Artificial Bee Colony Algorithm

1. Artificial Bee Colony Algorithm FaeghehJavadi ElhamSeifossadat Fall 2010

2. Contents • Intelligent Swarm-Based Optimisation Algorithms (SOAs) • Bees in Nature • Artificial Bee Colony Algorithm • Conclusion • References

3. Intelligent Swarm-Based Optimisation Algorithms (SOAs) • Definition: Swarm-based optimisation algorithms (SOAs) mimic nature’s methods to drive a search towards the optimal solution. • The difference between SOAs and direct search algorithms is that SOAs use a population of solution for every iteration. • Examples: bee colony, ant colony, particle swarm optimization, artificial immune system,…

4. Swarm Intelligent • Swarm Intelligent has two fundamental concepts: 1- self organizing: • Positive feedback • Negative feedback • Fluctuations • Multiple interactions 2- division of labour: • Simultaneous task performance by cooperating specialized individuals • Enables the swarm to respond to changed conditions in the search space.

5. Bees in Nature • Food Sources: • Proximity to the nest • Richness • Ease of extracting • Employed Bees: • Associated with a particular food source • Carry and share information about it • Unemployed Bees: • Looking for a food source to exploit • Scouts • Onlookers

6. Bees in Nature • A colony of honey bees can extend itself over long distances in multiple directions. Hive A C B 10 Km

7. Hive A C B Bees in Nature • Scout bees search for food randomly from one flower patch to another.

8. Bees in Nature • The exchange of information among bees is the most important occurrence in the formation of the collective knowledge. • Communication among bees related to the quality of food sources occurs in the dancing area. • The related dance is called waggle dance. • The bees evaluate the different patches according to: • The quality of the food • The amount of energy usage

9. Bees in Nature • Bees communicate through a waggle dance which contains information about: • The direction of flower patches (Angle between the sun and patch) • The distance from the hive (Duration of the dance) • The quality rating (Frequency of the dance)

10. An Example: S – Scout R- Onlooker UF-Uncommitted Follower EF1-Sharing information EF2- Continue work alone

11. Artificial Bee Colony(ABC) Algorithm • Proposed by Karaboga – 2005 • ABC is developed based on inspecting the behaviors of real bees on finding nectar and sharing the information of food sources to the bees in the hive. • Solving multidimensional and multimodal optimisation problems.

12. Artificial Bee Colony(ABC) • Contains three groups of bees: • The Employed Bee(50%):It stays on a food source and provides the neighborhood of the source in its memory. • The Onlooker Bee (50%):It gets the information of food sources from the employed bees in the hive and select one of the food source to gathers the nectar. • The Scout (5-10%):It is responsible for finding new food, the new nectar, sources.

13. Artificial Bee Colony(ABC) • The employed bee whose food source has been exhausted by the bees, becomes a scout. • Scouts are the colony’s explorers. • The number of employed bees = the number of food source • Food source position = possible solution to the problem • The amount of nectar of a food source=quality of the solution

14. Artificial Bee Colony(ABC) • The main steps of the algorithm are given below:

15. Movement of the Onlookers • Probability of Selecting a nectar source: (1) Pi : The probability of selecting the ith employed bee S : The number of employed bees θi : The position of the ith employed bee : The fitness value

16. Movement of the Onlookers (2) • Calculation of the new position: (2) • : The position of the onlooker bee. • t : The iteration number • k : The randomly chosen employed bee. • j : The dimension of the solution • : A series of random variable in the range .

17. Movement of the Scouts • The movement of the scout bees follows equation (3). (3) • r : A random number

18. Artificial Bee Colony (ABC) (3) • The Employed Bee • The Onlooker Bee • The Scout Record the best solution found so far

19. Different selection process in ABC • A global probabilistic selection process used by the onlooker bees. • A local probabilistic selection process carried out in a region by the employed bees and the onlookers. • A local selection called greedy selection process carried out by onlooker and employed bees. • A random selection process carried out by scouts

20. Conclusion • Population-based algorithm. • Robust search process: exploration and exploitation processes must be carried out together. • Solving multi-dimensional and multimodal numeric problems.

21. References • D. Karaboga, B. Basturk, “on the performance of artificial bee colony (ABC) algorithm”, journal of Applied Soft Computing 8 (2008) 687–697. • D. Karaboga, “An idea based on honey bee swarm for numerical optimization”, Technical Report, October 2005. • D. Karaboga, B. Basturk, “A powerful and efficient algorithm for numerical function optimization: Artificial bee Colony (ABC) algorithm”, J Glob Optim (2007) 39:459-471. • D. Karaboga, B. Akay, “Artificial Bee Colony (ABC), Harmony Search and Bees Algorithms on Numerical Optimization”,Erciyes University, The Dept. of Computer Engineering, 38039, Melikgazi, Kayseri, Turkiye

22. Thanks For Your Attention Questions/Comments