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Intelligent Traffic Management Using || AI & Metaheuristics || ~xRay Pixy

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Hybrid Artificial Intelligence and Metaheuristics for Smart City TRafci Management Problem Video Chapters: 00:00 Introduction 00:40 Smart Cities 01:14 Traditional Methods for Traffic Management 02:12 Hybrid Approach AI and Metaheuristics 02:47 STEPS for Hybrid  Traffic Management System 08:40 Advantages of Smart Traffic Management System 09:33 Conclusion
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Python Turtle Graphics || SPIRAL Design 05 || ~xRay Pixy

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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



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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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Cuckoo Search Algorithm for Optimization Problems

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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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Particle Swarm Optimization Implementation in Python Video Chapters: 00:00 Introduction 02:01 Code 05:55 Position Initialization 08:06 PSO Main Loop 08:42 Velocity Calculation 10:02 Position Update 10:36 Fitness Evaluation 13:21 Objective Function 17:44 Result 19:00 Conclusion .....................................................SOURCE CODE......................................................................... import random import numpy as np from tkinter import messagebox #Define Class Particles class Particle: def __init__ (self,position): self.position=position self.velocity=np.zeros_like(position) self.best_position=position self.best_fitness=float('inf') def PSO(ObjF,Pop_Size,D,MaxT): swarm_best_position=None swarm_best_fitness=float('inf') particles=[] #Posotion Initialization position=np.random.uniform(-0.5,0.5,D) particle=Particle(position) particles.append(particle) #Fit
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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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