CI research

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Bee Algorithm
Direct Bee Colony Algorithm
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Njoud Maitah and Lila Bdour
Copyright ©
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The Goal
• We will present an optimization algorithm
that inspired by decision-making process of
honey bees .
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Bee Algorithm
Presented by : Njoud Maitah and Lila bdour
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Outline
•Introduction
•Bee in nature
•Bee algorithm
•Example
•Applications
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Introduction
• Honeybee search for the best nest site
between many sites with taking care of both
speed and accuracy .
• This analogues to finding the optimal solution
(optimality) in an optimization process.
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Bee in nature
• The group decision making process used by
bees for searching out the best food resources
among various solutions is a robust example
of swarm-based decision method.
• This group decision-making process can be
mimicked for finding out solutions of
optimization problems.
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Bee in nature cont..
• Bee use a waggle dance to communicate
• What is the waggle dance ?!
It is a dance that performed by scout bees to
inform other foraging bees about nectar site.
• What are the scout and foraging ?!
Scout bee : the navigator
Forging bee : the collector of food from
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Bee in nature cont..
• The waggle dance is showed in the following video .
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‫?? ‪A moment of thinking‬‬
‫بـســـم هللا الـرحـمـــن الـرحـيــــم‬
‫" وأَوحى ربك إِلى النحل أَن اتخذِي مِنَ الجبال بيوتا ومِنَ‬
‫ْ َِ ِ ُُ ً َ‬ ‫َّ ْ ِ ِ َّ ِ‬ ‫َ ْ َ َ ُّ َ َ‬
‫ُ َّ َ َ ِ َ ْ‬ ‫ُ َّ ُ‬
‫الشجر ومما يعرشونَ (86) ثم كلِي مِنْ كلِّ الثمرات فاسلُكِي‬
‫َّ َ ِ َ ِ َّ َ ْ ِ ُ‬
‫سبلَ ربكِ ذلُال يخرج مِنْ بطونها شراب مختلِف أ َْلوانه فِيه‬
‫ُ ُ ِ َ ََ ٌ ُ َْ ٌ َ ُُ ِ‬ ‫ُ ُ َ ِّ ُ َ ْ ُ ُ‬
‫َ َ َ ً َ ْ ٍ َ َ َ َّ ُ‬ ‫َ ٌ َّ ِ‬
‫شِ فاء لِلناس إِنَّ فِي ذلِك آلية لِقوم يتفكرونَ (96) ”‬
‫01‬

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Bee in nature >>
• Waggle dance is a communication method used
by bees to inform other bees about food
resources and location of nest site .
• Figure-eight running 8 .
• Number of runs represents the distance .
• The angle of run indicates the direction.
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Bee in nature >>
• Waggle dance in decision-making
• Waggle dance gives precise information about
quality ,distance and direction of flower patch.
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Bee in nature >>
• Decision 1 : Quiescent bees evaluate the patch
and decide to recruit or explore for other
patches. “decision”
 If the patch still good ,increase the number of
foraging bees.
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Bee in nature >>
• Decision 2 : decide the number of bees
recruited to the patch based on the quality.
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Bee in nature >>
• Decision 3 : Nest-site selection.
Two activity to reach to the decision :
• Consensus : agreement among the group of
quiescent.
• Quorum : threshold value.
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Bee Algorithm (BA)
• The Bees Algorithm is an optimisation
algorithm inspired by the natural foraging
behaviour of honey bees to find the
optimal solution.
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Bee Algorithm (BA)
1. Initialise population with random solutions.
2. Evaluate fitness of the population.
3. While (stopping criterion not met)
//Forming new population.
4. Select sites for neighbourhood search.
5. Recruit bees for selected sites (more bees for
best e sites) and evaluate fitnesses.
6. Select the fittest bee from each patch.
7. Assign remaining bees to search randomly
and evaluate their fitnesses.
8. End While.
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Initialise a Population of n Scout Bees
Evaluate the Fitness of the Population
Select m Sites for Neighbourhood Search
Neighbourhood Search
Determine the Size of Neighbourhood
(Patch Size ngh)
Recruit Bees for Selected Sites
(more Bees for the Best e Sites)
Select the Fittest Bee from Each Site
Assign the (n–m) Remaining Bees to Random Search
New Population of Scout Bees
Flowchart of the Basic BA
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Simple Example: Function
Optimisation
• Here are a simple example about how Bee
algorithm works
• The example explains the use of bee
algorithm to get the best value representing
a mathematical function (functional optimal)
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Simple Example
• The following figure shows the mathematical
function
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Simple Example
• 1- The first step is to initiate the population
with any 10 scout bees with random search
and evaluate the fitness. (n=10)
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Simple Example
y
*
* *
* *
*
* * * *
x
Graph 1. Initialise a Population of (n=10) Scout Bees
with random Search and evaluate the fitness.
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2- Population evaluation fitness:
• An array of 10 values is constructed and
ordered in ascending way from the highest
value of y to the lowest value of y depending
on the previous mathematical function
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3- The best m site is chosen ( the best evaluation to
m scout bee) from n
m=5, e=2, m-e=3
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y
e
▪
▪ ▫
m
▫ ▫
*
* * * *
x
Graph 2. Select best (m=5) Sites for Neighbourhood Search:
(e=2) elite bees “▪” and (m-e=3) other selected bees“▫”
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4- Select a neighborhood search site upon ngh size:
y
▪
▪ ▫
▫ ▫
x
Graph 3. Determine the Size of Neighbourhood (Patch Size ngh)
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• 5- recruits bees to the selected sites and
evaluate the fitness to the sites:
– Sending bees to e sites (rich sites) and m-e sites
(poor sites).
– More bees will be sent to the e site.
• n2 = 4 (rich)
• n1 = 2 (poor)
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**
**
y **
*
*
▪**
▪
*
*
* *
▫*
*
▫
*
▫
*
* *
x
Graph 4. Recruit Bees for Selected Sites
(more Bees for the e=2 Elite Sites)
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6- Select the best bee from each location (higher
fitness) to form the new bees population.
Choosing the best bee from every m site as follow:
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Simple Example
y
*
* ▪*
*
▪
*
*
* *
▫
*
*
*
▫ ▫
*
* *
x
Graph 5. Select the Fittest Bee * from Each Site
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Simple Example
7- initializes a new population:
Taking the old values (5) and assigning random values
(5) to the remaining values n-m
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Simple Example
y
e
* o
* *
m
o * * o
o
o
x
Graph 6. Assign the (n–m) Remaining Bees to Random Search
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Simple Example
8- the loop counter will be reduced and the steps
from two to seven will be repeated until reaching
the stopping condition (ending the number of
repetitions imax)
• At the end we reach the best solution as shown in
the following figure
• This best value (best bees from m) will represent
the optimum answer to the mathematical function
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Simple Example
y
*
*
*
* *
x
Graph 7. Find The Global Best point
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BA- Applications
Function Optimisation
BA for TSP
Training NN classifiers like MLP, LVQ, RBF and
SNNs
Control Chart Pattern Recognitions
Wood Defect Classification
ECG Classification
Electronic Design
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Honeybee foraging algorithm for load
balancing in cloud computing
• Servers are bees
• Web applications are flower patches
• And an advert board is used to simulate a waggle
dance.
• Each server is either a forager or a scout
• The advert board is where servers, successfully
fulfilling a request or may place adverts
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Flow chart of Honeybee Foraging Algorithm in load
balancing for cloud computing
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