Computational Intelligence-based Optimization Algorithms : From Theory to Practice

Computational intelligence-based optimization methods, also known as metaheuristic optimization algorithms, are a popular topic in mathematical programming. These methods have bridged the gap between various approaches and created a new school of thought to solve real-world optimization problems. In this book, we have selected some of the most effective and renowned algorithms in the literature. These algorithms are not only practical but also provide thought-provoking theoretical ideas to help readers understand how they solve optimization problems. Each chapter includes a brief review of the algorithm’s background and the fields it has been used in. Additionally, Python code is provided for all algorithms at the end of each chapter, making this book a valuable resource for beginner and intermediate programmers looking to understand these algorithms. Cover Half Title Title Page Copyright Page Table of Contents List of Figures Foreword Preface 1 An Introduction to Meta-Heuristic Optimization 1.1 Introduction 1.2 Components of an Optimization Problem 1.2.1 Objective Function 1.2.2 Decision Variables 1.2.3 State Variables 1.2.4 Constraints 1.2.5 Search Space 1.2.6 Simulator 1.2.7 Local and Global Optima 1.2.8 Near-Optimal Solutions 1.3 The General Theme of Meta-Heuristic Algorithms 1.4 Different Types of Meta-Heuristic Algorithms 1.4.1 Source of Inspiration 1.4.2 Number of Search Agents 1.4.3 Implementation of Memory 1.4.4 Compatibility With the Search Space’s Landscape 1.4.5 Random Components 1.4.6 Preserving Algorithms 1.5 Handling Constraints 1.6 Performance of a Meta-Heuristic Algorithm 1.7 No-Free-Lunch Theorem 1.8 Concluding Remarks References 2 Pattern Search Algorithm 2.1 Introduction 2.2 Algorithmic Structure of Pattern Search Algorithm 2.2.1 Initiation Stage 2.2.2 Searching Stage 2.2.2.1 Exploratory Move 2.2.2.2 Pattern Move 2.2.3 Termination Stage 2.3 Parameter Selection and Fine-Tuning the Pattern Search Algorithm 2.4 Python Codes 2.5 Concluding Remarks References 3 Genetic Algorithm 3.1 Introduction 3.2 Algorithmic Structure of the Genetic Algorithm 3.2.1 Initiation Stage 3.2.2 Reproduction Stage 3.2.2.1 Selection Operators 3.2.2.2 Crossover Operators 3.2.2.3 Mutation Operators 3.2.3 Termination Stage 3.3 Parameter Selection and Fine-Tuning of the Genetic Algorithm 3.4 Python Codes 3.5 Concluding Remarks References 4 Simulated Annealing Algorithm 4.1 Introduction 4.2 Algorithmic Structure of Simulated Annealing Algorithm 4.2.1 Initiation Stage 4.2.2 Searching Stage 4.2.3 Termination Stage 4.3 Parameter Selection and Fine-Tuning the Simulated Annealing Algorithm 4.4 Python Codes 4.5 Concluding Remarks References 5 Tabu Search Algorithm 5.1 Introduction 5.2 Algorithmic Structure of the Tabu Search Algorithm 5.2.1 Initiation Stage 5.2.2 Searching Stage 5.2.3 Termination Stage 5.3 Parameter Selection and Fine-Tuning the Tabu Search Algorithm 5.4 Python Codes 5.5 Concluding Remarks References 6 Ant Colony Optimization Algorithm 6.1 Introduction 6.2 Algorithmic Structure of the Ant Colony Optimization Algorithm 6.2.1 Initiation Stage 6.2.2 Foraging Stage 6.2.3 Termination Stage 6.3 Parameter Selection and Fine-Tuning the Ant Colony Optimization Algorithm 6.4 Python Codes 6.5 Concluding Remarks References 7 Particle Swarm Optimization Algorithm 7.1 Introduction 7.2 Algorithmic Structure of the Particle Swarm Optimization Algorithm 7.2.1 Initiation Stage 7.2.2 Searching Stage 7.2.3 Termination Stage 7.3 Parameter Selection and Fine-Tuning the Particle Swarm Optimization Algorithm 7.4 Python Codes 7.5 Concluding Remarks References 8 Differential Evolution Algorithm 8.1 Introduction 8.2 Algorithmic Structure of the Differential Evolution Algorithm 8.2.1 Initiation Stage 8.2.2 Reproduction Stage 8.2.2.1 Mutation Operator 8.2.2.2 Crossover Operator 8.2.3 Termination Stage 8.3 Parameter Selection and Fine-Tuning Differential Evolution Algorithm 8.4 Python Codes 8.5 Concluding Remarks References 9 Harmony Search Algorithm 9.1 Introduction 9.2 Algorithmic Structure of the Harmony Search Algorithm 9.2.1 Initiation Stage 9.2.2 Composing Stage 9.2.2.1 Memory Strategy 9.2.2.2 Randomization Strategy 9.2.2.3 Pitch Adjustment Strategy 9.2.3 Termination Stage 9.3 Parameter Selection and Fine-Tuning the Harmony Search Algorithm 9.4 Python Codes 9.5 Concluding Remarks References 10 Shuffled Frog-Leaping Algorithm 10.1 Introduction 10.2 Algorithmic Structure of the Shuffled Frog-Leaping Algorithm 10.2.1 Initiation Stage 10.2.2 Foraging Stage 10.2.3 Termination Stage 10.3 Parameter Selection and Fine-Tuning the Shuffled Frog-Leaping Algorithm 10.4 Python Codes 10.5 Concluding Remarks References 11 Invasive Weed Optimization Algorithm 11.1 Introduction 11.2 Algorithmic Structure of the Invasive Weed Optimization Algorithm 11.2.1 Initiation Stage 11.2.2 Invasion Stage 11.2.3 Termination Stage 11.3 Parameter Selection and Fine-Tuning the Invasive Weed Optimization Algorithm 11.4 Python Codes 11.5 Concluding Remarks References 12 Biogeography-Based Optimization Algorithm 12.1 Introduction 12.2 Algorithmic Structure of the Biogeography-Based Optimization Algorithm 12.2.1 Initiation Stage 12.2.2 Migration Stage 12.2.3 Termination Stage 12.3 Parameter Selection and Fine-Tuning the Biogeography-Based Optimization Algorithm 12.4 Python Codes 12.5 Concluding Remarks References 13 Cuckoo Search Algorithm 13.1 Introduction 13.2 Algorithmic Structure of the Cuckoo Search Algorithm 13.2.1 Initiation Stage 13.2.2 Brood Parasitism Stage 13.2.3 Termination Stage 13.3 Parameter Selection and Fine-Tuning the Cuckoo Search Algorithm 13.4 Python Codes 13.5 Concluding Remarks References 14 Firefly Algorithm 14.1 Introduction 14.2 Algorithmic Structure of the Firefly Algorithm 14.2.1 Initiation Stage 14.2.2 Mating Stage 14.2.3 Termination Stage 14.3 Parameter Selection and Fine-Tuning the Firefly Algorithm 14.4 Python Codes 14.5 Concluding Remarks References 15 Gravitational Search Algorithm 15.1 Introduction 15.2 Algorithmic Structure of the Gravitational Search Algorithm 15.2.1 Initiation Stage 15.2.2 Repositioning Stage 15.2.3 Termination Stage 15.3 Parameter Selection and Fine-Tuning the Gravitational Search Algorithm 15.4 Python Codes 15.5 Concluding Remarks References 16 Plant Propagation Algorithm 16.1 Introduction 16.2 Algorithmic Structure of the Plant Propagation Algorithm 16.2.1 Initiation Stage 16.2.2 Propagation Stage 16.2.3 Termination Stage 16.3 Parameter Selection and Fine-Tuning the Plant Propagation Algorithm 16.4 Python Codes 16.5 Concluding Remarks References 17 Teaching-Learning-Based Optimization Algorithm 17.1 Introduction 17.2 Algorithmic Structure of the Teaching-Learning-Based Optimization Algorithm 17.2.1 Initiation Stage 17.2.2 Teaching/Learning Stage 17.2.3 Termination Stage 17.3 Parameter Selection and Fine-Tuning the Teaching-Learning-Based Optimization Algorithm 17.4 Python Codes 17.5 Concluding Remarks References 18 Bat Algorithm 18.1 Introduction 18.2 Algorithmic Structure of the Bat Algorithm 18.2.1 Initiation Stage 18.2.2 Repositioning Stage 18.2.3 Termination Stage 18.3 Parameter Selection and Fine-Tuning the Bat Algorithm 18.4 Python Codes 18.5 Concluding Remarks Notes References 19 Flower Pollination Algorithm 19.1 Introduction 19.2 Algorithmic Structure of the Flower Pollination Algorithm 19.2.1 Initiation Stage 19.2.2 Pollination Stage 19.2.3 Termination Stage 19.3 Parameter Selection and Fine-Tuning the Flower Pollination Algorithm 19.4 Python Codes 19.5 Concluding Remarks References 20 Water Cycle Algorithm 20.1 Introduction 20.2 Algorithmic Structure of the Water Cycle Algorithm 20.2.1 Initiation Stage 20.2.2 Hydrological Simulation Stage 20.2.3 Termination Stage 20.3 Parameter Selection and Fine-Tuning the Water Cycle Algorithm 20.4 Python Codes 20.5 Concluding Remarks Note References 21 Symbiotic Organisms Search Algorithm 21.1 Introduction 21.2 Algorithmic Structure of the Symbiotic Organisms Search Algorithm 21.2.1 Initiation Stage 21.2.2 Symbiosis Stage 21.2.2.1 Mutualism Operator 21.2.2.2 Commensalism Operator 21.2.2.3 Parasitism Operator 21.2.3 Termination Stage 21.3 Parameter Selection and Fine-Tuning the Symbiotic Organisms Search Algorithm 21.4 Python Codes 21.5 Concluding Remarks References Index