FOCUS ON SWARM INTELLIGENCE RESEARCH AND APPLICATIONS

FOCUS ON SWARM INTELLIGENCE RESEARCH AND APPLICATIONS

CONTENTS

PREFACE

Chapter 1 ACCURATE IDENTIFICATION OF THE SPECTRAL COMPLEX REFRACTIVE INDEX AND PARTICLE SIZE DISTRIBUTION IN PARTICIPATING MEDIA USING AN ANT COLONY ALGORITHM

ABSTRACT

1. INTRODUCTION

2. METHOD

2.1. Direct Model

2.2. Inverse Model

(1) Basic ACO

(2) Homogenous ACO

(3) PDF-based ACO

3. RESULTS AND DISCUSSION

CONCLUSION

ACKNOWLEDGMENTS

REFERENCES

BIOGRAPHICAL SKETCH

Chapter 2 PARAMETRIC OPTIMIZATION OF LASER BEAM MACHINING PROCESSES USING A SHUFFLED FROG LEAPING ALGORITHM

ABSTRACT

1. INTRODUCTION

2. LITERATURE REVIEW

3. SHUFFLED FROG LEAPING ALGORITHM

3.1. A Brief Description of Shuffled Frog Leaping Algorithm

3.2. Steps Involved in SFLA

3.3. Optimization Parameters in SFLA

4. LASER BEAM MACHINING PROCESS OPTIMIZATION USING SFLA

4.1. Example 1

4.2. Example 2

CONCLUSION

REFERENCES

Chapter3TUNINGOFMAMDANI-TYPEPIDCONTROLLERS:ACONSTRAINEDNONLINEAROPTIMISATIONAPPROACH

Abstract

1.Introduction

2.FuzzyLogicControlSystems

3.Fuzzy-PIDOptimisation

4.CaseStudy

4.1.Three-TankSystem

4.2.Fuzzy-PIDController

4.3.ExperimentalResults

Conclusion

References

Chapter4THEAPPLICATIONOFPARTICLESWARMOPTIMIZATIONFORSOLVINGELECTRICALENGINEERINGPROBLEMS

Abstract

1.Introduction

2.PSOinSolvingElectricalEngineeringProblems

3.FormulationofanOptimizationProblem

4.ParticleSwarmOptimization

4.1.PSOAlgorithm

4.2.PSOFlowchart

4.3.PSOParametersTuning

5.SomeVariantsofPSOandHybridization

5.1.TimeVaringAccelerationFactorsBasedPSO

5.2.ConstrictionFactorPSO

5.3.ChaoticSequencePSO

5.4.SomeHybridPSOAlgorithms

6.TestingPSOinSolvingBenchmarkOptimizationProblems

7.ApplicationofPSOinSolvingElectricalEngineeringProblems

7.1.MathematicalModelling

7.1.1.ModellingofOptimumLoadFlowAnalysis

7.1.2.ModellingofEconomicLoadDispatch

7.1.3.ProtectionCoordinationofDirectionalOvercurrentRelays

7.2.ResultsandDiscussion

7.2.1.ResultsofLFAofa5-busSystem

7.2.2.ResultsofELDProblemofa6-unitSystem

7.2.3.ResultsofProtectionCoordinationofa6-busSystem

8.SummaryandFutureScopeofPSO

Conclusion

References

Chapter 5 THE APPLICATION OF SWARM INTELLIGENCE TECHNIQUES TO ENHANCE THE PERFORMANCE OF THE OPTIMIZATION KERNEL OF ANALOG IC SIZING TOOLS

ABSTRACT

1. INTRODUCTION

2. ADVANCED GSA_PSO

2.1. Particle Swarm Optimization

2.2. Gravitational Search Algorithm

Step 1: Initialization

Step 2: Fitness Evaluation

Step 3: Normalization

Step 4: Gravitational Constant

Step 5: Acceleration

Step 6: Updating

Step 7: Repeat Steps 2 to 6

2.3. The Shrinking Circles Technique and Advanced GSA

2.4. Advanced GSA_PSO

3. AN ANALOG CIRCUIT SIZING TOOL BASED ON ADVANCED GSA_PSO

3.1. Constrained Advanced GSA_PSO

3.2. The Architecture of the Sizing Tool Based on Advanced GSA_PSO

3.3. Case Study: Single Ended Folded-Cascode Op-Amp

4. THE ROBUSTNESS VALIDATION OF THE SIZING TOOL BASED ON ADVANCED GSA_PSO

4.1. Statistical Study Tools

4.2. Corners Analysis

CONCLUSION

REFERENCES

BIOGRAPHICAL SKETCH

Chapter 6 A COMPARATIVE STUDY ON BINARY ARTIFICIAL BEE COLONY OPTIMIZATION METHODS FOR FEATURE SELECTION

ABSTRACT

INTRODUCTION

ARTIFICIAL BEE COLONY

Binary Artificial Bee Colony Optimization

EXPERIMENTAL RESULTS

CONCLUSION

REFERENCES

BIOGRAPHICAL SKETCH

Chapter7ACCELERATINGDIFFERENTIALEVOLUTIONALGORITHMINTHEOPTIMIZATIONOFAMPLIFIERSBYAPPLYINGTHEgm/IDMETHOD

Abstract

1.Introduction

2.DescribingCMOSICsintoSPICE

3.gm/IDDesignMethod

4.DifferentialEvolutionAlgorithm

4.1.ICsSizingCombininggm/IDandDEAlgorithm

5.SizingAnalogICsbyDEAlgorithmandGm/IDDesignMethod

Conclusion

Acknowledgment

References

Chapter 8 A METAHEURISTIC APPROACH FOR URBAN TRAFFIC OPTIMIZATION USING AN ANT COLONY MODEL

ABSTRACT

1. INTRODUCTION

2. ORGANIZATION OF THE CHAPTER

3. STATE OF ART

3.1. Optimization Algorithms

3.1.1. Artificial Neural Networks Approach

3.1.2. Fuzzy Logic Approach

3.1.3. Evolutionary Algorithms Approach

3.1.4. Reinforcement Learning

3.2. Multi-Agent System

3.3. Internet of Things

4. URBAN TRAFFIC OPTIMIZATION MODEL

4.1. Ant Colony Optimization Model

4.1.1. Biological Background

4.1.2. ACO Metaheuristic

4.1.3. Applications of ACO Algorithms

4.1.4. Hybridisation of Metaheuristics

4.2. Hybridization of Metaheursitics with (Meta-)Heuristics

4.3. Hybridizing Metaheuristics with Tree Search

4.4. Hybridizing Metaheuristic with Dynamic Programming

4.5. Hybridization of Metaheuristics with Problem Relaxation

4.6. Hybridizing Metaheuristics with Constraint Programming

4.7. Vehicle Rooting Problem Representation

Communication Architecture Model

CONCLUSION

REFERENCES

Chapter9APSO-PCAOPTIMIZATIONAPPROACHFORCONTROLDESIGN

Abstract

1.Introduction

2.OptimizationApproachesandApplications

3.TheProposedPSO-PCAOptimizationApproach

3.1.ClassicalParticleSwarmOptimization(PSO)Algorithm

3.2.PrincipalComponentsAnalysis(PCA)

3.3.ProposedPSO-PCAApproach

3.4.AdaptiveLQGController

4.SimulationandExperimentalResults

4.1.DTS-200Benchmark

4.2.OperatingParametersandConditions

4.3.AdaptiveSystemIdentificationandAdaptiveControlResults:LinearARXModel

4.4.AdaptiveSystemIdentificationandAdaptiveControlResults:NonlinearNeuralModel

4.5.AdaptiveSystemIdentificationandAdaptiveControlResults:RealDTS-200Process

4.6.ComparisonandPerformanceEvaluation

Conclusion

Acknowledgment

References

Chapter 10 ANT COLONY OPTIMIZATION FOR OPTIMAL ANALOG FILTER SIZING

ABSTRACT

1. INTRODUCTION

2. ANT COLONY OPTIMIZATION: AN OVER VIEW

2.1. Ant System

2.2. Max -Min Ant System

2.3. Ant Colony System

3. APPLICATION TO THE OPTIMAL DESIGN OF ANALOG FILTERS

3.1. Low Pass Butterworth Filter

3.2. Low Pass State Variable Filter

3.3. High-Pass Sallen-Key Filter

3.3.1. The General Sallen-Key High Pass Filter

3.3.2. The Unity Gain Sallen-Key High Pass Filter

4. RESULTS AND COMPARISON

4.1. Butterworth Low Pass Filter Design Results

4.2. State Variable Low Pass Filter Design Results

4.3. General Sallen-Key High Pass Filter Design Results

4.4. The Unity Gain High Pass Sallen-Key Filter

5. COMPARISON

5.1. The Convergence Rate of the Proposed Algorithms

5.2. The Execution Times of the Proposed Algorithms

CONCLUSION

REFERENCES

Chapter 11 OPTIMAL DESIGN OF RF CMOS CIRCUITS BY MEANS OF AN ARTIFICIAL BEE COLONY TECHNIQUE

ABSTRACT

INTRODUCTION

1. ARTIFICIAL BEE COLONY OPTIMIZATION

Step 1 (Initialization)

Step2 (Employed Bees Phase)

Step 3 (Onlooker Bees Phase)

Step 4 (Scout Bees Phase)

Initialize

2. PROPOSED OPTIMIZATION METHOD

3. APPLICATION: DESIGN OF RF CMOS CIRCUITS

3.1 Short-Channel RF CMOS Very Low Noise Amplifier (LNA)

3.1.1 Introduction

3.1.2. Optimization Results

3.1.3. Simulation of RF CMOS LNA Using ADS Software

3.1.4. Conclusion

3.2. Down-Converting CMOS Dual-Gate (DG-Mixer)

3.2.1. Introduction

3.2.2. Theoritical Calculations and Equations of Mixer

3.2.3. Design Requirement and Specifications for Optimization

3.2.4. Results

3.2.5. Conclusion

CONCLUSION

REFERENCES

Chapter 12 SENSITIVITY ANALYSIS IN THE OPTIMIZATION OF ANALOG ACTIVE FILTERS BY APPLYING THE RICHARDSON EXTRAPOLATION

ABSTRACT

INTRODUCTION

SENSITIVITY ANALYSIS BY APPLYING RICHARDSON EXTRAPOLATION

THE SIMULATION-BASED METAHEURISTICS TECHNIQUES

APPLICATION EXAMPLES OF ANALOG ACTIVE FILTERS

A- A Low Pass State Variable Filter

B- Low Pass OTA-C Filter Based on Two OTA LM13700

C- A Low Pass Single Amplifier Biquad

D- A High-Pass Filter Based on a Single CCII AD844

RICHARDSON EXTRAPOLATION-BASED SENSITIVITY ANALYSIS IN THE OPTIMIZATION OF ACTIVE FILTERS

1. State Variable Filter Design Results

2. Low Pass OTA-C Filter Results

3. SAB Filter Design Results

4. High-Pass Filter Based on a Single CCII AD844 Results

CONCLUSION

REFERENCES

BIOGRAPHICAL SKETCHES

EDITOR CONTACT INFORMATION

INDEX

Blank Page