Code for Computer Science

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.

Python Turtle Graphics || Create a Happy Birthday Message to Someone Special || Feirnds || Family Members ||

Donkey and Smuggler Optimization Algorithm Learn Donkey and Smuggler Optimization Algorithm Step-By-Step with Examples Video Chapters: Introduction: 00:00 Donkey's Behavior: 02:02 Donkey Mode: 04:20 Donkey and Smuggler Optimization Algorithm: 06:49 Smuggler Mode: 08:02 Donkey and Smuggler Optimization Algorithm STEPS: 11:54 Donkey and Smuggler Optimization Algorithm FLOWCHART: 15:39 Conclusion: 16:24 About Donkey and Smuggler Optimization Algorithm: - Introduced in 2019 by Ahmed S Shamsaldin et. al. - Nature Inspired Population-Based Metaheuristic Optimization Algorithm. - Used to Solve Complex Optimization Problems. - Implemented to solve Real Life Optimization Problems such as Travelling Salesman Problem (TSP) Routing Problems     Ambulance Routing Donkey and Smuggler Optimization Algorithm MODES: Smuggler Mode [Non-Adaptive] Donkey Mode [Adapative] Donkey and Smuggler Optimization Algorithm STEPS: Initialize algorithm parameters i.e., Population Size, Dimensions, and

TURTLE GRAPHICS USING PYTHON || Turtle Star || 08 ||  STEPS Python Turtle Graphics Design First Import Turtle Here, variable r is considered a Turtle. Set Turtle Speed. Set Line Width. Set Background Color as while.  Select Colors for the Design. Create a List of Colors. For Loop Initialization. Access Colors from the List inside Loop. Set Direction for Turtle. SOURCE CODE import turtle r = turtle.Turtle() r = turtle.Pen() r.speed(1000) r.width(2) turtle.bgcolor('white') c = ['blue','lime','red','black','gold','aqua','purple','silver'] for x in range (600):     r.pencolor(c[x%8])     r.forward(x)     r.right(160) r.done() OUTPUT

SOURCE CODE import turtle r = turtle.Turtle() r = turtle.Pen() r.width(4) r.speed(50) turtle.bgcolor('black') c = ['white','red','blue','orange'] for i in range (700):     r.pencolor(c[i%4])     r.left(i)     r.right(100) r.done()

Python Turtle Graphics || Flower Design 0 6|| SOURCE CODE from turtle import Turtle r = Turtle() r.screen.bgcolor('black') color = ['red','lime','yellow'] r.screen.tracer(0,0) for x in range(150):     r.circle(x)     r.color(color[x%3])     r.left(60) r.screen.exitonclick() r.screen.mainloop() OUTPUT

Python Turtle Graphics || SPIRAL Design 05 ||  SOURCE CODE import turtle r = turtle.Turtle() s = turtle.Screen() s.bgcolor('white') r.width(2) r.speed(20) color = ('lime','aqua','red','indigo') for i in range (300):     r.pencolor(color[i%3])     r.forward(i*4)     r.right(121) OUTPUT

PYTHON TURTLE GRAPHICS || Turtle Star || SOURCE CODE from turtle import * color('blue','yellow') begin_fill() while True:     forward(300)     left(170)     if abs(pos())<1:         break end_fill() OUTPUT

TURTLE GRAPHICS USING PYTHON SOURCE CODE import turtle as r r.title('CIRCLE PATTERN') r.speed(20) r.bgcolor('white') r.shape('circle') r.color('red') for i in range (0,360,10):     r.seth(i)     r.circle(125) r.done() OUTPUT

PYTHON TURTLE GRAPHICS DESIGNS SOURCE CODE FOR SPIRAL HEXAGON import turtle colors = ['red','yellow','blue','green','white','orange','silver','pink'] s = turtle.Pen() turtle.bgcolor('black') for i in range (250):     s.pencolor(colors [i % 6])     s.width(i/100 +1)     s.forward(i)     s.left(50) OUTPUT

Python Turtles Graphics || Source Code || PYTHON TURTLE SOURCE CODE import turtle c = turtle.Turtle() c.color("blue") c.pensize(10) c.shape("turtle") c.backward(150) c.left(90) c.forward(150) c.right(90) c.forward(150) c.left(45) c.forward(40) c.right(67) c.backward(50) c.left(250) c.backward(60) c.left(180) c.forward(50) c.right(90) c.forward(100) c.down() c.forward(150) c.left(60) c.down() c.forward(40) c.down() c.right(60) c.forward(100) c.left(230) c.forward(200) c.up() c.right(52) c.forward(50) c.backward(200) c.left(20) c.forward(100) turtle.done()

PYTHON FOR BEGINNERS # Variables: Used to hold Values x = 200 y = 2 z = x+y print(z) OUTPUT: 202 # Careating Strings in Python r = 'Create stings' print(r) d = 'Don\'t Give Up' print(d) OUTPUT   Create stings Don't Give Up #Hold Values in Strings val = 50 disp = 'My Value is %s' print(disp % val) OUTPUT:  My Value is 50 #placeholder different variables - replace stings msg = '%s: Python is good' msg2 = '%s: Yes' c1 = 'Roy' c2 = 'Jhon' print(msg % c1) print(msg2 % c2) OUTPUT   Roy: Python is good Jhon: Yes #Hold Multiple Values  Hold= 'Add %s and %s' num1 = 23 num2 = 65 print(Hold % (num1,num2)) OUTPUT:   Add 23 and 65 #String Multiplication print(3 * 'R') OUTPUT:  RRR #How to use Space/Tab Space = ' ' * 10 print('%s Hello' % Space) print() print('%s Life = Peace' % Space) OUTPUT  :             Hello            Life = Peace #Create a String List and access Values from it. SimpleList

ARCHIMEDES OPTIMIZATION ALGORITHM VIDEO LINK: CLICK HERE... VIDEO CHAPTERS Introduction: 00:00 Archimedes Principle: 01:19 Archimedes Optimization Algorithm Idea: 07:07 Archimedes Optimization Algorithm Steps: 08:08 Archimedes Optimization Algorithm Mathematical Models: 11:32 Conclusion: 20 :12 Learn Archimedes Optimization Algorithm Step-by-Step with Example. Archimedes Optimization Algorithm Inspiration: Popular Physics Law (Archimedes Principle). - Used to Solve Complex Numerical Optimization Problems. - Used to solve Engineering Design Optimization Problems. Archimedes Principle : According to Archimedes Principle when a body is immersed wholly or partially in a fluid it loses its weight which is equal to the weight of the liquid displaced by the body. KEY TERMS Fluid: The Substance that flows under the action of applied forces. The fluid does not have its own shape. Pressure: It is a normal force acting on a unit surface area of the liquid. It is Force / Area. Density: Mas

Artificial Ecosystem Based Optimization Algorithm Video Link :  https://youtu.be/rbaxNqu7bdM Learn Artificial Ecosystem-based Optimization (AEO) Algorithm Step-by-Step with Example. - Algorithm Type: Nature Inspired Population-Based Metaheuristic Optimization - Used to solve Optimization Problems, Real life Engineering Design Optimization Problems - Provide best results when tested on different benchmark functions. - Outperforms other metaheuristics performance. Video Chapters: Introduction: 00:00 Algorithm Introduction: 01:06 The Ecosystem on Earth: 02:01 Food Chain: 06:28 Artificial Ecosystem-based Optimization Algorithm: 08:27 Artificial Ecosystem-based Optimization Algorithm Steps: 10:41 Mathematical Models: 12:12 Decomposition Process: 19:15 Conclusion: 22:29 An ecosystem is also known as Ecological System. Ecosystem components are Abiotic and Biotic. Abiotic components are non-living parts of the Ecosystem like Rock, water, air, etc. Biotic components are living parts of

Horse Herd Optimization Algorithm Learn the Horse herd optimization Algorithm (HOA) Step-by-Step. - Nature Inspired Metaheuristic Optimization Algorithm - Inspired by Horse Herd Behavior. - A large number of Controlling Parameters are Used. - Used to Solve Higher Dimensional Optimization Problems in real life. Video Chapters: Introduction: 00:00 Horse Herd Optimization Algorithm: 00:39 Horse Age Classification: 02:31 Horse Behavior: 04:28 Horse Position Update: 06:21 Horse Velocity Vectors: 08:26 Horse Grazing Vector: 09:28 Horse Hierarchy Vector: 10:38 Horse Sociability Vector: 11:45 Horse Imitation Vector: 12:30 Horse Defense Meachnism: 13:05 Horse Herd Optimization Algorithm Step: 15:06 Horse Velocity Vectors: 15:23 Horse Herd Optimization Algorithm Flowchart: 18:18 Conclusion: 19:00 A horse herd optimization algorithm is introduced in 2021. It is the nature-inspired population-based metaheuristic optimization algorithm that is basically inspired by the horse herdin