Nature-Inspired Optimization Algorithms,
Edition 1
By Xin-She Yang

Publication Date: 25 Feb 2014

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
About the author
By Xin-She Yang, School of Science and Technology, Middlesex University, UK
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 Firefly Algorithm

8.5 Firefly Algorithms in Applications

8.6 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

9.5 Global Convergence: Brief Mathematical Analysis

9.6 Applications

10: Bat Algorithms

10.1 Echolocation of Bats

10.2 Bat Algorithms

10.3 Implementation

10.4 Binary Bat Algorithms

10.5 Variants of the Bat Algorithm

10.6 Convergence Analysis

10.7 Why the Bat Algorithm is Efficient

10.8 Applications

11: Flower Pollination Algorithms

11.1 Introduction

11.2 Flower Pollination Algorithm

11.3 Multi-Objective Flower Pollination Algorithms

11.4 Validation and Numerical Experiments

11.5 Applications

11.6 Further Research Topics

12: A Framework for Self-Tuning Algorithms

12.1 Introduction

12.2 Algorithm Analysis and Parameter Tuning

12.3 Framework for Self-Tuning Algorithms

12.4 A Self-Tuning Firefly Algorithm

12.5 Some Remarks

13: How to Deal with Constraints

13.1 Introduction and Overview

13.2 Method of Lagrange Multipliers

13.3 KKT Conditions

13.4 Penalty Method

13.5 Equality with Tolerance

13.6 Feasibility Rules and Stochastic Ranking

13.7 Multi-objective Approach to Constraints

13.8 Spring Design

13.9 Cuckoo Search Implementation

14: Multi-Objective Optimization

14.1 Multi-Objective Optimization

14.2 Pareto Optimality

14.3 Weighted Sum Method

14.4 Utility Method

14.5 The -Constraint Method

14.6 Metaheuristic Approaches

14.7 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

15.5 Final Remarks

Appendix A: Test Function Benchmarks for Global Optimization

Appendix B: Matlab Programs

B.1 Simulated Annealing

B.2 Particle Swarm Optimization

B.3 Differential Evolution

B.4 Firefly Algorithm

B.5 Cuckoo Search

B.6 Bat Algorithm

B.7 Flower Pollination Algorithm

Book details
ISBN: 9780124167438
Page Count: 300
Retail Price : £68.99
Nature-Inspired Algorithms for Optimisation, Chiong, 2009, Springer, 9783642002663 Nature Inspired Problem-Solving Methods in Knowledge Engineering, Mira & Álvarez, 2007, Springer, 9783540730545 Nature Inspired Cooperative Strategies for Optimization, Cruz et al, Springer, 2010, 9783642125379
Graduates, PhD students and lecturers in computer science, engineering and natural sciences and also researchers and engineers.