Bee algorithm

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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•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.

Introduction

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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 ??
مــــيـحرـلا نـــمـحرـلا الله مـــسـب
" َنِمَو اًتوُيُب ِلاَبِجْلا َنِم يِذِخَّتا ِنَأ ِلْحَّنلا ىَلِإ َكُّبَر ىَحْوَأَو
َنوُشِرْعَي اَّمِمَو ِرَجَّشلا(68 ) يِكُلْساَف ِتاَرَمَّثلا ِّلُك ْنِم يِلُك َّمُث
ِهيِف ُهُناَوْلَأ ٌفِلَتْخُم ٌباَرَش اَهِنوُطُب ْنِم ُجُرْخَي لاُلُذ ِكِّبَر َلُبُس
َنوُرَّكَفَتَي
ٍ
مْوَقِل ًةَيلآ َكِلَذ يِف َّنِإ ِساَّنلِل ٌءاَفِش(69 )”

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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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Evaluate the Fitness of the Population




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
Select m Sites for Neighbourhood Search
Neighbourhood Search
Flowchart of the Basic BA
Initialise a Population of n Scout Bees

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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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Graph 1. Initialise a Population of (n=10) Scout Bees
with random Search and evaluate the fitness.
x
y
*
*
*
*
*
*
*
*
*
*
Simple Example

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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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Graph 2. Select best (m=5) Sites for Neighbourhood Search:
(e=2) elite bees “▪” and (m-e=3) other selected bees“▫”
x
y
▪

▫
▪

▫
▫
*
* * *
*
m
e

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4- Select a neighborhood search site upon ngh size:











x
y
▪

▫
▪

▫
▫
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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x
y
▪

▫
▪

▫
▫
*
* *
*
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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x
y
▪

▫
▪

▫
▫
*
* *
*
Graph 5. Select the Fittest Bee * from Each Site
*
*
*
*
*
*
*
*
*
*
Simple Example

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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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x
y
*
Graph 6. Assign the (n–m) Remaining Bees to Random Search
*
*
*
o
*
o
o
o
o
m
e
Simple Example

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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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x
y
*
Graph 7. Find The Global Best point
*
*
*
*
Simple Example

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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