About the Book:
Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book-s unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning and control, as well as multi-objective optimization. This book can serve as an introductory book for graduates, doctoral students and lecturers in computer science, engineering and natural sciences. It can also serve a source of inspiration for new applications. Researchers and engineers as well as experienced experts will also find it a handy reference.
Key Features
Discusses and summarizes the latest developments in nature-inspired algorithms with comprehensive, timely literatureProvides a theoretical understanding as well as practical implementation hintsProvides a step-by-step introduction to each algorithm Table of Contents
1: Introduction to Algorithms 1.1: What is an Algorithm?
1.2: Newton-s Method
1.3: No-Free-Lunch Theorems
1.4: Nature-Inspired Metaheuristics
2: Analysis of Algorithms 2.1: Analysis of Optimization Algorithms
2.2: Nature-Inspired Algorithms
2.3: Parameter Tuning and Parameter Control
3: Random Walks and Optimization 3.1: Random Variables
3.2: Isotropic Random Walks
3.3: Lévy Distribution and Lévy Flights
3.4: Optimization as Markov Chains
4: Simulated Annealing 4.1: Annealing and Boltzmann Distribution
4.2: SA Algorithm
4.3: Unconstrained Optimization
4.4: Basic Convergence Properties
5: Genetic Algorithms 5.1: Genetic Algorithms
5.2: Role of Genetic Operators
5.3: Choice of Parameters
5.4: GA Variants
6: Differential Evolution
6.1: Variants
6.2: Choice of Parameters
6.3: Convergence Analysis
7: Particle Swarm Optimization 7.1: Swarm Intelligence
7.2: PSO Algorithm
7.3: Accelerated PSO
7.4: Binary PSO
8: Firefly Algorithms 8.1: Algorithm Analysis
8.2: Variants of the Firefly Algorithm
8.3: Firefly Algorithms in Applications
8.4: Why the Firefly Algorithm is Efficient
9: Cuckoo Search 9.1: Cuckoo Breeding Behavior
9.2: Lévy Flights
9.3: Cuckoo Search
9.4: Why Cuckoo Search is so Efficient
10: Bat Algorithms
10.1: Echolocation of Bats
10.2: Binary Bat Algorithms
10.3: Variants of the Bat Algorithm
10.4: Why the Bat Algorithm is Efficient
11: Flower Pollination Algorithms 11.1: Multi-Objective Flower Pollination Algorithms
11.2: Validation and Numerical Experiments
12: A Framework for Self-Tuning Algorithms 12.1: Algorithm Analysis and Parameter Tuning
12.2: Framework for Self-Tuning Algorithms
12.3: A Self-Tuning Firefly Algorithm
13: How to Deal with Constraints 13.1: Method of Lagrange Multipliers
13.2: KKT Conditions
13.3: Penalty Method
13.4: Equality with Tolerance
14: Multi-Objective Optimization 14.1: Pareto Optimality
14.2: Weighted Sum Method
14.3: Metaheuristic Approaches
14.4: NSGA-II
15: Other Algorithms and Hybrid Algorithms 15.1: Ant Algorithms
15.2: Bee-Inspired Algorithms
15.3: Harmony Search
15.4: Hybrid Algorithms
