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Full Description
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.
Contents
1. Swarm Intelligence and Bio-Inspired Computation: An Overview
2. Review and Analysis of Swarm-intelligence Based Algorithms
3. Lévy Flights and Global Optimization
4. Self-Adaptive Memetic Firefly Algorithm
5. Modelling and Simulation of Labor Division in An Ant Colony: A Problem-Oriented Approach
6. Particle Swarm Optimization and Their Variants: Convergence and Applications
7. A Survey of Swarm Algorithms Applied to Discrete Optimization Problems
8. A Comprehensive Survey of Test Functions for Global Optimization
9. Binary Bat Algorithm for Feature Selection
10. Intelligent Music Composition
11. The Development and Applications of the Cuckoo Search Algorithm
12. Bio-Inspired Models and the Semantic Web
13. Discrete Firefly Algorithm for Travelling Salesman Problem: A New Movement Scheme
14. Modelling to Generate Alternatives Using Biologically-Inspired Algorithms
15. Structural Optimization Using Krill Herd Algorithm
16. Artificial Plant Optimization Algorithm
17. Genetic Algorithms for the Berth Allocation Problem in Real Time
18. Opportunities and Challenges of Integrating Bio-Inspired Optimization and Data Mining Algorithms
19. Improvement of PSO Algorithm by Memory Based Gradient Search: Application in Inventory Management
