Artificial Bee Colony Algorithm

Faegheh Javadi Elham Seifossadat Fall 2010

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Contents

 Intelligent Swarm-Based Optimisation Algorithms (SOAs)  Bees in Nature  Artificial Bee Colony Algorithm  Conclusion  References

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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,…

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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.

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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

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Bees in Nature

 A colony of honey bees can extend itself over long distances in multiple directions.

A Hive 10 Km B C

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Bees in Nature

 Scout bees search for food randomly from one flower patch to another.

A Hive B C

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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

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Bees in Nature

 Bees communicate through a waggle dance which contains information about: 1.

2.

3.

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)

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An Example:

S – Scout R- Onlooker UF-Uncommitted Follower EF1-Sharing information EF2- Continue work alone

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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.

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Artificial Bee Colony(ABC)

 o o o 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.

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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

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Artificial Bee Colony(ABC)

 The main steps of the algorithm are given below:

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Movement of the Onlookers

 Probability of Selecting a nectar source:

P i P i



S

 

F F

 

i

 

k k

1 : The probability of selecting the i

th

S : The number of employed bees

F θ i

 

i

: The position of the i

th

: The fitness value employed bee employed bee (1)

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Movement of the Onlookers (2)

 Calculation of the new position:

x ij

  

x ij

    

x ij



x kj

 

x i

: The position of the onlooker bee.

  t : The iteration number k : The randomly chosen employed bee.

   j : The dimension of the solution     (2)

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Movement of the Scouts

 The movement of the scout bees follows equation (3).

x ij



x

min

j



r

 

x j

max 

j

min   r : A random number (3)

Artificial Bee Colony (ABC) (3)



ij

 

j

min 

r

  

j

max  

j

min     The Employed Bee The Onlooker Bee The Scout

x ij P i

 

k

 1

F



F

 

i



k ij

   

ij

 

kj

 Record the best solution found so far 18

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Different selection process in ABC

1.

A global probabilistic selection process used by the onlooker bees.

2.

A local probabilistic selection process carried out in a region by the employed bees and the onlookers.

3.

A local selection called greedy selection process carried out by onlooker and employed bees.

4.

A random selection process carried out by scouts

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Conclusion

 Population-based algorithm.

 Robust search process: exploration and exploitation processes must be carried out together.

 Solving multi-dimensional and multimodal numeric problems.

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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

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Thanks For Your Attention

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