1 Constraints Handling Method -- 3.2 Populations Set -- 4 Experiments and Discussion -- 5 Conclusion -- Acknowledgments -- References |
| Title | Advances in Swarm Intelligence |
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