Nature-Inspired Optimization Algorithms

1st Edition

Print ISBN: 9780128100608
eBook ISBN: 9780124167452
Imprint: Elsevier
Published Date: 20th February 2014
Page Count: 300
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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 literature
  • Provides a theoretical understanding as well as practical implementation hints
  • Provides a step-by-step introduction to each algorithm


Graduates, PhD students and lecturers in computer science, engineering and natural sciences and also researchers and engineers.

Table of Contents


1: Introduction to Algorithms

1.1 What is an Algorithm?

1.2 Newton’s Method

1.3 Optimization

1.4 Search for Optimality

1.5 No-Free-Lunch Theorems

1.6 Nature-Inspired Metaheuristics

1.7 A Brief History of Metaheuristics

2: Analysis of Algorithms

2.1 Introduction

2.2 Analysis of Optimization Algorithms

2.3 Nature-Inspired Algorithms

2.4 Parameter Tuning and Parameter Control

2.5 Discussions

2.6 Summary

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

3.5 Step Sizes and Search Efficiency

3.6 Modality and Intermittent Search Strategy

3.7 Importance of Randomization

3.8 Eagle Strategy

4: Simulated Annealing

4.1 Annealing and Boltzmann Distribution

4.2 Parameters

4.3 SA Algorithm

4.4 Unconstrained Optimization

4.5 Basic Convergence Properties

4.6 SA Behavior in Practice

4.7 Stochastic Tunneling

5: Genetic Algorithms

5.1 Introduction

5.2 Genetic Algorithms

5.3 Role of Genetic Operators

5.4 Choice of Parameters

5.5 GA Variants

5.6 Schema Theorem

5.7 Convergence Analysis

6: Differential Evolution

6.1 Introduction

6.2 Differential Evolution

6.3 Variants

6.4 Choice of Parameters

6.5 Convergence Analysis

6.6 Implementation

7: Particle Swarm Optimization

7.1 Swarm Intelligence

7.2 PSO Algorithm

7.3 Accelerated PSO

7.4 Implementation

7.5 Convergence Analysis

7.6 Binary PSO

8: Firefly Algorithms

8.1 The Firefly Algorithm

8.2 Algorithm Analysis

8.3 Implementation

8.4 Variants of the Fir


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"...the book is well written and easy to follow, even for algorithmic and mathematical laymen. Since the book focuses on optimization algorithms, it covers a very important and actual topic."--IEEE Communications Magazine, Nature-Inspired Optimization Algorithms

"...this book strives to introduce the latest developments regarding all major nature-inspired algorithms…" -, August 2014