Advances in Swarm Intelligence, Part I

Name: Advances in Swarm Intelligence, Part I | Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part I
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Second International Conference, ICSI 2011, Chongqing, China, June 12-15, 2011, Proceedings, Part I

Ying Tan Yuhui Shi Yi Chai Guoyin Wang(Herausgeber*in)

Springer (Verlag)

Erschienen am 31. Mai 2011

XXVI, 639 Seiten

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Inhalt

Title
Preface
Organization
Table of Contents
Theoretical Analysis of Swarm Intelligence Algorithms
Particle Swarm Optimization: A Powerful Family of Stochastic Optimizers. Analysis, Design and Application to Inverse Modelling
Global Optimization Methods and Sampling
Generalized PSO (GPSO) and PSO Family
Selection of the PSO Version
How to Input Prior Information
Phosphorylation Site Prediction Using PSO
Methods and Data Generation
Results and Discussion
Conclusions
References
Building Computational Models of Swarms from Simulated Positional Data
Introduction
Target System: The Vicsek Model
Radial Basis Models
General Form
Modeling and Embedding for Swarming
Measuring Swarms
Methodology
Results
Case 1: Regular Vicsek Model
Case 2: Modified Vicsek Model
Conclusions
References
Robustness and Stagnation of a Swarm in a Cooperative Object Recognition Task
Introduction
Simplified Hexagonal Model
Results
Conclusion
References
Enforced Mutation to Enhancing the Capability of Particle Swarm Optimization Algorithms
Introduction
PSO Introduction
Enforced Mutation in PSO (EMPSO) Algorithm
Sample Examples
Example 1: Sphere Function with Difficulty Index of 1
Example 2: Rastrigin Function with n Variables and Difficulty Index of 1
Example 3: Rosenbrock Function with n Variables and Difficulty Index of 2
Example 4: Schafer Function with n Variables. Eq. (6) Shows n=2, (x, y) Only. The Difficulty Index is 3.
Conclusions
References
Normalized Population Diversity in Particle Swarm Optimization
Introduction
Preliminaries
Particle Swarm Optimization
Vector Norm and Matrix Norm
Normalized Population Diversity
Position Diversity
Velocity Diversity
Cognitive Diversity
Experimental Studies
Conclusion
References
Particle Swarm Optimization with Disagreements
Introduction
Standard Particle Swarm Optimization
Preliminary Studies
Disagreements
6-PSOD Operator
Experimental Setup
Results
Conclusion
References
PSOslope: A Stand-Alone Windows Application for Graphical Analysis of Slope Stability
Introduction
Slope Stability Analysis
PSO Formulation and Application Development
Verification Assessment
Computational Findings and Discussion
Summary and Conclusions
References
A Review of the Application of Swarm Intelligence Algorithms to 2D Cutting and Packing Problem
Introduction
Two-Dimensional Cutting and Packing Problem
Applications of Swarm Intelligence Algorithms in 2D Cutting and Packing Problem
Applications of Ant Colony Algorithms
Applications of Particle Swarm Optimization
Discussion
Conclusion
References
Particle Swarm Optimization
Inertia Weight Adaption in Particle Swarm Optimization Algorithm
Introduction
Adaptive Control of Inertia Weight
Methods in APSO
Adaption of Inertia Weight Based on Velocity
Adaptive Inertia Weight Based on Both Position and Velocity Information
Experiments and Comparison
Benchmark Functions and Variants of PSOs
Comparison and Discussion
PSOs with Elitist Learning Strategy (ELS)
Monitoring of the Inertia Weight
Conclusion
References
Nonlinear Inertia Weight Variation for Dynamic Adaptation in Particle Swarm Optimization
Introduction
Particle Swarm Optimization Algorithm
NDW-PSO
Simulation Results
Concluding Remarks
References
An Adaptive Tribe-Particle Swarm Optimization
Introduction
Basic PSO Algorithm
Tribe-PSO
Adaptive Tribe-Particle Swarm Optimization
Adaptive Inertia Weight
Adaptive Tribe-PSO
Experimental Setup and Simulation
Benchmark Functions
Parameter Settings
Results
Conclusion
References
A Novel Hybrid Binary PSO Algorithm
Introduction
The Hybrid Binary PSO (HBPSO) Algorithm
Crossover and Mutation
Experiments and Numerical Optimization Results
Conclusions and Discussion on Results
References
PSO Algorithm with Chaos and Gene Density Mutation for Solving Nonlinear Zero-One Integer Programming Problems
Introduction
PSO Algorithm with Chaos and Gene Density Mutation
Basic PSO
The Equation on the Position of the Particles
The Strategy of Overcoming Premature Phenomena
The Description of the New PSO Algorithm
Numerical Experiments
Test Function
Parameter Settings
Experimental Results and Analysis
Conclusion
References
A New Binary PSO with Velocity Control
Introduction
Particle Swarm Optimization
Continuous Particle Swarm Optimization
Binary Particle Swarm Optimization
Binary PSO with Velocity Control. Method Proposed.
Results Obtained
Conclusions
References
Adaptive Particle Swarm Optimization Algorithm for Dynamic Environments
Introduction
Related Works
Proposed Method
Experimental Studying
Moving Peaks Benchmark
Experimental Settings
Experimental Results
Conclusion
References
An Improved Particle Swarm Optimization with an Adaptive Updating Mechanism
Introduction
The Improved Particle Swarm Optimization Embedded an Adaptive Updating Mechanism
Numerical Experiments and Results
Conclusion
References
Mortal Particles: Particle Swarm Optimization with Life Span
Introduction
Particle Swarm Optimization
Mortal Particle Swarm Optimization
Mortal Particles
Differential Operator
Validation Experiments
Parameter Setting
Benchmark Functions
Simulation Results
Discussion and Conclusion
References
Applications of PSO Algorithms
PSO Based Pseudo Dynamic Method for Automated Test Case Generation Using Interpreter
Introduction
The Pseudo Dynamic Execution (PDE) Method
PSO Based Test Data Generator
Experiments and Results
Related Work
Conclusion
References
Reactive Power Optimization Based on Particle Swarm Optimization Algorithm in 10kV Distribution Network
Introduction
The Mathematical Model of Reactive Power Optimization
The Objective Function
The Power Constraint Equations
The Variable Constraint Equations
Particle Swarm Optimization
The Introduction of Inertia Factor
Initialization of PSO
System Examples
Conclusion
References
Clustering-Based Particle Swarm Optimization for Electrical Impedance Imaging
Introduction
The EIT Problem
Clustering-Based PSO for Image Reconstruction in EIT
Conclusions and Further Developments
References
A PSO- Based Robust Optimization Approach for Supply Chain Collaboration with Demand Uncertain
Introduction
Problem Description
The Cost Calculation
Calculating the Total Cost
Evaluation of the Decision Variables
Optimize the Decision Variables by the PSO
Description of the Standard PSO
Algorithm Steps
Numerical Experiments
Concluding Remarks
References
A Multi-valued Discrete Particle Swarm Optimization for the Evacuation Vehicle Routing Problem
Introduction
Particle Swarm Optimization
Problem Formulation and Solution Representation
Discrete Particle Swarm Optimization
Computational Result and Discussion
Experimental Setup
Comparison of Solutions Using DPSO and GA
Conclusion and Recommendation
References
A NichePSO Algorithm Based Method for Process Window Selection
Introduction
Niching Particle Swarm Optimizer
Particle Swarm Optimization
The Guaranteed Convergence Particle Swarm Optimizer (GCPSO)
Niching Particle Swarm Optimizer
NichePSO Algorithm Based Method for Process Window Selection
Simulation Results
DOE Analysis and Production Validation
Conclusion
References
Efficient WiFi-Based Indoor Localization Using Particle Swarm Optimization
Introduction
Related Work
Wireless Signal Propagation Model
Particle Swarm Based Localization Algorithm
Experiment and Results
Discussion
Conclusion
References
Using PSO Algorithm for Simple LSB Substitution Based Steganography Scheme in DCT Transformation Domain
Introduction
Related Works
DCTIASMTT
Particle Swarm Optimization Algorithm
A Steganographic Method Based Upon JPEG and Particle Swarm Optimization Algorithm
Proposed Schemes
Three Different Schemes
PSO Settings
Experimental Results and Discussions
Conclusion
References
Numerical Integration Method Based on Particle Swarm Optimization
Introduction
Overall Description Strategy of Particle Swarm Optimization
Examples
Example 1
Example 2
Conclusion
References
Identification of VSD System Parameters with Particle Swarm Optimization Method
Introduction
2 Particle Swarm Optimization Algorithm [3][4]
Inverter Parameters and Motor Nameplate Parameters [5] [6]
Objective Functions
Test System&Algorithm
Identification and Results
Summary
References
PSO-Based Emergency Evacuation Simulation
Introduction
The Abstractions and Representations
The Scenario
The Representation of Evacuation Solution
The PSO Based Emergency Evacuation Simulation
The Fitness Functions of PSO for EES
The Distance to the Danger Point (DDP)
The Distance to the Exit (DE)
The Triple-Distance Safe Degree (TDSD)
Experiment and Analysis
Conclusion and Future Works
References
Training Spiking Neurons by Means of Particle Swarm Optimization
Introduction
Spiking Neuron Model
Proposed Method
Adjusting Synapses of the Neuron Model
Experimental Results
Conclusions
References
Ant Colony Optimization Algorithms
Clustering Aggregation for Improving Ant Based Clustering
Introduction
The CAC Algorithm
The Furthest Algorithm for Clustering Aggregation
Hybridization of the CAC Algorithm and the Furthest Algorithm
Experimental Evaluation
Data Sets
Evaluation Measures
Experimentations
Conclusion
References
Multi-cellular-ant Algorithm for Large Scale Capacity Vehicle Route Problem
Introduction
Decomposition for Large Scale CVRP
Multi-cellular-ant Algorithm
Cellular Ants
Distributed Multi-cellular-ant Algorithm
Experimental Result
Solution Superiority
Efficiency for System Coupling
Discussion and Conclusions
References
Ant Colony Optimization for Global White Matter Fiber Tracking
Introduction
Global Fiber Tracking
Ant Colony Fiber Tracking
Probability Density Function of Fiber Orientation
Pheromone Representation and Diffusion
Ant Colony Fiber Tracking Algorithm
Experiments and Results
Synthetic Data
Brain Diffusion MRI
Conclusions
References
Bee Colony Algorithms
Artificial Bee Colony Based Mapping for Application Specific Network-on-Chip Design
Introduction
Related Work
Problem Formulation
Energy Model
Optimization Model
ABC Based Mapping
The Behavior of Leader Bee
The Behavior of Scout Bee
The Behavior of Follower Bee
The Pseudo Code
Experiments and Results
Experimental Descriptions
Experimental Results
Conclusions and Future Works
References
Using Artificial Bee Colony to Solve Stochastic Resource Constrained Project Scheduling Problem
Introduction
Definition of Stochastic RCPSP
Artificial Bee Colony (ABC) Algorithm
Application of ABC Algorithm for Stochastic RCPSP
Computational Results
Conclusions
References
Novel Swarm-Based Optimization Algorithms
Brain Storm Optimization Algorithm
Introduction
Brainstorming Process
Brain Storm Optimization Algorithm
Experiments and Discussions
Conclusions
References
Human Group Optimizer with Local Search
Introduction
Human Group Optimizer with Local Search
Implementation of Human Group Optimization
Search Direction
Step Length
The Neighbors of Every Seeker
Local Search Schedule
Simulation Results
Conclusions
References
Average-Inertia Weighted Cat Swarm Optimization
Introduction
Cat Swarm Optimization
AICSO Algorithm
Experimental Results
Conclusions
References
Standby Redundancy Optimization with Type-2 Fuzzy Lifetimes
Introduction
Preliminaries
Fuzzy Standby Redundancy Optimization Problem
Solution Method
An Approximation Scheme
Computing the Generalized Credibility
Hybrid Particle Swarm Optimization
A Numerical Example
Conclusions
References
Oriented Search Algorithm for Function Optimization
Introduction
Oriented Search Algorithm
Search-Individual
Search-Object
Oriented-Neighbor-Space
Search-Neighbor-Space, Updating Strategy of Search Direction and Search Step
Evaluation and Decision
OSA Architecture
Simulation and Analysis
Conclusion
References
Evolution of Cooperation under Social Norms in Non-structured Populations
Introduction
Model Summary
Simulation Results and Analysis
Conclusion
References
Collaborative Optimization under a Control Framework for ATSP
Introduction
Problem Description
The Collaborative Optimization
The Overall Scheme
The Implementation
Computational Experiments
Conclusion
References
Bio-Inspired Dynamic Composition and Reconfiguration of Service-Oriented Internetware Systems
Introduction
Background
Foraging Principles of Physarum polycephalum and Its Mathematical Model
A Brief Introduction to Internetware
Bio-inspired Service Composition and Reconfiguration Model
Problem Description
Mathematical Model
Algorithm Characteristics
Experimental Results and Discussion
Conclusions
References
A Novel Search Interval Forecasting Optimization Algorithm
Introduction
Preliminaries
Problem Definition
Subinterval Definition
Search Interval Forecasting Optimization Algorithm
Parameters Definition
Five Types of Searching Strategies
SIF Summarization
Experimental Studies
Benchmark Functions
Experimental Data
Conclusion
References
Artificial Immune System
A Danger Theory Inspired Learning Model and Its Application to Spam Detection
Introduction
Danger Theory Based Learning Model
Generating Signals
Classification Using Signals
The Framework of the DTL Model
Analysis of the DTL Model
Filter Spam Using the DTL Model
Feature Extraction
Selection of Classifiers
Performance Measures
Experiments of Spam Detection
The Effects of the Danger Zone
Comparison Experiments
Conclusions
References
Research of Hybrid Biogeography Based Optimization and Clonal Selection Algorithm for Numerical Optimization
Introduction
BBOCSA
Framework of BBOCSA
The Procedure of BBOCSA
Experimental Results
Conclusions and Future Work
References
The Hybrid Algorithm of Biogeography Based Optimization and Clone Selection for Sensors Selection of Aircraft
Introduction
The Procedure of BBOCSA
Benchmark Results
Sensor Selection of Aircaft
Results and Discussion
Conclusion
References
A Modified Artificial Immune Network for Feature Extracting
Introduction
The Immune System: From Information Processing Perspective
Immune Feature Extracting Network
Experiments Result and Analysis
The Experiments of Clustering Analysis
Function Approximation Experiment
Conclusion and Further Works
References
Differential Evolution
Novel Binary Encoding Differential Evolution Algorithm
Introduction
Conventional DE
Generation of Initial Population
Mutation Operator
Crossover Operator
Selection Operator
Binary Encoding DE
New Mutation Operator
Flowchart of the BDE
Numerical Experiments
Conclusions
References
Adaptive Learning Differential Evolution for Numeric Optimization
Introduction
Basic Differential Evolution
My Self-adaptive Differential Evolution
Learning Strategy
Experimental Comparison
Experimental Setting
Impact of Learning Frequency L
Conclusion
References
Differential Evolution with Improved Mutation Strategy
Introduction
Differential Evolution
Mutation
Crossover
Selection
TDE
Modified Trigonometric DE
Experimental Studies
Experimental Setup
Comparison between WDE and Other DE Algorithms
Discussion on Parameter p
Conclusion
References
Gaussian Particle Swarm Optimization with Differential Evolution Mutation
Introduction
The Improved Optimization Algorithm
Gaussian Particle Swarm Optimization Algorithm
Differential Evolution Mutation
Regeneration Strategy
Feasibility-Based Comparison Method
The Improved Gaussian Particle Swarm Optimization Algorithm
Simulation Results for Engineering Optimization Problems
Conclusions and Future Works
References
Neural Networks
Evolving Neural Networks: A Comparison between Differential Evolution and Particle Swarm Optimization
Introduction
Basics on Feed-Forward Neural Networks
Basics on Particle Swarm Optimization
Basics on Differential Evolution
Evolving the Synaptic Weights of an ANN Using PSO and DE
Experimental Results
Conclusions
References
Identification of Hindmarsh-Rose Neuron Networks Using GEO Metaheuristic
Introduction
Problem Description
Preliminaries
Problem Formation
Methodology
Solution Encoding
Identification Using GEO Metaheuristic
Simulation
The Parameters
The Results
Conclusions
References
Delay-Dependent Stability Criterion for Neural Networks of Neutral-Type with Interval Time-Varying Delays and Nonlinear Perturbations
Introduction
Problem Statement
Main Result and Its Proof
Numerical Examples
Conclusion
References
Application of Generalized Chebyshev Neural Network in Air Quality Prediction
Introduction
Database
Methodology
Chebyshev Neural Network
Learning Algorithm
Results and Discussion
Conclusions
References
Financial Time Series Forecast Using Neural Network Ensembles
Introduction
Algorithms and Methods
Backpropagation Algorithm
Radial Basis Neural Network
Recurrent Neural Network
Evolutionary BPA Neural Network
Ensembles
Experiment and Results
Research Data
Methodology
Empirical Results
Conclusions
References
Selection of Software Reliability Model Based on BP Neural Network
Introduction
BP Neural Network Model
Mechanism of BP Neural Network
Learning Steps in BP Algorithm
The Execution Process of Model Selection Based on BP Network
Software Reliability Model
Encoding
The Model Selection Process
Simulation Analysis
Conclusions
References
Genetic Algorithms
Atavistic Strategy for Genetic Algorithm
Introduction
Atavistic Evolutionary Strategies
Operation Process of GA
Atavistic Evolution Strategy
Atavistic Evolution GA
Effectiveness Analyzing of Atavistic Strategy
Experimental Simulation and Analysis
Conclusions
References
An Improved Co-Evolution Genetic Algorithm for Combinatorial Optimization Problems
Introduction
The Improved Co-Evolution Genetic Algorithm (ICGA)
The Traditional Genetic Algorithm (GA)
Mutation
Information Transfer Mode
The Optimal Solution and Nash Equilibrium
Numerical Experiments on Deceptive Problems
Strong-Linkage Deceptive Functions
Weak-Linkage Deceptive Functions
ICGA Applied in the Optimal Control
Discrete Linear System's Optimal Control Problem
Results
Conclusion
References
Recursive Structure Element Decomposition Using Migration Fitness Scaling Genetic Algorithm
Introduction
Background
Recursive Model
Optimization Problem
Migration Fitness Scaling GA
Experiments
Conclusions
References
A Shadow Price Guided Genetic Algorithm for Energy Aware Task Scheduling on Cloud Computers
Introduction
Energy Aware Task Scheduling
Shadow Price Guided Genetic Task Scheduling Algorithm
Shadow Price
Shadow Price Guided Genetic Algorithm
Energy Aware Shadow Price Guided Genetic Task Scheduling Algorithm
Experiment
Conclusion
References
A Solution to Bipartite Drawing Problem Using Genetic Algorithm
Introduction
Genetic Algorithm
Proposed Techniques
Chromosome Representation
Initialization
Stochastic Operator
Fitness Function
Experimental Results
Population Size
Graph Density
Crossing Minimization
Comparison of GA1 with GA2
Conclusion
References
Evolutionary Computation
Evaluation of Two-Stage Ensemble Evolutionary Algorithm for Numerical Optimization
Introduction
Algorithm
Experiment Comparison between TSEA and Its Sub-optimizers
Experiment Comparison between TSEA and State-of-the-Art EAs and MAs
Experiment Comparison with State-of-the-Art EAs
Experiment Comparison with State-of-the-Art MAs
Conclusion
References
A Novel Genetic Programming Algorithm for Designing Morphological Image Analysis Method
Introduction
Proposed GP Algorithm
Definition of GP Algorithm
Definition of Gene
Evolution Strategy
Experimental Results
Experiment on Artificial Binary Images
Experiment on OCR Music Sheet
Experiment on Gray Scale Image
Conclusion
References
Fuzzy Methods
Optimizing Single-Source Capacitated FLP in Fuzzy Decision Systems
Introduction
Problem Formulation
Handling Credibility Service Level Constraints
Numerical Experiments
Conclusions
References
New Results on a Fuzzy Granular Space
Introduction
Preliminaries on Fuzzy Granular Space
The Dynamic Property of a FE Relation on Its Granular Space
The Ordering Relationship between FE Relations and Their Granular Spaces
The Collaborative Clustering of FE Relations on Granular Space
Conclusions
References
Fuzzy Integral Based Data Fusion for Protein Function Prediction
Introduction
Methods
Fuzzy Measures and Choquet Integral
Our Improved Sugeno ?-Measure
Determine Fuzzy Measure Based on Particle Swarm Algorithm
Experimental Results and Analysis
Data Sets and Functional Classes of Protein
Experimental Setup and Results
Conclusions
References
Hybrid Algorithms
Gene Clustering Using Particle Swarm Optimizer Based Memetic Algorithm
Introduction
Methods
Particle Encoding and Fitness Calculation
Global Search
Local Search
Experimental Design
Results
Mean Square Error, Homogeneity and Separation
Sensitivity to the Choice of Initial Centroids
Convergence Rate
Conclusion and Discussion
References
Hybrid Particle Swarm Optimization with Biased Mutation Applied to Load Flow Computation in Electrical Power Systems
Introduction
Power System Load Flow Analysis
Hybrid Particle Swarm Optimization with Biased Mutation Applied to Load Flow Computation
Mutation Operation in Genetic Algorithms
Definition of the Proposed Algorithm
Numerical Results
Conclusion
References
Simulation of Routing in Nano-Manipulation for Creating Pattern with Atomic Force Microscopy Using Hybrid GA and PSO-AS Algorithms
Introduction
View of Dynamic Modeling
GA and PSO Algorithms
Description of Hybrid Algorithms
Simulation in Mathematical
Simulation of Creating Pattern
Conclusions
References
Neural Fuzzy Forecasting of the China Yuan to US Dollar Exchange Rate -- A Swarm Intelligence Approach
Introduction
Methodology of the Proposed Approach
Theory of Neuro-Fuzzy System (NFS)
Clustering for Self-organization of the NFS Predictor
Parameter Learning with PSO-RLSE-PSO Hybrid Learning Method
CNY-USD Exchange Rate Forecasting by the Proposed Approach
Discussion and Conclusion
References
A Hybrid Model for Credit Evaluation Problem
Introduction
RBF Neural Network and Genetic Algorithm
RBF Neural Network
Genetic Algorithm
GA-RBF Neural Network Model
GA-RBF Neural Network Model to Evaluate the Application of Personal Credit
Dataset and Pretreatment
Parameters Setting and Experiment Results
Conclusion
References
Author Index

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