Advances in Swarm Intelligence 7th International Conference, ICSI 2016, Bali, Indonesia, June 25-30, 2016, Proceedings, Part II

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Bibliographic Details
Main Authors Tan, Ying, Shi, Yuhui, Li, Li
Format eBook Conference Proceeding
LanguageEnglish
Published Cham Springer International Publishing AG 2016
Springer International Publishing
Edition1
SeriesLecture Notes in Computer Science
Subjects
Algorithm Analysis and Problem Complexity
Artificial Intelligence
Computer Science
Data Mining and Knowledge Discovery
Models and Principles
Numeric Computing
Simulation and Modeling
Swarm intelligence-Congresses
Online AccessGet full text
ISBN3319410083
9783319410081
ISSN0302-9743
1611-3349
DOI10.1007/978-3-319-41009-8

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Table of Contents:
  • Intro -- Preface -- Organization -- Contents -- Part II -- Contents -- Part I -- Scheduling and Planning -- Hyper-heuristics for the Flexible Job Shop Scheduling Problem with Additional Constraints -- 1 Introduction -- 2 The Multi-objective Flexible Job Shop Scheduling Problem with Additional Constraints -- 3 The Heterogeneous Meta-hyper-heuristic Scheduling Algorithm -- 4 Empirical Evaluation of the Improved Priority-Based HMHH Scheduling Algorithm -- 5 Conclusion -- References -- On-Orbit Servicing Mission Planning for Multi-spacecraft Using CDPSO -- Abstract -- 1 Introduction -- 2 Problem Description -- 3 Mathematical Models -- 3.1 Decision Variables -- 3.2 Service Spacecraft's Consume Indicators -- 3.3 Target Spacecraft's Value Indicators -- 3.4 The Optimal Time and Fuel Consuming Indicators -- 3.5 Objective Function and Constraints -- 4 Hybrid Discrete Particle Swarm Optimization Algorithm -- 4.1 Discrete Particle Swarm Optimization -- 4.2 Improved Discrete Particle Swarm Algorithm Combined with Chaos -- 4.3 Implementation Process of On-Orbit Servicing Spacecraft Mission Planning Based on CDPSO -- 5 Simulation -- 6 Conclusion -- References -- Solving the Test Task Scheduling Problem with a Genetic Algorithm Based on the Scheme Choice Rule -- Abstract -- 1 Introduction -- 2 Problem Statement -- 3 Proposed Method -- 3.1 The Genetic Algorithm -- 3.2 The Scheme Choice Rule -- 3.3 Performance Evaluation -- 4 Computational Results -- 5 Conclusions -- Acknowledgments -- References -- Robust Dynamic Vehicle Routing Optimization with Time Windows -- Abstract -- 1 Introduction -- 2 Description of Dynamic Vehicle Routing Problems with Time Windows -- 3 Robust Dynamic Vehicle Routing Methods Based on Ant Colony Algorithm and Local Optimization Strategy -- 4 Comparison and Analysis of Simulation Results -- 5 Conclusions -- Acknowledgement -- References
  • References -- An Improved Ensemble Extreme Learning Machine Based on ARPSO and Tournament-Selection -- Abstract -- 1 Introduction -- 2 Preliminaries -- 2.1 Extreme Learning Machine -- 2.2 Attractive and Repulsive Particle Swarm Optimization -- 2.3 Tournament-Selection -- 3 The Proposed Algorithm -- 4 Experiment Results -- 5 Conclusions -- Acknowledgements -- References -- An Improved LMDS Algorithm -- Abstract -- 1 Introduction -- 2 Intrinsic Dimension -- 3 CMDS Algorithm and LMDS Algorithm -- 4 The Selection Algorithm of Landmark Points -- 5 Experiments -- 5.1 The Comparison of iLMDS and CMDS -- 5.2 The Relationship Between the Runtime of ILMDS and the Data Size -- 6 Conclusion -- References -- Clustering Algorithm -- An Improved K-means Clustering Algorithm Based on the Voronoi Diagram Method -- Abstract -- 1 Introduction -- 2 K-means Clustering Algorithm -- 2.1 Researches of K-means Clustering Algorithm -- 2.2 Theory of K-means Algorithm -- 3 Improvement of K-means Clustering Algorithm -- 3.1 Voronoi Diagram -- 3.2 Criterion Function Based on the Weighted Average Method -- 4 An Improved K-means Algorithm - VK-means Algorithm -- 4.1 VK-means Algorithm -- 4.2 Experiments Analysis -- 5 Conclusions -- Acknowledgments -- References -- Brain Storm Optimization with Agglomerative Hierarchical Clustering Analysis -- 1 Introduction -- 2 Fundamentals of Brain Storm Optimization -- 3 Brain Storm Optimization Based on Hierarchical Clustering -- 3.1 Agglomerative Hierarchical Clustering -- 3.2 BSO Based on Agglomerative Hierarchical Clustering -- 4 Numerical Experiments and Performance Analysis -- 4.1 Description of Functions and Parameters -- 4.2 Experimental Results and Analysis -- 5 Conclusion -- References -- Discovering Alias for Chemical Material with NGD -- Abstract -- 1 Introduction -- 2 Related Work -- 3 System Design -- 3.1 Data Sources
  • Task Oriented Load Balancing Strategy for Service Resource Allocation in Cloud Environment -- Abstract -- 1 Introduction -- 2 Related Work -- 3 Information Service Model -- 4 Load Balancing Strategy -- 5 Experiments -- 5.1 Experiment 1: Comparing DWLC with WLC -- 5.2 Experiment 2: Phenomena Analysis -- 6 Conclusion -- Acknowledgement -- References -- Solving Flexible Job-Shop Scheduling Problem with Transfer Batches, Setup Times and Multiple Resources in Apparel Industry -- Abstract -- 1 Introduction -- 2 Flexible Job Shop Problem (FJSP): A Literature Review -- 3 Dispatching Algorithm for FJSP -- 3.1 Problem Description -- 3.2 Steps of Dispatching Algorithm -- 3.2.1 Start -- 3.2.2 Machine Selection -- 3.2.3 Operation Selection -- 3.2.4 Update -- 3.2.5 Objective Function -- 4 A Case Study in Apparel Industry -- 5 Conclusions -- References -- A Comparative Analysis of Genetic Algorithms and QAP Formulation for Facility Layout Problem: An Application in a Real Context -- Abstract -- 1 Introduction -- 2 Literature Review -- 3 Problem Formulation -- 4 Approach Proposed -- 4.1 Sule's Conventional Method -- 4.2 Genetic Algorithm -- 4.2.1 Solution Representation and Initial Population -- 4.2.2 Selection, Crossover and Mutation -- 4.2.3 Fitness Function -- 5 Experimental Environment, Parameter Settings and Results -- 5.1 Crossover Probability and Mutation Probability -- 5.2 Assumptions Verification and ANOVA -- 5.3 Comparison of the Proposed Model -- 6 Conclusions -- References -- Machine Learning Methods -- An Empirical Evaluation of Machine Learning Algorithms for Image Classification -- Abstract -- 1 Introduction -- 2 Machine Learning Techniques -- 2.1 Artificial Neural Network -- 2.2 Decision Trees -- 2.3 k-NN -- 2.4 Naïve Bayes -- 3 Related Work -- 4 Experiments -- 4.1 Experimental Set-Up -- 4.2 Experimental Results -- 5 Conclusion -- Acknowledgements
  • 4.2 Performance Tested on Dataset Group1 and Comparison with Other Feature-Based Methods
  • 3 Document Clustering by SS P System -- 4 Example and Discussion -- 5 Conclusion -- Acknowledgments -- References -- Classification -- Crop Classification Using Artificial Bee Colony (ABC) Algorithm -- 1 Introduction -- 2 Artificial Bee Colony (ABC) Algorithm -- 3 Co-occurrence Matrix -- 4 Proposed Methodology -- 5 Experimental Results -- 6 Conclusions -- References -- Classification of Distorted Handwritten Digits by Swarming an Affine Transform Space -- 1 Background -- 2 Searching the Affine-Transformed Space Using PSO -- 2.1 Affine Transformed Digits -- 2.2 Representing Transform Parameters in PSO -- 2.3 Designing Matching Evaluation Measures -- 3 Experimental Design &amp -- Results -- 4 Discussion &amp -- Conclusion -- References -- DKDD_C: A Clustering- Based Approach for Distributed Knowledge Discovery -- Abstract -- 1 Introduction -- 2 Basic Concepts -- 2.1 Mining Distributed Association Rules -- 2.2 Clustering -- 2.3 Panorama of Existing Platforms -- 3 Motivation -- 4 New Approach -- 4.1 General Principe -- 4.2 Architecture of the New Approach -- 5 Implementation and Validation of the New Approach -- 5.1 Implementation. -- 5.2 Validation -- 6 Conclusion -- References -- Fuzzy Rule-Based Classifier Design with Co-operation of Biology Related Algorithms -- Abstract -- 1 Introduction -- 2 Problem Statement -- 3 Optimization Techniques -- 4 Experimental Results -- 5 Conclusion -- Acknowledgement -- References -- Identifying Protein Short Linear Motifs by Position-Specific Scoring Matrix -- 1 Introduction -- 2 Methods -- 2.1 Data Sets -- 2.2 Continuous Binding Residues Analysis -- 2.3 Physicochemical Properties Analysis of SLiMs and Their Flanking Regions -- 2.4 Evolutionary Information (PSSM) -- 2.5 SVM -- 3 Evaluation Criteria -- 4 Results and Discussion -- 4.1 Optimizing Window Size
  • 3.2 Data Classification -- 3.3 Experimental Methods -- 3.3.1 Simple Method -- 3.3.2 Classification Affixation Method -- 4 Experimental Results -- 4.1 Simple Method -- 4.2 Classification Affixation Method -- 4.3 Comparison Result of Two Methods -- 5 Conclusion and Future Research -- 5.1 Conclusion -- 5.2 Future Research -- References -- Estimate the Kinematics with EMG Signal Using Fuzzy Wavelet Neural Network for Biomechanical Leg Application -- Abstract -- 1 Introduction -- 2 Extract Synergies from EMG Signal -- 3 Estimation Kinematics Using Fuzzy Wavelet Neural Network -- 3.1 Wavelet Neural Network -- 3.2 Fuzzy Inference System -- 3.3 Fuzzy Wavelet Neural Network -- 4 Experiment Results and Analysis -- 4.1 Experimental Protocol -- 4.2 Results and Analysis -- 5 Conclusion -- Acknowledgement -- References -- A Physarum-Based General Computational Framework for Community Mining -- 1 Introduction -- 2 Related Works -- 2.1 Formulation of Community Mining -- 2.2 Algorithms for Community Mining -- 3 Physarum Computational Framework for Community Mining -- 3.1 A Physarum Mathematic Network Model for Community Mining -- 3.2 Physarum-Based General Computational Framework for Community Mining -- 4 Experiments -- 4.1 Datasets -- 4.2 Accuracy Comparison -- 4.3 Computation Complexity Analysis -- 5 Conclusion -- References -- Rank-Based Nondomination Set Identification with Preprocessing -- Abstract -- 1 Introduction -- 2 Concept of Nondomination: Importance and Issues -- 3 Proposed Method -- 4 Experimental Setup and Simulation Results -- 5 Conclusion -- Acknowledgement -- References -- Spiking Simplicial P Systems with Membrane Coefficients and Applications in Document Clustering -- Abstract -- 1 Introduction -- 2 Spiking Simplex P System with Membrane Coefficients -- 2.1 Spiking Simplex P System with Membrane Coefficients -- 2.2 Configuration and Computation