Swarm Intelligence and bio-inspired computation have become increasing popular in the last two decades. Bio-inspired algorithms such as ant colony algorithms, bat algorithms, bee algorithms, firefly algorithms, cuckoo search and particle swarm optimization have been applied in almost every area of science and engineering with a dramatic increase of number of relevant publications. This book reviews the latest developments in swarm intelligence and bio-inspired computation from both the theory and application side, providing a complete resource that analyzes and discusses the latest and future trends in research directions. It can help new researchers to carry out timely research and inspire readers to develop new algorithms. With its impressive breadth and depth, this book will be useful for advanced undergraduate students, PhD students and lecturers in computer science, engineering and science as well as researchers and engineers.

Key Features

  • Focuses on the introduction and analysis of key algorithms
  • Includes case studies for real-world applications
  • Contains a balance of theory and applications, so readers who are interested in either algorithm or applications will all benefit from this timely book.


Advanced students and researchers in computer science, engineering and applied mathematics.

Table of Contents

List of Contributors


Part One: Theoretical Aspects of Swarm Intelligence and Bio-Inspired Computing

1. Swarm Intelligence and Bio-Inspired Computation

1.1 Introduction

1.2 Current Issues in Bio-Inspired Computing

1.3 Search for the Magic Formulas for Optimization

1.4 Characteristics of Metaheuristics

1.5 Swarm-Intelligence-Based Algorithms

1.6 Open Problems and Further Research Topics


2. Analysis of Swarm Intelligence–Based Algorithms for Constrained Optimization

2.1 Introduction

2.2 Optimization Problems

2.3 Swarm Intelligence–Based Optimization Algorithms

2.4 Numerical Examples

2.5 Summary and Conclusions


3. Lévy Flights and Global Optimization

3.1 Introduction

3.2 Metaheuristic Algorithms

3.3 Lévy Flights in Global Optimization

3.4 Metaheuristic Algorithms Based on Lévy Probability Distribution: Is It a Good Idea?

3.5 Discussion

3.6 Conclusions


4. Memetic Self-Adaptive Firefly Algorithm

4.1 Introduction

4.2 Optimization Problems and Their Complexity

4.3 Memetic Self-Adaptive Firefly Algorithm

4.4 Case Study: Graph 3-Coloring

4.5 Conclusions


5. Modeling and Simulation of Ant Colony’s Labor Division

5.1 Introduction

5.2 Ant Colony’s Labor Division Behavior and its Modeling Description

5.3 Modeling and Simulation of Ant Colony’s Labor Division with Multitask

5.4 Modeling and Simulation of Ant Colony’s Labor Division with Multistate

5.5 Modeling and Simulation of Ant Colony’s Labor Division with Multiconstraint

5.6 Concluding Remarks



6. Particle Swarm Algorithm

6.1 Introduction

6.2 Co


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About the authors

Xin-She Yang

Xin-She Yang obtained his DPhil in Applied Mathematics from the University of Oxford. He then worked at Cambridge University and National Physical Laboratory (UK) as a Senior Research Scientist. He is currently a Reader at Middlesex University London, Adjunct Professor at Reykjavik University (Iceland) and Guest Professor at Xi’an Polytechnic University (China). He is an elected Bye-Fellow at Downing College, Cambridge University. He is also the IEEE CIS Chair for the Task Force on Business Intelligence and Knowledge Management, and the Editor-in-Chief of International Journal of Mathematical Modelling and Numerical Optimisation (IJMMNO).


"Civil and other engineers, mathematicians, computer scientists, and other contributors summarize the current status of biologically inspired computation and swarm intelligence, looking at both fundamentals and applications of algorithms based on swarm intelligence and other biological systems."--Reference and Research Book News, August 2013