The document discusses the firefly algorithm, which is a nature-inspired optimization algorithm based on the flashing characteristics of fireflies. Fireflies use bioluminescent flashes to attract mates or find prey. The firefly algorithm simulates this behavior with brighter fireflies attracting less bright ones. The algorithm has stages including initializing parameters, generating a firefly population, calculating fitness, and updating positions iteratively as fireflies move toward brighter ones. The firefly algorithm has advantages like efficiency but disadvantages of getting trapped in local optima. It has applications in production optimization problems.

1. Firefly
Optimization
Algorithm
By. Mohamed Essam

2. Contents of this Presentation
Here’s what you’ll find :
1. Introduction about Firefly.
2. Firefly Characteristics
3. Introduction about firefly algorithm and how it works.
4. Firefly algorithms advantages and disadvantages.
5. Firefly algorithms applications
6. Firefly Algorithms Stages
7. Pseudo code .

3. Fireflies!
Swarm intelligence algorithms are a form
of nature-based optimization algorithms.
Their main inspiration is the cooperative
behavior of animals within specific
communities.

4. Fireflies
01
They don't bite, they have no pincers, they don't attack, they don't
carry disease, they are not poisonous, they don't even fly very fast.
The larvae of most species are specialized predators and feed on
other insect larvae, snails and slugs.

5. Characteristics of
Fireflies
Fireflies has a flashlight as a major characteristic. It can be used to
attract possible mates and to warn from potential threats or
enemies. This working of flashlight follow the physics' rule.

6. Introduction
01
Firefly Optimization Algorithm

7. Characteristics of
Fireflies
The flashlight is way they are talking and follow each other
according to law of physics.
With increase in distance, flashlight intensity decrease
where r is distance, and I is intensity of light.

10. Characteristics of
Fireflies
We need the small values attract to the big values as less brighter fireflies
Attracted to the brighter firefly.

11. Characteristics of
Fireflies
Secondly, the firefly blink their flashlight at regular time instances using
θ= 2π,
which is again a law of physics

12. Application use
Fireflies
Many production companies continue to face financial difficulties.
The main purpose of this algorithms is to present
an effective method for these companies to maximize profits and
minimize production costs.

13. Advantage and
disadvantage of
Fireflies
The advantages of such deterministic algorithms are that they
usually have good efficiency for certain problems and require
only a small number of iterations.
However, one of their main disadvantages is the high probability of
being trapped in local optima because they are local search
algorithms.

14. Advantage and
disadvantage of
Fireflies
In computer science, local search is a heuristic method for solving
computationally hard optimization problems. ...
Local search algorithms move from solution to solution in the
space of candidate solutions (the search space) by applying
local changes, until a solution deemed optimal is found or a time
bound is elapsed.

15. Firefly algorithm
stages
1. Measure the flashing light intensity of each firefly.
2. Compute the distance between any two fireflies xi and xj.
3. Calculate the attractiveness between two fireflies.
4. The movement which is attracted by the more brighter
firefly.

16. Firefly optimization
Algorithm
1. Initialize Parameters
2. Generate Population of n fireflies
3. Calculate Fitness value for each firefly.
4. Check if (t:=1 to maxt).
5. Update Position and light Intensity for Each Firefly.
6. Report the Best Solution.

17. Firefly optimization
Algorithm
Step1:
Initialize Parameters
N=20
Gamma=1
Beta=2
Alpha=0.2

18. Firefly optimization
Algorithm
Step2:
Create a Random Population of Firefly Swarm
Population Size(n)=20.
Maximum iteration(maxt)=50.
Dimension(d)=10.
Upper Bound [UB]=100.
Lower Bound [UB]=-100.
Position=Lower Bound+(Upper Bound-Lower Bound).*rand(1,dimension)

20. Firefly optimization
Algorithm
Step3:
Calculate the fitness value of each(Light Intensity) Firefly
Light Intensity of firefly(𝑰𝒊) at (𝒙𝒊) is computed by value of
Objective function.

22. Firefly optimization
Algorithm
Step4:
Check while(T<=Maxt)
If condition True .
Next step.
Here,1<=50[true]

23. Firefly optimization
Algorithm
Step5:
Update the position for each firefly

24. Firefly optimization
Algorithm
Step5:
Update the position for each firefly(move firefly i to j.

25. Firefly optimization
Algorithm
Step5:
Or move firefly randomly.

26. Firefly optimization
Algorithm

27. Firefly optimization
Algorithm

28. Firefly optimization
Algorithm

29. Firefly optimization
Algorithm

30. Any Questions?
 Firefly Characteristics
 Introduction about firefly algorithm and how it works.
 Firefly algorithms advantages and disadvantages.
 Firefly algorithms applications
 Firefly Algorithms Stages
 Pseudo code .