Nature-Inspired Algorithms For Engineers and Scientists

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Cover Half Title Title Copyright Contents Preface Acknowledgments About the Author Chapter 1 Introduction 1.1 Introduction 1.2 Optimization Problems 1.2.1 Classification of Optimization Problems 1.2.1.1 Classification Based on the Number of Points in the Search Space 1.2.1.2 Classification Based on the Number of Objective Functions 1.2.1.3 Classification Based on the Availability of Objective Functions 1.2.1.4 Classification Based on the Number of Global Optimal Solutions 1.3 Methods for Solving Optimization Problems 1.3.1 Mathematical Methods 1.3.2 Programming Methods 1.3.2.1 Deterministic Algorithm 1.3.2.2 Randomized Algorithms Chapter 2 Binary Genetic Algorithms 2.1 Introduction 2.2 GAs 2.2.1 Some Basic Requirements for Designing Optimization Algorithms 2.2.2 Solving Optimization Problems Using a GA 2.2.3 Encoding Techniques Used in GAs 2.3 Detailed Implementation of Binary GAs 2.3.1 Types of Selection Operators 2.3.1.1 Fitness Proportionate Selection/Roulette Wheel Selection 2.3.1.2 Tournament Selection 2.3.1.3 Rank Selection 2.3.1.4 Sharing Fitness Method 2.3.2 Types of Crossover Operator 2.3.2.1 Single-Point Crossover 2.3.2.2 Two-Point Crossover 2.3.2.3 Uniform Crossover 2.3.2.4 Arithmetic Crossover 2.3.3 Types of Mutation Operator 2.3.3.1 One-Bit Mutation 2.3.3.2 Multibit Mutation 2.4 Theoretical Foundation of GAs (Schemata Theory) 2.4.1 Effect of Selection on a Schema 2.4.2 Effect of Crossover on a Schema 2.4.3 Effect of Mutation on the Survival of a Schema 2.5 MATLAB® Program for a GA 2.5.1 Writing Program for a Binary GA 2.5.1.1 Generating the Initial Population 2.5.1.2 Implementing Selection Operator in a BGA Using MATLAB 2.5.1.3 Implementing One-Point Crossover in MATLAB 2.5.1.4 Implementing Mutation Operator in MATLAB Chapter 3 Real-Parameter Genetic Algorithm 3.1 Introduction 3.1.1 Problems with Binary Encoding 3.1.1.1 Poor Coverage of Search Space 3.1.1.2 More Computational Requirements 3.1.1.3 Longer Computational Time and Lower Accuracy 3.1.1.4 Hamming Cliff Problem with Binary Encoding 3.2 Real-Parameter GA 3.2.1 Detailed Implementation of a Real-Parameter GA 3.2.2 Operators of a Real-Parameter GA 3.2.2.1 Selection Operator 3.2.2.2 Crossover Operator 3.2.2.3 Type of Mutation Operator 3.3 MATLAB® Program for a Real-Parameter GA 3.3.1 Generating the Initial Population 3.3.2 Implementing Selection Operator in Real-Parameter GA using MATLAB 3.3.3 Implementing Crossover in a Real-Parameter GA using MATLAB 3.3.3.1 Implementing a Crossover Operator 3.3.4 Mutation Operation Chapter 4 Differential Evolution 4.1 Introduction 4.2 DE Algorithms 4.2.1 Mutation Operator 4.2.2 Crossover Operator 4.2.3 Selection Operator 4.2.4 Control Parameters 4.3 More on DE Algorithms 4.4 MATLAB® Implementation of a DE Algorithm 4.4.1 Defining the Initial Population 4.4.2 Updating Solutions by Using Mutation and a Crossover Operator 4.4.3 Selecting the Population for the Next Iteration Chapter 5 Particle Swarm Optimization 5.1 Introduction 5.2 PSO Algorithms 5.2.1 Natural Phenomena Used for Designing the Algorithm 5.2.2 Understanding the Relevance of Phenomena with Optimization 5.3 Detailed Implementation of a PSO Program 5.3.1 Defining Velocities and Positions of Particles 5.3.2 Calculating gbest and pbest 5.3.3 Clamping Velocity and the Position of Each Particle 5.3.4 Termination PSO Program 5.3.5 Parameter Tuning 5.3.6 Pseudocode of a PSO Algorithm 5.4 MATLAB® Implementation of a PSO Program 5.4.1 Population Initialization 5.4.2 Calculating gbest and pbest 5.4.3 Updating Velocity and Position Vectors 5.4.4 Clamping Velocity and Position Vectors 5.5 Improving the Performance of a PSO Algorithm 5.6 Real-Life Applications of a PSO Algorithm Chapter 6 Grey Wolf Optimization 6.1 Introduction 6.2 GWO Algorithms 6.2.1 Natural Phenomena Used for Designing the Algorithm 6.2.2 Understanding the Relevance of Phenomena with Optimization 6.3 Detailed Implementation of a GWO Program 6.3.1 Generating Initial Population 6.3.2 Identification of Alpha, Beta, Delta, and Omega Solutions 6.3.3 Updating Position of Each Wolf and Producing Output 6.3.4 Clamping 6.3.5 Parameter Tuning 6.4 MATLAB® Implementation of a GWO Program 6.4.1 Defining the Initial Population 6.4.2 Updating Alpha, Beta, and Delta Solutions Chapter 7 Environmental Adaptation Method 7.1 Introduction 7.2 EAM 7.2.1 Natural Phenomena Used for Designing EAM 7.3 Detailed Description of an EAM Program 7.3.1 Binary EAM 7.3.1.1 Adaptation Operator 7.3.1.2 Alteration Operator 7.3.1.3 Selection Operator 7.3.1.4 Explaining EAM with One Numerical Example 7.3.2 Understanding the Relevance of Phenomena with Optimization 7.3.3 Real-Parameter EAM 7.4 Improved Environmental Adaptation Method 7.4.1 Binary Version of IEAM 7.4.2 Real-Parameter IEAM 7.5 MATLAB® Implementation of EAM and IEAM 7.5.1 Generating Binary Solutions 7.5.2 Solutions Generated after the Adaption Operator 7.5.3 Selection Operator in IEAM 7.6 Writing MATLAB Program for a Real-Parameter Version of IEAM 7.6.1 Defining Initial Population 7.6.2 Adaption Operator 7.6.3 Selection Operator Chapter 8 Other Important Optimization Algorithms 8.1 Methods for Creating an Optimization Algorithm 8.1.1 Mathematical Methods 8.1.1.1 Gradient-Based Methods 8.1.1.2 Derivation-Free Method 8.1.2 Search-Based Methods 8.1.2.1 Heuristic Search-Based Method 8.1.2.2 Metaheuristic Search-Based Method 8.1.3 Special Search-Based Methods (Nature-Inspired Algorithms) 8.1.3.1 Point-to-Point-Based Algorithm 8.1.3.2 Population-Based Algorithms Chapter 9 Application of Genetic Algorithms, Partial Swarm Optimization, and Differential Evolution in Software Testing 9.1 Introduction 9.2 Introduction of Software Testing 9.3 Test Case Generation Techniques 9.3.1 Random Test Data Generation Technique 9.3.2 Symbolic Test Data Generation Technique 9.3.3 Dynamic Test Data Generation Technique 9.3.4 Metaheuristic Techniques 9.3.5 Test Case Coverage Tools 9.3.5.1 Gcov 9.3.5.2 Trucov 9.4 Test Case Generation Using a GA for White-Box Testing 9.4.1 Test Suite Generator 9.4.2 Initiator 9.4.3 Test Case Generator Module 9.4.4 Code Coverage Checker 9.4.5 The Triangle Program 9.4.6 Initial Population Generation for a GA 9.4.7 Selection Operator 9.4.8 Crossover Operator 9.4.9 Mutation Operator 9.4.10 Generation of a New Population by a GA 9.5 Application of PSO in Test Case Generation 9.5.1 Particle Initialization in PSO 9.5.1.1 Rule for Updating 9.6 Application of DE in Test Case Generation 9.6.1 Initialization Population Generation in DE 9.6.2 Mutation 9.6.3 Crossover 9.6.4 Selection Operator 9.7 Conclusion Chapter 10 Application of Genetic Algorithms, Partial Swarm Optimization, and Differential Evolution in Regression Testing 10.1 Introduction 10.2 Regression Testing 10.2.1 Test Suite Minimization Problem 10.2.2 Test Case Selection 10.2.2.1 Test Case Selection Problem 10.2.3 Test Case Prioritization 10.2.3.1 Test Case Prioritization Problem 10.2.4 Application of a Genetic Algorithm in Test Suite Minimization 10.2.4.1 Minimization of Pre-Prioritized Test Suite Using a GA 10.2.5 Example Program 10.2.5.1 Initial Population Generation for a GA 10.2.5.2 Selection for a GA 10.2.6 Crossover for a GA 10.2.7 Mutation for a GA 10.2.8 Generation of a New Population by a GA 10.3 Test Case Generation for Modified Code Using PSO-TVAC 10.3.1 Particle Initialization in PSO-TVAC 10.3.2 Particle Update Rule 10.4 Test Case Generation for Modified Code Using DE 10.4.1 Population Initialization 10.4.2 Update Rule 10.4.3 Mutation 10.4.4 Crossover 10.4.5 Selection Operator Chapter 11 Application of Genetic Algorithms and Partial Swarm Optimization in Cloud Computing 11.1 Introduction 11.2 Cloud Computing 11.2.1 Types of Cloud 11.2.1.1 IaaS 11.2.1.2 PaaS 11.2.1.3 SaaS 11.3 Workflow and Its Scheduling 11.3.1 How to Choose an Appropriate Cloud Provider for Running Workflows 11.3.2 Workflow Scheduling Problem Formulation 11.3.3 Generating Optimal Workflow with the Help of Partial Swarm Optimization 11.3.4 Fitness Function for Workflow Scheduling 11.4 Application of Partial Swarm Optimization for Generating Optimal Workflow Schedule 11.4.1 An Example Application 11.5 Application of a Binary Genetic Algorithm in Workflow Scheduling Problem 11.5.1 Binary Encoding Used for Representing the Workflow Schedule References and Further Reading Questions Answers Index