Code for Computer Science

Hybrid Metaheuristic  Learn Hybrid Metahuristic Step-By-Step (Genetic Algorithm (GA) and Particle Swarm Optimization(PSO)) Hybridization. Hybrid Metahuristic Video Chapters: 00:00 Introduction 00:30 Optimization Problems 02:50 Optimization Process 04:28 Metaheuristic Hybridization 08:56 GA-PSO Hybridization 13:44 Conclusion

Learn Honey Badger Optimization Algorithm Step-By-Step with Examples. Honey Badger Algorithm Video Chapters: 00:00 Introduction 01:00 About Honey Badger 03:33 Honey Badger Algorithm 07:21 Honey Badger during Digging Mode 10:05 Honey Badger during Honey Mode 12:43 Conclusion

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)

Learn Pelican Optimization Algorithm Step-By-Step with Examples. Video Chapters: Introduction: 00:00 Pelicans Behaviors: 00:34 Pelicans Hunting Behavior: 01:47 Pelican Optimization Algorithm: 03:18 Pelican Optimization Algorithm Steps: 06:36 Conclusion: 12:35

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(

Improved Grasshopper Optimization Algorithm Learn Improved Grasshopper Optimization Algorithm Step-By-Step. Video Chapters: Introduction: 00:00 Improved Grasshopper Optimization Algorithm: 00:53 Grasshopper Optimization Algorithm: 03:18 GOA Mathematical Models: 07:07 IGOA Mathematical Models: 08:25 Conclusion: 09:44

Feature Selection using Artificial Hummingbird Algorithm Learn Feature Selection using Artificial Hummingbird Algorithm Step-By-Step with Examples. Video Chapters: Introduction: 00:00 Feature Selection: 01:00 Feature Selection Problem: 02:55 Feature Selection Process: 03:52 Feature Engineering: 05:18 Feature Selection Techniques: 06:42 Feature Extraction: 07:22 Feature Selection Application: 08:15 Feature Selection using Artificial Hummingbird Algorithm: 08:33 Conclusion: 10:34 Artificial Hummingbird Algorithm: 11:00

Learn Artificial Hummingbird Algorithm Introduction: 00:00 Artificial Hummingbird Algorithm Main Component 03:16 Artificial Hummingbird Algorithm Steps: 06:50 Hummingbirds Flisht Skills: 08:55 Hummingbirds Foraging Strategies: 10:18 Artificial Hummingbird Algorithm Flowchart: 13:30 Conclusion: 16:05

Artificial Gorilla Troops Optimizer Learn Artificial Gorilla Troops Optimizer Step-By-Step with Example. Video Chapters: Artificial Gorilla Troops Optimizer Introduction: 00:00 Gorilla Lifestyle and Behavior: 02:32 Artificial Gorilla Troops Optimizer Steps: 05:13 Step 01 - Algorithm Parameters: 06:51 Step 02 - Population Initialization: 07:17 Step 03 - Population Evaluation: 08:31 Step 04 - Position Update - Exploration Phase: 09:13 Step 05 - Updated Population Evaluation: 15:05 Step 06 - Fitness Values Comparision: 15:35 Step 07 - Select Best Solution: 15:45 Step 08 - Position Update - Exploitation Phase: 16:00 Step 09 - Updated Population Evaluation: 19:10 Step 10 - Fitness Values Comparision: 19:20 Step 11 - Select Best Solution: 19:25 Step 12 - Check Stopping Condition: 19:54 Step 13 - Display Best Solution: 20:02 Conclusion: 20:18

Research Paper Writing for Beginners Simple Steps for Writing a Research Paper Video Chapters: Introduction: 00:00 Key Tool in Research Paper: 00:25 What is Research Paper: 02:35 What is Knowledge: 03:21 Research Process: 04:07 Research Areas: 04:26 Research Paper Layout: 05:32 Pieces of writing the research paper: 06:07 Experiments Role in Research: 08:48 Research Paper Keywords: 10:16 Research Paper Title: 10:47 Research Paper Abstract Section: 11:43 Research Paper Introduction Section: 12:32 Research Paper Literature Review: 13:58 Research Paper Method section: 14:35 Research Paper Result and Discussion Section: 15:09 Research Paper Conclusion Section: 16:04 Research Paper Plagiarism: 16:50 Research Paper References: 17:10 citation Methods: 17:30 Figures, Tables, Graphs, Bar Chart: 18:50 Conclusion: 19:00

Squirrel Search Algorithm (SSA) Learn Squirrel Search Optimization Algorithm Step-By-Step with Example. Video Chapters: Introduction: 00:00 Squirrel Search Algorithm: 01:11 Squirrel Search Algorithm MODEL: 03:33 Squirrel Search Algorithm STEPS: 06:18 Squirrel Search Algorithm MATHEMATICAL MODELS: 06:26 Conclusion: 15:37

Wind Turbine Optimal Positioning using Particle Swarm Optimization Algorithm Video Chapters: Introduction: 00:00 Wind Energy Projects Objectives: 01:15 Wind Turbine: 04:16 Wind Farm: 05:20 Jensen Wake Effect Model: 06:55 Wind Farm Layout: 09:05 3 Scenarios for Optimal Wind Turbine Positions: 12:02 Metaheuristics for Wind Energy Optimization: 13:54 Optimal Wind Turbine Placement Using Particle Swarm Optimization: 14:53 Optimization Process Flowchart: 20:08 Conclusion: 21:00

All Members-Based Optimizer (AMBO) Learn All Members-Based Optimizer Step-by-Step with Examples. Algorithm Type: Metaheuristic Optimization Technique Algorithm Main Idea: Make more use of the Population Matrix. Tested on Different Benchmark Test Functions. Algorithm Performance: Provide Better results in comparison with different metaheuristic optimization algorithms. Used for Solving Optimization Problems. ALGORITHM MAIN IDEA Make use of the Population Matrix and All Members can play role in Updating Algorithm Population. ALL MEMBERS-BASED OPTIMIZER STEPS STEP 01: Initialize Algorithm Important Parameters. STEP 02: Initialize Population Randomly in the Search Space. STEP 03: Evaluate Initial Population using the Fitness Function. STEP 04: Check While (Current Iteration < Maximum Iteration) Do STEP 05: Update Members Position and Best Member Position. STEP 06: Update Population Members using STAGE 01. STEP 07: Update Population Members using STAGE 02. STEP 08: Save Best Solut

Elephant Herding Optimization Algorithm Learn Elephant Herding Optimization Algorithm Step-By-Step with Examples. Elephant Herding Optimization Algorithm - Introduced in 2015 - Inspired by Elephant Herding Behavior. - Main Operator used: + Elephant Clan Updating Operator + Elephant Separating Operator - Used to Solve Optimization Problems.

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