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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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WFLO in Python || Optimal Placement of Wind Turbines using PSO in Python...

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Wind turbine optimal placement using particle swarm optimization Implementation in Python. Video Chapters: 00:00 Introduction 00:30 Key Points 03:17 Implementation 05:50 Flowchart 06:32 Code 23:47 Apply PSO 31:53 Output SOURCE CODE import numpy as np import math import random #Probability Distribution Function def PDF(U,k,c): return (k / c) * (U / c)**(k - 1) * math.exp(-((U / c)**k)) #Calculate Alpha def Cal_alpha(Z,Z_o): return 0.5 / math.log (Z/Z_o) #Calculate Full Wake Effect def Full_WE(u_o,a,alpha,X,R_1): return u_o*(1-(2*a/(1+alpha*(X/R_1)**2))) #Calculate Partial Wake Effect def Partial_WE(u_o,a,alpha,X,R_1,A_Partial,A_Total): return u_o * (1-(2*a/(1+alpha*(X/R_1)**2)))*(A_Partial-A_Total) #Calculate No Wake Effect def No_WE(u_o): return u_o #Calculate Power def Power(u,Ideal_Power): if u<3: return 0 elif 3<=u<=12: return Ideal_Power elif 12<=u<=25: return 518.4 else: return 0 #Calcu
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How to Initialize Population by Good-Point Set in Python ~xRay pixy

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How to Initialize Population by Good-Point Set in Python ~xRay pixy
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GWO Python Code || Grey Wolf Optimizer in Python || ~xRay Pixy

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SOURCE CODE import numpy as np import tkinter as tk import matplotlib.pyplot as plt from tkinter import messagebox def initialization (PopSize,D,LB,UB):     SS_Boundary = len(LB) if isinstance(UB,(list,np.ndarray)) else 1     if SS_Boundary ==1:         Positions = np.random.rand(PopSize,D)*(UB-LB)+LB     else:         Positions = np.zeros((PopSize,D))         for i in range(D):             Positions[:,i]=np.random.rand(PopSize)*(UB[i]-LB[i])+LB[i]     return Positions def GWO(PopSize,MaxT,LB,UB,D,Fobj):     Alpha_Pos = np.zeros(D)     Alpha_Fit = np.inf     Beta_Pos = np.zeros(D)     Beta_Fit = np.inf     Delta_Pos = np.zeros(D)     Delta_Fit = np.inf     Positions = initialization(PopSize,D,UB,LB)     Convergence_curve = np.zeros(MaxT)     l = 0     while l<MaxT:         for i in range (Positions.shape[0]):             BB_UB = Positions[i,:]>UB              BB_LB = Positions[i,:]<LB             Positions[i,:] = (Positions[i,:]*(~(BB_UB+BB_LB)))+UB*BB_UB+LB*BB_LB            
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Implement TSP in Python ||Travelling Salesman Problem|| ~xRay Pixy

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Travelling salesman problem implementation in Python. Video Chapters: 00:00 Introduction 00:34 TSP Code 06:51 Calculate the Total Distance 11:17 Find Out the Optimal Route and Minimum Distance 15:03 Output 16:00 Conclusion
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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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JavaScript Dynamic Barcode Generator || Step-By-Step || ~xRay Pixy

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SOURCE CODE <html> <head> <title>Dynamic Barcode Generator</title> <link rel="stylesheet" href="bar.css"/>  <script src="https://cdn.jsdelivr.net/npm/jsbarcode@3.11.3/dist/JsBarcode.all.min.js"></script> </head> <body> <center> <h1>Dynamic Barcode Generator</h1> <div id="box">   <input id="barcode-input" type ="text" placeholder="Enter Text for Barcode"/>   <button onclick="BarCodeGenerate()" >Generate Barcode</button> <br><br>   <svg id ="barcode"></svg> </div>   <script>      function BarCodeGenerate(){          var text = document.getElementById("barcode-input").value;          JsBarcode("#barcode",text);      }             </script> </center> </body> </html>
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JavaScript Analog Clock || Step-By-Step || ~xRay Pixy

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---------------------------- clock.html --------------------------------------- <html> <head> <title>Javascript Analog Clock</title> <link rel ="stylesheet" href="clock.css"> <script src = "clock.js"></script> </head> <body> <div id="clockBox">      <div id="hour"></div>      <div id="minute"></div>      <div id="second"></div>      <div id="point"></div> </div> </body> </html> ------------------------------ clock.css ----------------------------------------- #clockbox{           height: 650px;           width: 650px;           background: url(clc.png);           background-size:100%;           margin:auto;         } #hour, #minute, #second{                         position:absolute;                         background:red;                         border-radius:15px;                  
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Hunger Games Search Algorithm || Step-By-Step || ~xRay Pixy

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Learn Hunger Games Search (HGS) Optimization Algorithm Step-By-Step Hunger Games Search Algorithm Video Chapters: 00:00 Introduction 00:30 About Hunger Games Search Algorithm 06:00 Algorithm Steps 16:00 Conclusion
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JavaScript Calculator Step-By-Step ~xRay Pixy

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HTML + JAVASCRIPT CODE <html> <head> <title>JavaScript Calculator</title> <link rel="stylesheet" href="calc.css"/> </head> <body> <div class="front"> <form name="form"> <input id="clac" type="text" name="result"><br><br> <input type= "button" value ="1" onclick="form.result.value += '1'"> <input type= "button" value ="2" onclick="form.result.value += '2'"> <input type= "button" value ="3" onclick="form.result.value += '3'"> <input type= "button" value ="+" onclick="form.result.value += '+'"> <br><br> <input type="button" value="4" onclick="form.result.value += '4'"> <input type="button" value="5"
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Dwarf Mongoose Optimization Algorithm || Step-By-Step || ~xRay Pixy

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Video Chapters: Dwarf Mongoose Optimization Algorithm 00:00 Introduction 02:10 About Mongoose 03:55 Mongoose Communication 05:00 Mongoose Lifestyle 07:23 Dwarf Mongoose Optimization Algorithm Steps 11:29 Optimization Process Start 16:00 Conclusion New Metaheuristic Optimization Algorithm Dwarf Mongoose Optimization We can use this algorithm to solve different optimization problems as when this algorithm is tested on different continuous, discrete optimization problems it provides efficient results. So, we can use this algorithm to solve complex optimization problems This algorithm is basically inspired by the foraging behavior of mongooses in real life. Dwarf Mongoose Optimizer is introduced in 2022 by Jeffrey and all. It is a swarm intelligence-based optimization algorithm that we can use to solve complex optimization problems. This algorithm provides efficient results in comparison with seven different algorithms as you can see here Particle Swarm Optimizer, Gray wolf O
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Optimization Problems || PART 02 || Design Variables, Constraints, Objec...

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Learn how to solve Optimization problems Step-By-Step. Video Chapters: Optimization Problems PART - 02 00:00 Introduction 00:16 Optimization Problems 01:51 Design Variables 02:34 Constraints 03:13 Objective Function 04:57 Conclusion
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Fireworks Algorithm For Optimization || Step-By-Step || ~xRay Pixy

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Fireworks Algorithm For Optimization Video Link Learn Fireworks Algorithm For Optimization || Step-By-Step || Video Chapters: Firework Algorithm For Optimization 00:00 Introduction 00:55 About Fireworks 04:07 Fireworks Algorithm Steps 05:41 Set Off Fireworks 08:11 Calculate the Total Number of Spark 10:02 Sparks Location Calculation 14:00 conclusion Firework Algorithm For Optimization Key Points It is a Swarm intelligence Based Metaheuristic Algorithm. We can use Fireworks Algorithm to solve complex optimization problems in real life. The Fireworks Algorithm is basically inspired by the explosion process of Fireworks in real life. Fireworks Algorithm mimics this Fireworks explosion behavior to find out the optimal solution. The fireworks algorithm simulates a simple process. 1. Initialize the population for (N) fireworks. 2. Evaluate fireworks performance using an objective function. 3. Set Off N fireworks. 4. Calculate the number of sparks each firework yield and the
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Hybrid Metaheuristic || GA-PSO || Step-By-Step ~xRay Pixy

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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
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Honey Badger Algorithm (HBA) ||Step-By-Step|| ~xRay Pixy

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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
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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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