PARTICLE SWARM OPTIMIZATION: THEORY, TECHNIQUES AND APPLICATIONS -- 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 -- Abstract -- 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 -- Abstract -- 1. Introduction -- 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 -- 5. Conclusion -- References -- Chapter 4CONVERGENCE ISSUES IN PARTICLESWARM OPTIMIZATION -- Abstract -- 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 -- References -- Chapter5GLOBALLYCONVERGENTMODIFICATIONSOFPARTICLESWARMOPTIMIZATIONFORUNCONSTRAINEDOPTIMIZATION -- Abstract -- 1.Introduction -- 2.AGeneralizedSchemeforPSO -- 3.IssuesontheParametersAssessmentinPSO -- 4.OurOptimizationFramework -- 5.PreliminaryTheoreticalResults -- 6.NewAlgorithms -- 7.HowtoGenerateSearchDirectionsforGlobalConvergence -- 8.Conclusions -- Acknowledgments -- References -- Chapter6NONLINEAR0-1PROGRAMMINGTHROUGHPARTICLESWARMOPTIMIZATIONUSINGDECODINGALGORITHMS -- 1.Introduction -- 2.Nonlinear0-1ProgrammingProblems -- 3.ParticleSwarmOptimization -- 4.DecodingAlgorithmUsingaReferenceSolutionwithBack-trackingandIndividualModification -- 5.TheProcedureofRevisedPSOUsingtheDecodingAlgorithm -- 6.NumericalExamples -- 7.Conclusion -- References.

Chapter7COMPARATIVESTUDYOFDIFFERENTAPPROACHESTOPARTICLESWARMOPTIMIZATIONINTHEORYANDPRACTICE -- Abstract -- 1.Introduction -- 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 -- References -- Chapter8PARTICLESWARMOPTIMIZATIONFORCOMPUTERGRAPHICSANDGEOME -- Abstract -- 1.Introduction -- 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.FittingaBézierCurve -- 4.2.3.FittingaBézierSurface -- 5.Conclusion -- Acknowledgments -- References -- Chapter9PARTICLESWARMOPTIMIZATIONUSEDFORMECHANISMDESIGNANDGUIDANCEOFSWARMMOBILEROBOTS -- Abstract -- 1.Introduction -- 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 -- 5.Conclusion -- Acknowledgments -- References -- Chapter10ANEWNEIGHBORHOODTOPOLOGYFORTHEPARTICLESWARMOPTIMIZATIONALGORITHM -- Abstract -- 1.Introduction -- 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 -- 5.Conclusion -- References -- Chapter11PSOASSISTEDMULTIUSERDETECTIONFORDS-CDMACOMMUNICATIONSYSTEMS -- Abstract -- 1.Introduction -- 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 -- 7.Conclusion -- Appendix.

A.MinimalNumberofTrialsandSingle-UserPerformance -- B.MonteCarloSimulationSetup -- References -- INDEX -- Blank Page.