This document discusses the bee algorithm, which is an optimization technique inspired by the foraging behavior of honey bees. It begins with an introduction and overview of concepts like nature of bees, hill climbing, swarm intelligence, and bee colony optimization. It then describes the key steps of the proposed bee algorithm, including initializing a population of solutions, evaluating their fitness, selecting sites for neighborhood search, recruiting bees to search those sites, and iterating until an optimal solution is found. An example application to a traveling salesperson problem is provided. The document concludes that bee algorithm can help provide an optimal solution for problems with many possible solutions, such as in artificial intelligence applications.

1. S.SRINIVASALOU
S.KOUSICK
DEPARTMENT OF CSE

2.  Introduction
 Nature of the bees
 Hill climbing
 Swarm based optimization
 Bee algorithm
 Proposed bee algorithm
 Example-bee algorithm
 Conclusion

3.  Proposing a shortest path
 Provide speed and accuracy
 Gives optimal solution
 Swarm based algorithm
 Avoid bottleneck

4.  To provide a decision making process to
overcome the bottleneck
 The main goal of bees is to provide a optimal
solution for the given set of process
 It initiates a waggle dance to communicate
 What is a waggle dance?
◦ It is a dance that performed by scout
bees to inform other foraging bees
about nectar site.

5.  A group of bees provide a nector from
different places……..

6.  Evalute the total bees from different
places….

7.  Perform waggle dance -

8.  A method to occur on with optimal solution.
 Provides hill climbing process
 it includes ---
 Ant colony optimization – (to determine shortest path)
 Genetic algorithm –(creates a new set of population)
 Particle swarm optimization – (social behaviour of
groups on org.)
 Bee algorithm – (occur at optimal solution)

9.  It is an optimization technique
 It can be used to solve that has many number
of solutions.
 Applied to travelling sales person problem to
determine the shortest route with minimal
cost.
 Widely used in AI , to know the starting and
goal state.

10.  The algorithm that determine the basic
concept of honey bees to occur with the
optimal solution.

11. 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.

12.  The bee algorithm can be explained as with a
simple flow chart…..

13. Masaryk University, Brno, Czech Republic , Wed 08 Apr 2009 13
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
NeighbourhoodSearch
Flowchart of the Basic BA
Initialise a Population of n Scout Bees

14.  Step 1
 N= no of bees in random manner
 (ie) N = 500

15.  Step 2
 F = fitness
 if optimal solution is 500 out of
1000
 Then a single bee results with the
50% of its optimal solution
 Second bee may have 50% or more
 Process continues until it reaches
the optimal solution

16.  The optimal solution for the 500 bees is
represented by.,
01 02 03 ……
…
……
…
……
…
500
50% 60% 80% ----
---
----
----
----
----
75%

17.  Once stored recognizes the best among the
overall bees
 Filtered in ascending order
◦ (ie) from higher priority to lower priority.

18.  Consider a graph in a random manner….

19.  Consider N = 10 in a random graph.

20. Bee - example
x
y
*
*
*
*
*
* ***
*

21.  Population evaluation
 an array of 10 values in constructed and
ordered in ascending way from the highest
value of y to the lowest value of y depending
on the previous mathematical function
 The best m site is chosen randomly ( the
best evaluation to m scout bee) from n
m=5, e=2, m-e=3

22. x
y
▪
▫
▪
▫
▫
* ***
*
me

23. • Assign random neighborhood ngh
as follow

24. x
y
▪
▫
▪
▫
▫

25.  Select the best bee from each location (higher
fitness) to form the new bees population.
 At the end we reach the best solution as
shown in the following figure

26.  Bees = servers
 Flower patches = web applications
 Advert board = waggle dance
 Server = forager/scout
 Advert board = the board which fulfills the
request of the corresponding servers.

27.  Thus an optimal solution is obtained through
bee’s algorithm in cloud computing.
 Provided with speed and accuracy
 Better optimal value.