Code for Computer Science

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. Simple...

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...

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...

Video Chapters: Krill Herd Optimization Algorithm Introduction: 00:00 KHO Parameters: 00:51 Krill's Position Initialization: 01:51 Objective Function Calculation: 03:52 Conclusion: 05:22 Learn How to Calculate Objective Function values for Metaheuristic Optimization Algorithm. Objective Function is also known as Cost Function, Fitness function, or Evaluation Function. Krill herd Optimization Algorithm Introduction, Numerical Examples: https://www.youtube.com/playlist?list=PLVLAu9B7VtkYR8GkHtTHV83AlR0WjGCfi Initialize the position for search agents randomly in the search space using this equation: Agent's Position in the Search Space : Using any Objective Function to calculate fitness values for each agent: Sphere Function is used here Fitness Values for each agent: Fitness(1) = 4.11424

Metaheuristics Search Space Design and Standard Testing Functions Video Chapters: Introduction: 00:00 Metaheuristics Testing Functions: 01:05 Metaheuristics Search Space: 05:40 Conclusion: 10:00 Metaheuristics Standard Benchmark Functions for Testing - Unimodel Test Functions - Multimodel Test Functions - Fixed Dimensions Test Functions - Hybrid Test Functions - Composition Test Functions Metaheuristics Search Space Design - Search Space - Search Space Bounds Metaheuristic Search Space Design and Meta-heuristic testing functions: Different testing functions that we can use for the comparison between different metaheuristic algorithms and we can analyze algorithm Performance, Stability, Convergence Speed, Accuracy, Efficiency and other. Unimodal test functions are used to check algorithm convergence property and Exploitation capability and the fitness curve in the unimodal test function is used to check the algorithm convergence speed. Multimodal test functions are used to chec...