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Bermuda Triangle Optimizer

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VIDEO LINK The Bermuda Triangle Optimizer (BTO) is a nature-inspired algorithm that simulates a gravity-like pull in the Bermuda Triangle to find optimal solutions. Learn Bermuda Triangle Optimizer (BTO) Step-By-Step using Examples. Video Chapters: Bermuda Triangle Optimizer (BTO) 00:00 Introduction 00:34 About the Bermuda Triangle 02:06 Bermuda Triangle Optimizer  05:44 BTO STEPS 09:30 BTO Advantages 10:17 BTO Limitations 10:42 BTO Applications 11:07 Conclusion Bermuda Triangle Optimizer || Step-By-Step || ~xRay Pixy Video Link:  https://youtu.be/bBnsd7BBttg #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python #optimizationproblem #optimizationalgorithms 

POA - CODE || Pelican Optimization Algorithm Code Implementation ||

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Learn Pelican Optimization Algorithm Code Implementation Step-By-Step POA-CODE Video Chapters: 00:00 Introduction 01:22 Test Function Information Program File 02:37 Pelican Optimization Algorithm Program File 11:23 Main Program File 12:30 Conclusion 1.) Test Function Information File function [LB,UB,D,FitF] = test_fun_info(C) switch C case 'F1' FitF = @F1; LB=-100; UB =100; D =30; case 'F2' FitF = @F2; LB=-10; UB =10; D =30; case 'F3' FitF = @F3; LB=0; UB=1; D=3; end end % F1 function R = F1(x) R=sum(x.^2); end % F2 function R = F2(x) R=sum(abs(x))+prod(abs(x)); end 2.) POA File function[Best_Solution,Best_Location,Sol_con_Curve]=POA(PopSize,MaxT,LB,UB,D,FitF) LB=ones(1,D).*(LB); % Lower limit UB=ones(1,D).*(UB); % Upper limit % POPULATION INITIALIZATION PHASE for i=1:D X(:,i) = LB(i)+rand(PopSize,1).*(UB(i) ...
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