Chapter 7 - Artificial Bee and Ant Colony Optimization

Abstract

We present the Artificial Bee Colony (ABC) algorithm and the Ant colony Optimization (ACO). These algorithms share a common concept of swarm intelligence, that is by allowing the transfer and use of information between all members of the colony. We describe both methods and some modifications which have been suggested as improvements to the basic algorithm. We have also provided numerical studies for the ABC algorithm, examining the effect of important parameters. The Ant Colony optimization algorithm has a long established role amongst nature inspired optimization methods having been introduced in 1991 and subsequently used to successfully solved the TSP problem for a large number of cities and over the years has been applied to demanding and practical combinatorial problems with many reported successes. The approach has provided a spur to further research in the area. We have described the Ant Colony algorithm and provided a description of some of the developments in the field. The ABC algorithm is a more recent entry to the field of nature inspired optimization, having been introduced in 2005. Specifically designed for the global optimization of non-linear problems, it has been extensively studied and tested on a wide range of standard test problems and practical applications. It has been reported that its performance is strong in relation to many other algorithms in the field. We have described the basic algorithm and some of the suggested improvements.