Particle swarm optimization : theory, techniques and applications / Andrea E. Olsson, editor.

Format:

Book

Contributor:

Olsson, Andrea E.

Series:

Engineering tools, techniques and tables.

Engineering tools, techniques and tables

Language:

English

Subjects (All):

Swarm intelligence.

Mathematical optimization.

Physical Description:

1 online resource (319 p.)

Edition:

1st ed.

Place of Publication:

Hauppauge, N.Y. : Nova Science Publishers, c2011.

Language Note:

English

Summary:

Particle swarm optimisation (PSO) is an algorithm modelled on swarm intelligence that finds a solution to an optimisation problem in a search space or model & predicts social behaviour in the presence of objectives. This book presents information on particle swarm optimisation.

Contents:

Intro

PARTICLE SWARM OPTIMIZATION: THEORY, TECHNIQUES AND APPLICATIONS

CONTENTS

PREFACE

Chapter 1USING MONO-OBJECTIVE AND MULTI-OBJECTIVEPARTICLE SWARM OPTIMIZATION FOR THE TUNINGOF PROCESS CONTROL LAWS

Abstract

I. Introduction

II. Choice for the Use of Particle Swarm Optimization

III. Mono-objective PSO for the Design of Control Laws

III.1. Definition of Optimization Problems

III.2. Tuning of PID Controllers

III.3. Reduced Order H∞ Control

III.3.1. H∞ Control

III.3.2. Full Order H∞ Synthesis

III.3.3. Reduced Order H∞ Synthesis

III.3.4. Case Study

III.3.5. One Output H∞ Synthesis

III.3.6. Three Output H∞ Synthesis

III.4. Non Linear Predictive Control

IV. Multi-objective PSO for the Design of Controllers

V. Conclusions

References

Chapter 2STUDY ON VEHICLE ROUTING PROBLEM WITH TIMEWINDOWS BASED ON ENHANCED PARTICLESWARM OPTIMIZATION APPROACH

1. Introduction

2. Problem Solving Methodology

2.1. The Basic Particle Swarm Optimization

2.2. The Vehicle Routing Problem with Time Windows Problem

2.3. The Difficulty for Using Basic PSO to Solve the VRPTW

2.4. The New Solution Strategies of Predicting Particle Swarm Optimization

Strategy I: Search Space Transformation

Strategy II: Boundary Constraint Handling

Strategy III: Forward Predicting Update on Velocity Update Equation

3. The Algorithm of Predicting PSO

Phase I: Candidate Customer Search in Initial Stage

Phase II: Global Initial Solution Generated Through Feasibility Test

Phase III. Population Search for Optimization

4. Computational Experiment

4.1. Problem Data

4.2. Computational Results

5. Conclusion

Reference.

Chapter 3RELIABILITY OPTIMIZATION PROBLEMS USINGADAPTIVE GENETIC ALGORITHM AND IMPROVEDPARTICLE SWARM OPTIMIZATION

2. Reliability Optimization Problems

3. Hybrid Approach Using iGA and iPSO

3.1. iGA Design

3.2. iPSO Design

3.3. Hybrid Approach

3.4. Overall Procedure

4. Numerical Examples

4.1. Test Problems

4.1.1. Test Problem 1 (T-1)

1) Case 1

2) Case 2

3) Case 3

4.1.2. Test Problem 2 (T-2)

4.2. Test Results

Chapter 4CONVERGENCE ISSUES IN PARTICLESWARM OPTIMIZATION

Introduction

Managing Population and Exploration

Managing Velocity and Search Region

Managing Optima and Resuming Exploration

Convergence in Optimization Algorithms Otherthan PSO - Overview

Convergence Analysis and Discussion

Terminology

Convergence as a Stopping Condtion

Conclusion

Convergence Methods

Guidelines for Convergence

MAV Convergence/Stopping Point Pseudo-Code

Unimodal Problems

Multimodal Problems

Epilogue

Chapter5GLOBALLYCONVERGENTMODIFICATIONSOFPARTICLESWARMOPTIMIZATIONFORUNCONSTRAINEDOPTIMIZATION

1.Introduction

2.AGeneralizedSchemeforPSO

3.IssuesontheParametersAssessmentinPSO

4.OurOptimizationFramework

5.PreliminaryTheoreticalResults

6.NewAlgorithms

7.HowtoGenerateSearchDirectionsforGlobalConvergence

8.Conclusions

Acknowledgments

Chapter6NONLINEAR0-1PROGRAMMINGTHROUGHPARTICLESWARMOPTIMIZATIONUSINGDECODINGALGORITHMS

2.Nonlinear0-1ProgrammingProblems

3.ParticleSwarmOptimization

4.DecodingAlgorithmUsingaReferenceSolutionwithBack-trackingandIndividualModification

5.TheProcedureofRevisedPSOUsingtheDecodingAlgorithm

6.NumericalExamples

7.Conclusion

References.

Chapter7COMPARATIVESTUDYOFDIFFERENTAPPROACHESTOPARTICLESWARMOPTIMIZATIONINTHEORYANDPRACTICE

2.TheParticleSwarmOptimizationApproach

2.1.TheAlgorithm

2.2.MoveClassesforParticleSwarmOptimization

2.2.1.Variant1

2.2.2.Variant2

2.2.3.Variant3

2.2.4.Variant4

2.3.Heuristics

2.4.BoundaryConditions

3.PerformanceComparisonofParticleSwarmOptimizationApproaches

3.1.ContinuousBenchmarkFunctions

3.2.ParameterSetup

3.3.PerformanceComparisonforContinuousTestFunctions

3.4.InvestigationoftheConvergenceBehavior

3.5.InfluenceofHeuristicsandBoundaryConditions

4.ComparativeAnalysistoAlternativeMethods

4.1.GlobalOptimizationHeuristics

4.2.SingleStateMethods

4.3.TemperatureParameter

4.4.MoveClassesfortheContinuousDomain

4.5.EvolutionaryAlgorithms

4.6.ExperimentalComparison

5.Simulation-basedOptimizationofaHubandSpokeInventorySystem

6.Conclusion

Chapter8PARTICLESWARMOPTIMIZATIONFORCOMPUTERGRAPHICSANDGEOME

2.ParticleSwarmOptimization

3.ApplicationsofPSOtoComputerGraphics

3.1.ArtificialLife

3.2.RealisticSimulationofVirtualCrowds

3.3.HumanBodyPoseEstimationwithPSO

4.ApplicationsofPSOtoGeometricModeling

4.1.GeometricConstraintSolving

4.2.CurveandSurfaceFitting

4.2.1.BestLeast-SquaresApproximation

4.2.2.FittingaB´ezierCurve

4.2.3.FittingaB´ezierSurface

5.Conclusion

Chapter9PARTICLESWARMOPTIMIZATIONUSEDFORMECHANISMDESIGNANDGUIDANCEOFSWARMMOBILEROBOTS

2.AlgorithmforConstrainedEngineeringProblems

2.1.GeneralMethodsfortheConstrainedOptimizationProblem

2.2.ExtendingtheBasicPSOtoALPSOforEfficientConstraintHandling

3.PSOBasedAlgorithmUsedforMechanismDesign

3.1.MechanismDesignandOptimization

3.2.OptimizationDesignoftheHEXACT.

4.PSOBasedAlgorithmUsedforGuidanceofSwarmMobileRobots

4.1.BackgroundofRobotNavigation

4.2.MechanicalPSOModelofWarmMobileRobots

4.3.ModificationoftheNeighborhood

4.4.ExtensionoftheBasicPSOAlgorithmtoVL-ALPSOforCoordinatedMovementsofSwarmMobileRobots

4.4.1.SwarmParticleRobots

4.4.2.VolumeConstrainedSwarmRobots

4.5.SimulationandResults

4.5.1.ObjectiveFunctionandConstraints

4.5.2.ExperimentalSetup

4.5.3.ResultsandDiscussions

Chapter10ANEWNEIGHBORHOODTOPOLOGYFORTHEPARTICLESWARMOPTIMIZATIONALGORITHM

2.NeighborhoodStructures

3.TheSingly-LinkedRing

4.Experiments

4.1.ControledTests

4.1.1.TestI

4.1.2.TestII

4.2.GlobalOptimizationBenchmark

4.3.Parameters

4.4.Results

Chapter11PSOASSISTEDMULTIUSERDETECTIONFORDS-CDMACOMMUNICATIONSYSTEMS

2.SystemModel

2.1.DS-CDMA

2.2.OptimumDetection

2.3.MultiuserDetection:AHeuristicPerspective

2.4.WeightingMulti-objectiveOptimization

3.PSOMultiuserDetectors

3.1.DiscreteSwarmOptimizationAlgorithm

3.2.WOQ-LLFSelectionforSIMOPSO-MUD

4.PSO-MUDParametersOptimization

4.1.VmaxOptimization

4.2.°1and°2Optimization

4.3.!Optimization

4.4.°1and°2OptimizationunderHigh-orderModulation

4.5.OptimizationforSystemswithDiversityExploration

4.6.OptimizedParametersforPSO-MUD

5.NumericalResults

5.1.AWGNChannels

5.2.RayleighChannels

5.2.1.PathDiversity

5.2.2.SpatialDiversity

5.2.3.High-orderModulation

6.ComplexityAnalysis

6.1.AnalyticalComplexity

6.1.1.OMUDComplexity

6.1.2.PSO-MUDComplexity

6.2.NumericalComplexity

6.2.1.AWGNSynchronousChannel

6.2.2.FlatRayleighChannel

6.2.3.PathDiversity

6.2.4.SpatialDiversity

6.2.5.ModulationOrder

Appendix.

A.MinimalNumberofTrialsandSingle-UserPerformance

B.MonteCarloSimulationSetup

INDEX

Blank Page.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record and CIP data provided by publisher.

ISBN:

1-61324-700-1

OCLC:

830627788