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Ant Colony Optimization Metaheuristics for the Traveling Salesman Problem Numerical Example In this video you will learn, How to Solve Traveling Salesman Problem (TSP) using Ant Colony Optimization Algorithm (ACO). Ant System for the Traveling Salesman Problem Chapters: Introduction : 00:00 Traveling Salesman Problem (TSP): 00:33 Ant Colony Optimization Traveling Salesman Problem Example: 02:11 Distance Calculation between Cities: 04:30 Solution Construction for Artificial Ants: 06:45 Ants Tour Comparison for TSP: 14:52 Conclusion: 15:45 Part 1: Learn Ant Colony Optimization Algorithm Ant Colony Optimization Algorithm step-by-step with Example (ACO) ~xRay Pixy https://youtu.be/qYXeMFCf1Gk Ant System for the Traveling Salesman Problem. Ant Colony Optimization is a Nature Inspired Approach. Ant Colony Optimization (ACO) algorithm is basically inspired by the foraging behavior of ants searching for suitable paths between their colonies and food source. Ant Colony O

WHAT IS ALGORITHM DESIGN? Algorithm Design is a specific method to create mathematical process in solving various classical problems, real world complex problems. Techniques for designing and implementing algorithm design are design patterns. ALGORITHM DESIGN TECHNIQUES We have 5 base techniques that can be used to design any algorithm.  Divide and Conquer Greedy Method Dynamic Programming Back Tracking Branch and Bound WHAT IS DIVIDE AND CONQUER TECHNIQUE?  In branch and bound technique, we break the main problem into several sub-problems. Sub-problems are similar to original problem but smaller in size. To solve given problem algorithm call themselves to deal with subproblems. Once subproblems are solved recursively then combine these solutions to create a final solution for the original problem. Merge Sort Algorithm follow Divide and Conquer method. Step 01: Divide the main problem into subproblems [n/2]. Step 02: Solve the subproblems recursively.  Step 03: Combine the solutions to

Shuffled Frog Leaping Algorithm (SFLA)  Video Timestamps: Introduction: 00:00 Shuffled Frog Leaping Algorithm (SFLA) Steps: 00:46 Shuffled Frog Leaping Algorithm (SFLA) Example: 02:00 Conclusion: 06:30 There are over 6000 different species of frogs. Frogs are found all over the world expect Antarctica. Shuffled Frog Leaping Algorithm (SFLA) is an Nature Inspired Swarm Based Metaheuristic Optimization Algorithm. Shuffled Frog Leaping Algorithm (SFLA) is inspired by frogs behavior. Shuffled Frog Leaping Algorithm (S.F.L.A.) is proposed by Eusuff and Lansey in 2003. To determine the optimum size of New Pipes (in the development of Network of pipes). Shuffled Frog Leaping Algorithm is basically inspired by the frogs behavior in finding food in the wetlands. Shuffled Frog Leaping Algorithm is combination of PSO and Memetic Algorithm. In Shuffled Frog Leaping Algorithm (S.F.L.A.), Population is Groups of Frogs and e ach Frog: Solution / Answer for the given problem. Shuffled Frog Leapi

Glowworm Swarm Optimization (GSO) Video Timestamps: Introduction : 00:00 Glowworm Swarm Optimization Algorithm (GSO): 01:04 Glowworm Swarm Optimization Mathematical Model: 03:33 Conclusion: 08:40 Glowworms release excess energy as glow. Glowworms emit blue / green glow in the larval form. Glowworm eat other insects and many species also feed on fungus. Glowworms glow for different reasons: Trying to attract mates, Warning off predators' or Attracting other insects for food. Glowworm Swarm Optimization (GSO) Algorithm for solving Optimization Problems. Glowworm Swarm Optimization is introduced by Krishnanand and Ghoose in 2005. Glowworm Swarm Optimization is Nature inspired metaheuristic optimization algorithm. Glowworm Swarm Optimization mimic the lightening behavior of Glowworms in the nature. Glowworm Swarm Optimization is used in various areas such as Engineering, Robotics, Mathematics, Networking, and to solve various problems such as Scheduling problems, Vehicle routin

Learn Face Recognition Step-by-Step using Local Binary Patterns (LBP) In this video you will learn, Face Recognition using Local Binary Patterns (LBP) . Video Timestamps: Introduction: 00:30 Face Recognition using LBP: 01:01 Conclusion: 08:22 Local Binary Pattern (LBP) Videos | Projects Click Here

JAYA Optimization Algorithm Step-by-Step with Numerical Example  JAYA Algorithm is very simple and new optimization algorithm used for solving constrained and unconstrained optimization problems. Jaya: An Advanced Optimization Algorithm. JAYA Algorithm is very simple and new optimization algorithm used for solving constrained and unconstrained optimization problems. It is Simple, Unique and Powerful Optimization Algorithm. JAYA algorithm is introduced by R.V. Rao in 2016. JAYA is a SANSKRIT word it means VICTORY. That's why this algorithm always tries to get closer to the source (i.e., reaching the BEST Solution) and at the same time tries to avoid the WORST Solution. All values at the end of iteration are maintained and these values become the INPUT to the next iteration. JAYA Algorithm is simpler than TLBO (Teaching Learning Based Optimization) algorithm. Author also compared this algorithm with latest approaches and found JAYA algorithm at RANK 01 for BEST and MEAN solution for

Face Recognition Using Local Binary Pattern (LBP) Multi-Block LBP is popular in texture recognition and is used for facial features extraction and detection has been used. The local binary operator is used for the calculation of binary patterns in digital images. The extracted features of the input images are displayed using the binary image. Binary images used two-pixel values and color black and white. The calculation of the local binary pattern is shown in Figure 3. A comparison of every neighboring pixel is done with the center pixel is done. If the neighbor pixel value is greater or equal (>=) to the center pixel value than we will assign 1 and if the neighbor pixel is smaller (<) than the central pixel than assign 0. Steps to calculate the binary patterns for face facial feature extraction and face detection are given below: Algorithm: Multi-Block LBP is used to encode the rectangular region’s intensity by using local binary patterns. Local Binary Pattern (LBP) looks at ni

Local Binary Patterns (LBP) Local Binary Pattern (LBP) Image Dataset Training for Face Recognition Local Binary Pattern (LBP) Feature Extraction Local Binary Pattern (LBP) Histogram Construction. Local Binary Pattern (LBP) Videos | Projects https://www.youtube.com/playlist?list=PLVLAu9B7VtkbmfbamE0kRutTMbPTlCYyM

Jaya Algorithm || JAYA OPTIMIZATION ALGORITHM   Jaya: An Advanced Optimization Algorithm. Video Timestamp's: Basic Introduction: 00:00 JAYA Algorithm Example: 00:55 JAYA Algorithm Application Areas: 05:07 JAYA Algorithm Mathematical Model: 05:25 JAYA Algorithm Pseudocode: 07:56 JAYA Algorithm is very simple and new optimization algorithm used for solving constrained and unconstrained optimization problems. It is Simple, Unique and Powerful Optimization Algorithm. JAYA algorithm is introduced by R.V. Rao in 2016. JAYA is a SANSKRIT word it means VICTORY. That's why this algorithm always tries to get closer to the source (i.e., reaching the BEST Solution) and at the same time tries to avoid the WORST Solution. All values at the end of iteration are maintained and these values become the INPUT to the next iteration. JAYA Algorithm is simpler than TLBO (Teaching Learning Based Optimization) algorithm. Author also compared this algorithm with latest approaches and found JAYA a

Constrained Engineering Optimization Problems In this video, we applied different Metaheuristic Optimization Algorithms on 3 different Constrained Engineering Design Optimization Problems E01, E02 and E03. E01: Welded beam design problem. E02: Speed Reducer design optimization problem. E03: Tension/Compression spring design optimization problem. All constrained engineering optimization problems have different Objective function, Decision variables and Constraints. We did not try to optimize SSA parameters, for each problem constraints are directly handled [it means IF Solution can not satisfy the constraints – we will consider it Infeasible Solution]. Three engineering problems are solved using Sparrow Search Algorithm (SSA). We also compared the results with respect to 3 Metaheuristic Algorithms: Particle Swarm Optimization Algorithm (PSO), Grey Wolf Optimization Algorithm (GWO) and Teaching Leaning Based Optimization Algorithm (TLBO). When we compared SSA with other algorithms, the p

Metaheuristic Optimization Algorithms in Big Data, Web Mining and Text Clustering. Metaheuristic optimization algorithms are best swarm intelligence methods and widely used today in Big Data, Web Mining AND Text clustering. Using metaheuristic optimization algorithms we can solve complex Machine Learning problems.  Clustering: Clustering is a common text mining technique. We can used clustering technique for the representation of Dataset that contain similarities between objects. We can use clustering in Web mining, Image Processing, Sentiment Analysis, Data Clustering, Text document clustering, and Text classification. Clustering technique is classified into 3 classes: 1.) Overlapping 2.) Partitioning 3.) Hierarchical  In Partitioning Process we can use metaheuristic optimization approaches. Partitioning process is used for the transformation of any given problem into optimization problem. Partitioning process is based on either minimization or maximization. Partitioning methods are a

Bacterial Foraging Optimization Algorithm (BFOA)  Bacterial Foraging Optimization Algorithm is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of Escherichia coli or E. coli bacteria. Bacterial Foraging Optimization Algorithm Advantages: 1.) Used to solve Engineering Problems. 2.) Used to Solve complex real world Optimization Problems. About Escherichia coli or E. coli bacteria. Escherichia coli or E. coli bacteria lives in our intestine and they are also found in the gut of some animals. Most of the Escherichia coli or E. coli bacteria are harmless. But some can cause Diarrhoea, if you eat contaminated food or drink fouled water. Escherichia coli or E. coli bacteria is mainly associated with Food positioning, Urinary Tract Infection (UTI) - approximate 75%-95% UTI are caused by Escherichia coli or E. coli bacteria. Escherichia coli or E. coli bacteria causes certain symptom's: Vomiting's, Confusion, Diarrhoea, Abdominal Cram

Video Timestamp's: Introduction: 00:00 Economic Load dispatch Cost Calculation: 00:37 Particle Swarm Optimization Parameter: 01:50 PSO Initialization: 02:19 PSO Main Loop: 03:25 Output : 06:06 Conclusion: 06:33 Economic Load Dispatch Problem (EDP) Economic Load Dispatch Problem using Lambda Iteration Method learn how we can solve Economic Load Dispatch Problem using Lambda Iteration Method  Step-by-Step with Numerical Example. This is a simple Economic load dispatch of Real power with Example.  Transmission losses are not considered.   Economic Dispatch Solution By Lambda-Iteration Method. Topics Covered in this video:  Power System Types​ Load Center, Power Plants.​ Economic Dispatch Problem?​ Economic Dispatch Problem: Equality and Inequality Constraints. ​ Economic Dispatch Problem Objective.​ Economic Dispatch Problem Step-by-Step Explanation with Numerical Example​