Code for Computer Science

Ant Colony Optimization Numerical Example Step-By-Step 00:00 Introduction 01:34 Ant Colony Optimization Steps 04:33 Step 1 Parameter Initialization 06:14 Step 2 Population Initialization 06:40 Step 3 Path Selection by Ants 12:16 Step 4 Objective Function Evaluation 14:28 Step 5 Test Convergence 15:27 Step 6 Second Iteration Start 15:36 Step 7 Pheromone Update 18:20 Step 8 Repeat ACO Loop 19:27 Path Selection by Ants 21:42 Objective Function Evaluation 22:51 Test Convergence 22:58 Third Iteration 23:25 Pheromone Update 24:26 Path Selection by Ants 25:00 Conclusion Ant Colony Optimization (ACO) Algorithm Steps Initialize Parameters. Initialize Ants Population. Calculate Path Selection Probability by ants. Apply Roulette Wheel Selection Process. Calculate Objective Function Values. Test Process Convergence. Increment Counter. Update Pheromones. Repeat Step 3 to Step 6. Display Best Solution  How to Calculate Path Selection Probability by Ants? For any ant (k), the Probability of Selecting

Learn the Dingo Optimization Algorithm (DOA) Step-By-Step using Example Video Chapters: DOA 00:00 Introduction 01:42 About Dingo Lifestyle 02:54 Dingo Hunting Methods 03:49 DOA Flowchart 05:15 DOA STEPS 06:14 Initialize Parameters 06:41 Initialize Population 08:06 Fitness Values Calculation 08:31 Main Loop Start 08:46 Hunting Mathematical Models 09:28 Group Attack 11:37 Percussion 12:26 Scavenger 13:02 Dingo Survival Rate 14:24 New Fitness Calculation 15:00 Conclusion 

Learn the Mountain Gazelle Optimization Algorithm Step-By-Step. Video Chapters: MGO 00:00 Introduction 01:07 About Mountain Gazelle 03:11 Steps for Mountain Gazelle Optimizer  04:48 Initialize Population 05:11 Fitness Calculation 05:33 MGO Main Loop 07:17 TSM Calculation 10:05 MH Calculation 11:12 BMH Calculation 12:25 MSF Calculation 13:00 Add TSM, MH, BMH, MSF 13:35 Update Herd 14:00 Conclusion 

Learn Quantum Cat Swarm Optimization Algorithm Step-By-Step using Example Video Chapters: QCSO 00:00 Introduction 01:29 Cat Swarm Optimization 06:34 Quantum Cat Swarm Optimization 08:46 Quantum Computing Principles 12:12  What is Quantum Bit (Qubit)? 12:46 Quantum Population Initialization 16:26 QCSO Advantages 17:05 QCSO Applications 17:20 Conclusion ns 17:20 Conclusion

Video Chapters: Soft Computing Module 2 - Neural Networks 00:00 Introduction 00:39 Topics Covered in Video 01:19 Neural Network Basics 05:12 Problem 01 - Calculate Net Input to Output Neuron 06:33 Artificial Neural Network Terminologies 06:54 Weights 07:59 Bias 08:13 Threshold 08:33 Learning Rate 08:49 Momemtum Factor 09:03 Problem 02 - Calculate Output of Neuron (Y) using Activation Function 09:30 Activation Function 12:42 Problem 02 - Solution 15:25 Neural Network Types 15:38 Simple Neural Network 16:08 Single Feedforward Neural Network 16:41 Multilayer Feedforward Neural Network 17:21 Single Layer Recurrent Network 17:50 Multilayer Recurrent Network 17:52 Perceptron 21:57 Multilayer Perceptron 22:15 Adaline -Its Training and Capabilities 22:59 Conclusion

Find the maximum value for the objective function using Particle Swarm Optimization Step-By-Step. Video Chapters: Find the Maxima of Function using the PSO Method 00:00 Introduction 02:18 Objective 03:17 Maximization Problem 04:22 Particle Swarm Optimization Steps 05:22 Step 1 - Objective Function 05:30 Step 2 - Position and Velocity Initialization 06:00 Step 3 - Fitness Calculation 07:06 Step 4 - Update Personal Best 07:16 Step 5 - Update Global Best 07:42 Step 6 - Position Update 10:34 Step 7 - Solution Boundary Checking 10:53 Step 8 - New Solution Evaluation 11:31 Step 9 - Update Personal Best 12:12 Step 10 - Update Global Best 13:24 Iteration 2 Start - Position Update 14:45 New Solution Boundary Checking 15:24 New Solution Fitness Calculation 15:48 Update Personal Best 16:32 Update Global Best 17:42 Conclusion Problem: Find the Maxima of the function � ( � ) = � 2 + 2 � + 11 f ( x ) = x 2 + 2 x + 11 � ( � ) = � 2 + 2 � + 11 f ( x ) = x 2 in the range -2<=x<=2 using PSO m