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matlab code for image segmentation using artificial bee

Artificial Bee Colony (ABC) is one of the most recently defined algorithms by Dervis Karaboga in 2005, motivated by the intelligent behavior of honey bees. It is as simple as Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithms, and uses only common control parameters such as colony size and maximum cycle number. ABC as an optimization tool, provides a population-based search procedure in which individuals called foods positions are modified by the artificial bees with time and the bee s aim is to discover the places of food sources with high nectar amount and finally the one with the highest nectar. In ABC system, artificial bees fly around in a multidimensional search space and some (employed and onlooker bees) choose food sources depending on the experience of themselves and their nest mates, and adjust their positions. Some (scouts) fly and choose the food sources randomly without using experience. If the nectar amount of a new source is higher than that of the previous one in their memory, they memorize the new position and forget the previous one. Thus, ABC system combines local search methods, carried out by employed and onlooker bees, with global search methods, managed by onlookers and scouts, attempting to balance exploration and exploitation process.

Since 2005, some members of the intelligent systems research group, the head of the group is D.Karaboga, have studied on ABC algorithm and its applications to real world-problems. Karaboga and Basturk have studied on the version of ABC algorithm for unconstrained numerical optimization problems and its extended version for the constrained optimization problems.

Abstract

This chapter explores the use of the Artificial Bee Colony (ABC) algorithm to compute pixel classification for image segmentation. ABC is a heuristic algorithm motivated by the intelligent behaviour of honey-bees which has been successfully employed to solve complex optimization problems. In this approach, an image 1-D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. For the approximation scheme, each Gaussian function represents a pixel class and therefore a threshold. Unlike the Expectation-Maximization (EM) algorithm, the ABC-based method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental results demonstrate the algorithm s ability to perform automatic multi-threshold selection yet showing interesting advantages by comparison to other well-known algorithms.

i need to know how image segmentation is performed using artificial bee colony so please send the code

I need mat-lab code for image segmentation using artificial bee colony algorithm.please help