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Bee Colony Algorithm Report

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2.5.2.5. Artificial Bee Colony Optimization

It is a meta-heuristic algorithm developed by D. Karaboga in 2005. This algorithm simulates the foraging behavior of honey bees. The ABC algorithm has three phases: employed bee phase, onlooker bee phase and scout bee phase. In the first two phases, bees exploit the sources by making local search in the neighborhood of the solutions which are generated randomly. In the scout bee phase, solutions that are not beneficial anymore for search progress are abandoned, and new solutions are determined. This algorithm is the basis of the research work which is discussed in detail in the upcoming chapters.

In [30], Lin and Huang presents an algorithm based on artificial bee colony algorithm hybridized with chaos. This algorithm combines the population-based searching ability of artificial bee colony optimization and chaotic searching behavior. A chaotic variable has certain features such as ergodicity, pseudo-randomness and irregularity. Artificial bee colony optimization cannot necessarily produces global optimized solution since it is based on randomness. So the chaotic variable is introduced with special characteristics such as certainty, ergodicity …show more content…

The second step uses bee colony algorithm for optimizing the path obtained in first step. The algorithm assumes that the path is comprised of several line segments passing from certain points. The algorithm attempts to find such points with the help of bee colony algorithm. These points are the source of generating shorter paths. It is assumed that the obstacles are polygonal in shape. Sensors were used to help robot equipped with the information about the robot. The obstacles are surrounded by a virtual boundary and the distance between the boundaries is equal to the radius of the circle surrounding the

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