Nature-inspired Optimization Algorithms

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