Firefly algorithm is a swarm-based metaheuristic algorithm that was introduced by Yang. Firefly Algorithm is inspired by the FLASHING Behavior of Fireflies.
Assumptions
- Fireflies are attracted to each other.
- Attractiveness is proportional to BRIGHTNESS.
- Less Brighter Firefly is attracted to the Brighter Firefly.
- Attractiveness decrease as the distance between 2 fireflies increase.
- If brightness for both is the same, fireflies move randomly.
- New Solutions are generated by Random walks & the Attraction of fireflies.
Firefly Optimization Algorithm Steps
- Initialize Parameters.
- Generate Population of n Fireflies.
- Calculate Fitness Value for Each Firefly.
- Check stopping criteria if (CurrentIteration := 1 to MaximumIteration ).
- Update Position and Light Intensity for Each Firefly.
- Report the Best Solution.
Initialize Parameters, Population of Fire Fly Swarm.
Population Size (n) = 20;
Maximum Iteration (Maxt) = 50;
Dimension (d) = 10;
Upper Bound [UB] = 100;
Lower Bound [LB] = -100;
Calculate Fitness Value [Light Intensity] for Each FireFly.
The light intensity of Firefly (i.e., 𝐼_𝑖) at 𝑥_𝑖 is computed by the Value of the Objective Function.
Firefly Position Updated as:
For i = 1 to n -1;
For j = i + 1 to n;
IF ( 𝑰_𝒋 > 𝑰_𝒊 )
Update Position. [move Firefly i towards Firefly j ];
End IF
End For
End For
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