Nature Inspired Optimization Algorithms

Description

Nature Inspired Optimization Algorithms is a comprehensive book on the most popular optimization algorithms that are based on nature. It starts with an overview of optimization and goes from the classical to the latest swarm intelligence algorithm. Nature has a rich abundance of flora and fauna that inspired the development of nature inspired optimization techniques. The study of the intelligent survival strategies of animals, birds and insects in a hostile and ever-changing environment has led to the development of techniques emulating their behaviour. Nature provides us with simple solutions to complex problems in an effective and adaptive manner. This book is a valuable resource for engineers, researchers, faculty and students who are devising optimum solutions to any type of problem. The problems range from computer science to economics covering diverse areas that require maximizing output and minimizing resources and this is the crux of all optimization algorithms. The book is a lucid description of fifteen of the existing important optimization algorithms that are based on swarm intelligence and superior in performance. Features: Detailed description of the algorithms along with pseudocode and flowchart Easily translatable to program code that is also readily available in Mathworks website for some of the algorithms Simple examples to demonstrate the optimization strategies have been given wherever possible that makes understanding easier Standard applications and benchmark datasets for testing and validating the algorithms have been enumerated This book is a reference for under-graduate and post-graduate students. It will be useful to faculty members teaching the subject on optimization. It also a comprehensive guide for researchers who are looking for optimizing resources in attaining the best solution to a problem. The nature inspired optimization algorithms are unconventional and this makes them more efficient than their traditional counterparts. Cover Half Title #2,0,-32767Title Page #4,0,-32767Copyright Page #5,0,-32767Table of Contents #6,0,-32767Preface #12,0,-32767Author 1 Introduction 1.1 Introduction 1.2 Fundamentals of Optimization 1.3 Types of Optimization Problems 1.4 Examples of Optimization 1.5 Formulation of Optimization Problem 1.6 Classification of Optimization Algorithms 1.7 Traveling Salesman Problem and Knapsack Problem 1.8 Summary 2 Classical Optimization Methods 2.1 Introduction 2.2 Mathematical Model of Optimization 2.3 Linear Programming 2.3.1 Simplex Method 2.3.2 Revised Simplex Method 2.3.3 Kamarkar’s Method 2.3.4 Duality Theorem 2.3.5 Decomposition Principle 2.3.6 Transportation Problem 2.4 Non-Linear Programming 2.4.1 Quadratic Programming 2.4.2 Geometric Programming 2.5 Dynamic Programming 2.6 Integer Programming 2.7 Stochastic Programming 2.8 Lagrange Multiplier Method 2.9 Summary References 3 Nature-Inspired Algorithms 3.1 Introduction 3.2 Traditional versus Nature-Inspired Algorithms 3.3 Bioinspired Algorithms 3.4 Swarm Intelligence 3.5 Metaheuristics 3.6 Diversification and Intensification 3.7 No Free Lunch Theorem 3.8 Parameter Tuning and Control 3.9 Algorithm 3.10 Pseudocode 3.11 Summary References 4 Genetic Algorithm 4.1 Introduction 4.2 Basics of Genetic Algorithm 4.3 Genetic Operators 4.4 Example of GA 4.5 Algorithm 4.6 Pseudocode 4.7 Schema Theory 4.8 Prisoner’s Dilemma Problem 4.9 Variants and Hybrids of GA 4.10 Summary References 5 Genetic Programming 5.1 Introduction 5.2 Basics of Genetic Programming 5.3 Data Structures for Genetic Programming 5.4 Binary Tree Traversals 5.5 Genetic Programming Operators 5.6 Genetic Programming Algorithm 5.7 Pseudocode 5.8 Variants of the Algorithm 5.9 Summary References 6 Particle Swarm Optimization 6.1 Introduction 6.2 Swarm Behavior 6.3 Particle Swarm Optimization 6.3.1 Algorithm 6.3.2 Pseudocode 6.4 Variants of the Algorithm 6.5 Summary References 7 Differential Evolution 7.1 Introduction 7.2 Differential Evolution 7.2.1 Algorithm 7.2.2 Pseudocode 7.3 Variants of the Algorithm 7.4 Summary References 8 Ant Colony Optimization 8.1 Introduction 8.2 Ant Colony Characteristics 8.3 Ant Colony Optimization 8.3.1 Traveling Salesman Problem 8.3.2 Algorithm 8.3.3 Pseudocode 8.4 Variants of the Algorithm 8.5 Summary References 9 Bee Colony Optimization 9.1 Introduction 9.2 Honey Bee Characteristics 9.3 Bee Colony Optimization 9.3.1 Algorithm 9.3.2 Pseudocode 9.4 Variants of the Algorithm 9.5 Summary References 10 Fish School Search Algorithm 10.1 Introduction 10.2 Fish School Behavior 10.3 Fish School Search Optimization 10.3.1 Algorithm 10.3.2 Pseudocode 10.4 Variants and Applications 10.5 Summary References 11 Cuckoo Search Algorithm 11.1 Introduction 11.2 Cuckoo Bird Behavior 11.3 Levy Flights 11.4 Cuckoo Search Optimization 11.4.1 Algorithm 11.4.2 Pseudocode 11.5 Variants of the Algorithm 11.5.1 Discrete Cuckoo Search Algorithm 11.5.2 Binary Cuckoo Search (BCS) Algorithm 11.5.3 Multi-Objective Cuckoo Search Algorithm (MOCS) 11.6 Summary References 12 Firefly Algorithm 12.1 Introduction 12.2 Firefly Behavior and Characteristics 12.3 Firefly-Inspired Optimization 12.3.1 Algorithm 12.3.2 Pseudocode 12.4 Variants and Applications 12.5 Summary References 13 Bat Algorithm 13.1 Introduction 13.2 Behavior of Bats in Nature 13.3 Bat Optimization Algorithm 13.3.1 Algorithm 13.3.2 Pseudocode 13.4 Variants and Applications 13.5 Summary References 14 Flower Pollination Algorithm 14.1 Introduction 14.2 Flower Pollination 14.3 Flower Pollination Optimization 14.3.1 Algorithm 14.3.2 Pseudocode 14.4 Variants of the Algorithm 14.5 Summary References 15 Gray Wolf Optimization 15.1 Introduction 15.2 Gray Wolf Characteristics 15.3 Gray Wolf Optimization 15.3.1 Gray Wolf Encircling Prey 15.3.2 Hunting Behavior of Gray Wolves 15.3.3 Attacking of Prey by Gray Wolves 15.3.4 Gray Wolves Searching for Prey (Exploration) 15.4 Variants and Applications 15.5 Summary References 16 Elephant Herding Optimization 16.1 Introduction 16.2 Elephant Herding Behavior 16.3 Elephant Herding Optimization 16.3.1 Algorithm 16.3.2 Pseudocode 16.4 Variants of the Algorithm 16.5 Summary References 17 Crow Search Algorithm 17.1 Introduction 17.2 Crows in Nature 17.3 Crow Search Optimization 17.3.1 Algorithm 17.3.2 Pseudocode 17.4 Variants and Applications 17.5 Summary References 18 Raven Roosting Optimization Algorithm 18.1 Introduction 18.2 Raven Roosting Behavior 18.3 Raven Roosting Optimization 18.3.1 Algorithm 18.3.2 Pseudocode Flowchart 18.4 Variants of the Algorithm 18.5 Summary References 19 Applications 19.1 Introduction 19.2 Benchmark Test Functions 19.3 Applications 19.3.1 Traveling Salesman Problem 19.3.2 Knapsack Problem 19.3.3 Graph Coloring Problem 19.3.4 Job Scheduling Problem 19.3.5 Feature Reduction Problem 19.3.6 Network Routing Problem 19.4 Summary 20 Conclusion