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 Tan, Ying, Takagi, Hideyuki, Shi, Yuhui, Niu, Ben
Format eBook Conference Proceeding
LanguageEnglish
Published Cham Springer International Publishing AG 2017
Springer International Publishing
Edition1
SeriesLecture Notes in Computer Science
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ISBN3319618326
9783319618326
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-61833-3

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Author 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
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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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