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Firefly Algorithm Step-by-Step with Numerical Example [PART - 2]
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Firefly Algorithm
Firefly algorithm is a swarm-based metaheuristic algorithm that was introduced by Yang. Firefly algorithm is used for solving optimization problems. In this video, you will learn the Firefly algorithm with an example.
Firefly Algorithm is inspired by the FLASHING Behavior of Fireflies.
For simplicity certain Assumptions used in Firefly Optimization Algorithm: -
1.) Fireflies are attracted to each other.
2) Attractiveness is proportional to BRIGHTNESS.
3.) Less Brighter Firefly is attracted to the Brighter Firefly.
4.) Attractiveness decrease as the distance between 2 fireflies increase.
5.) If the brightness for both is the same, fireflies move randomly.
6.) New Solutions are generated by Random walks & the Attraction of fireflies.
Firefly Optimization Algorithm Steps:
Initialize Parameters
Initialize Population randomly in the search space.
Compute Fitness values and select the best solution.
Check Stopping Criteria.
While Current Iteration = 1:Maximum Iteration
for i=1:Population Size
for j=1:Population Size
If ( Light Intensity(j) < Light Intensity(i)
Move Firefly i towards j.
Else
Move i Randomly.
End If
Update New Position and Light Intensity.
end
end
Check new solutions are within bound or not.
Merge Population
Sort Population
Rank Fireflies according to their light intensity
Find Best Solution Ever Found
Damp Mutation Ratio
end
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Whale Optimization Algorithm Code Implementation Whale Optimization Algorithm Code Files function obj_fun(test_fun) switch test_fun case 'F1' x = -100:2:100; y=x; case 'F2' x = -10:2:10; y=x; end end function [LB,UB,D,FitFun]=test_fun_info(C) switch C case 'F1' FitFun = @F1; LB = -100; UB = 100; D = 30; case 'F2' FitFun = @F2; LB = -10; UB = 10; D = 30; end % F1 Test Function function r = F1(x) r = sum(x.^2); end % F2 Test Function function r = F2(x) r = sum(abs(x))+prod(abs(x)); end end function Position = initialize(Pop_Size,D,UB,LB) SS_Bounds = size(UB,2); if SS_Bounds == 1 Position = rand(Pop_Size,D).*(UB-LB)+LB; end if SS_Bounds>1 for i = 1:D UB_i = UB(i); LB_i = LB(i); Position(:,i) = rand(Pop_Size,1).*(UB_i-LB_i)+LB_i; end end end function [Best_Val,Best_Pos,Convergence_Curve]=WOA(
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