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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​

 Local Directional Pattern How to Calculate Local Directional Pattern (LDP) Code? With Example |Kirsch Compass Mask| ~xRay Pixy Click here - >  WATCH NOW What are Local Directional Patterns? LDP = Local Directional Pattern. Local Directional Pattern is a descriptor i.e., used for Face Recognition.  What is Descriptor?  Descriptors rely on Gradient-based or intensity variation approaches detect Local Features (e.g.,  Edges, Blobs and Regions).  BLOB = Binary Large Object (i.e., the region of the image). Descriptors such as HOG, SIFT, SURF (rely on local gradient computation).  Binary Descriptors such as BRISK, ORB or FREAK (rely on local intensity differences). Local Directional Pattern ( LDP) Assign code for each pixel in the image.  Local Directional Pattern ( LDP) encoded image is divided into regions. How LDP Calculate?  For Each pixel in the image LDP computes an 8-bit binary code. 8-bit binary pattern is calculated by involving the lo...

In this video, you will 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​ Particle Swarm Optimization Algorithm for Solving Economic Load Dispatch Problem

Butterfly Optimization Algorithm (B.O.A) Butterfly optimization algorithm : a novel approach for global optimization It is a novel optimization technique that mimics the food foraging behavior of butterflies. Keywords Butterfly optimization algorithm ·Global optimization ·Nature inspired ·Metaheuristic ·Benchmark testfunctions ·Engineering design problem Butterfly are Flying Insects. About Butterfly Butterfly Features: Small Head, 2 compound eyes. Butterfly basically feed on Nectar from flowers. Adult Butterfly consume only liquid [nectar from flowers]. They use their Antenna to sense air from wind and fragrance. Butterfly can fly only when their temperature is 27℃ or 81℉.  Largest butterfly in the world: Queen Alexandra Birdwing. Butterflies also derive nourishment from rotting fruits, dung, decaying flesh, dissolved minerals in the dirt/ sand. Butterfly Lifecycle An Adult Butterfly lay eggs on the food plant. From Eggs to Larva [Larva consume plant leaves]. When metamorphosis compl...

Moth Flame Optimization Algorithm (MFOA) is inspired by moth's behavior in nature. Moth Flame Optimization Algorithm is a nature-inspired population-based algorithm used to solve real-life optimization problems. Video timestamps Introduction: 00:00 About Moths: 00:30 Moth Flame Optimization Algorithm (MFOA): 01:20 MFOA Mathematical Model: 02:50 Moth Flame Optimization Algorithm Steps: 04:19 Moth Flame Optimization Algorithm Assumptions: 05:15 Moth Flame Optimization Algorithm Example: 06:43 Conclusion: 08:28 Topics Covered in this video What is a moth-flame optimization algorithm? The mathematical model for moth flame optimization algorithm. How moth-flame optimization algorithm works? Certain features about moth-flame optimization algorithm. Certain Assumptions about moth-flame optimization algorithm. Moth-flame optimization algorithm Application areas. How to solve moth-flame optimization algorithm? Moth Flame Optimization Algorithm Applications Forecast the electricity consumpt...

Knapsack Problem using Greedy Method Algorithm Design Techniques Divide and Conquer Greedy Method Dynamic Programming Back Tracing Branch and Bound Divide and Conquer:  Many algorithms are recursive in structure. To solve any problem, they call themselves recursively again and again [one or more times]. Three steps are followed by divide and conquer algorithms. 1.) Divide the problem into the number of sub-problems. 2.) Conquer the sub-problems by solving them recursively. 3.) Combine the solution to the sub-problems into the solution for the original problem. The greedy method  is the Straight design technique. It can be applied to a wide variety of problems. Obtain a subset that satisfies the same constraints.  Feasible Solution: If any subset satisfies these constraints.  Our GOAL: Find a feasible solution that either Maximize or Minimize the given Objective Function. A feasible solution that does this is known as OPTIMAL SOLUTION.   A feasible Solution...

 Optimization Engineering - Metaheuristic Optimization Algorithms Optimization plays a very important role in science and engineering. Optimization aim is to find out the minimum or maximum value using any objective function or cost function. In optimization different Metaheuristic Algorithms are used to solve complex problems in various fields such as Engineering Problems, Medical Problems, Computer Problems, and different real-life problems that can not be solved using classical methods. Metaheuristic optimization algorithms are classified into two main categories as Single-based optimization algorithms and Population-based optimization algorithms.  Single-based Meta-heuristic algorithms are also known as Trajectory Algorithms. Single-based metaheuristic algorithms provide the single solution in every iteration. Single-based Metaheuristic algorithm examples: Tabu Search, Guided Local Search, Iterated Local Search, Stochastic Local Search, Iterated Local Search, Variable ne...

Water Cycle Algorithm (WCA)  A number of metaheuristic algorithms have been developed to solve various constraints optimization problems. Because according to the No Free Lunch theorem no algorithm alone can not solve various real-world problems. Different problems exist in real life that is complex in nature and hard to solve. Water Cycle Optimization Algorithm is inspired by nature. The water cycle algorithm is a nature-inspired metaheuristics algorithm. Water Cycle Algorithm is basically inspired by the water cycle process in nature. In this video, you will learn how the water cycle algorithm is working step-by-step with examples and its mathematical Model. Water Cycle Algorithm is a metaheuristic optimization method used to solve different constraints-based problems and real-life engineering design problems. Water Cycle is also known as Hydrological Cycle. Water Cycle represents the continuous movement of water below and above the earth's surface. Most Precipitation: Occur as ...

C++ Program to Calculate Area of Rectangle using Objects and Classes. Calculate Area of Rectangle: Area = Length * width Program Output: Source Code  #include<iostream> #include<conio.h> using namespace std; class rectangle { private: int a, b; public: void setdata(int x, int y)  { a = x; b = y; } void area() { int area = a*b; cout<<"\n Area of Rectangle = " <<area; } }; int main() { rectangle r1, r2; //objects r1.setdata(15,40);  //object r1 called setdate() cout<<"\nFor First Rectangle "; r1.area(); // object r1 calls area() r2.setdata(30,60);  //object r1 called setdate() cout<<"\nFor First Rectangle "; r2.area(); // object r1 calls area() getch(); return 0; }

 Programming in C Language: Pointers Define Pointer. A pointer is a variable that stores a memory address. Like all other variables, it also has a name, has to be declared, and occupies some spaces in the memory.  Why Pointer is called Pointer?  It is called a pointer because it points to a particular location in memory by sorting the address of that location. Pointer General Syntax of Declaration data-type * Pointername; Here, Pointername = Name of pointer variable Astric * preceding this name informs the compiler that the variable is declared as a pointer.  Data type = Base type of pointer. For example:          int * iptr;          float * fptr; here iptr is a pointer that should point to a variable of type int. Pointers are also variables so, the compiler will reserve space for t hem and they will also have some address. All pointers irrespective of their base type will occupy the same space in memory since all of...

Firefly Algorithm Firefly algorithm is a swarm-based metaheuristic algorithm that was introduced by Yang. Firefly algorithm is used for solving optimization problems. In this video, you will learn the Firefly algorithm with an example. Firefly Algorithm is inspired by the FLASHING Behavior of Fireflies. For simplicity certain Assumptions used in Firefly Optimization Algorithm: - 1.) Fireflies are attracted to each other. 2) Attractiveness is proportional to BRIGHTNESS. 3.) Less Brighter Firefly is attracted to the Brighter Firefly. 4.) Attractiveness decrease as the distance between 2 fireflies increase. 5.) If the brightness for both is the same, fireflies move randomly. 6.) New Solutions are generated by Random walks & the Attraction of fireflies. Firefly Optimization Algorithm Steps: Initialize Parameters Initialize Population randomly in the search space. Compute Fitness values and select the best solution. Check Stopping Criteria. While Current Iteration = 1:Maximum Iterat...

Manta Ray Foraging Optimization (MRFO) Algorithm  Manta Ray Foraging Optimization (MRFO) Algorithm Example Step 01: Initialize Population Size Suppose, Population Size = 4; Lower Bound = -10; Upper Bound = 10; Maximum Iteration = 4; Suppose Initial Population  1.1  2  0.9  3 Step 02: Compute Fitness Value for each using fitness function. Fitness Values 1.21 4 0.81 9 Step 03: Obtain Best Solution Best solution = Minimum Fitness Value in the current population Best Solution = 0.81 Step 04: Check Stopping Criteria While (Current < Maximum Iteration)  1 < 4   ((True) move to next step )  If stopping criteria is then stop and return the best cost. Step 05: Update Position for each individual. For i = 1 to PopulationSize For i = 1:4 If (rand < 0.5)  THEN Cyclone Foraging Else Chain Foraging End if Step 06: Compute Fitnee Value for Each individual and Select Best Individual. Step 07: Perform Somersault Foraging.  Step 08: Co...

 Grey Wolf Optimization Algorithm Numerical Example Grey Wolf Optimization Algorithm Steps 1.) Initialize Grey Wolf Population. 2.) Initialize a, A, and C. 3.) Calculate the fitness of each search agent. 4.) 𝑿_𝜶 = best search agent 5.) 𝑿_𝜷 = second-best search agent 6.) 𝑿_𝜹 = third best search agent. 7.) while (t<Max number of iteration) 8.) For each search agent       update the position of the current search agent by the above equations end for 9.) update a, A, and C 10.) Calculate the fitness of all search agents. 11.) update 𝑿_𝜶, 𝑿_𝜷, 𝑿_𝜹 12.) t = t+1 end while 13.) return 𝑿_𝜶 Grey Wolf Optimization Algorithm Numerical Example STEP 1.  Initialize the Grey wolf Population [Initial Position for each Search Agent] 𝒙_(𝒊  )  (i = 1,2,3,…n)     n = 6 // Number of Search Agents  [ -100, 100] // Range Initial Wolf Position      3.2228     4.1553    -3.8197     4.2330  ...

 Grey Wolf Optimization Algorithm  (GWO) Grey Wolf Optimization Grey Wolf Optimization Algorithm is a metaheuristic proposed by Mirjaliali Mohammad and Lewis, 2014. Grey Wolf Optimizer is inspired by the social hierarchy and the hunting technique of Grey Wolves. What is Metaheuristic? Metaheuristic means a High-level problem-independent algorithmic framework (develop optimization algorithms). Metaheuristic algorithms find the best solution out of all possible solutions of optimization. Who are the Grey Wolves? Wolf (Animal): Wolf Lived in a highly organized pack. Also known as Gray wolf or Grey Wolf, is a large canine. Wolf Speed is 50-60 km/h. Their Lifespan is 6-8 years (in the wild). Scientific Name: Canis Lupus. Family: Canidae (Biological family of dog-like carnivorans). Grey Wolves lived in a highly organized pack. The average pack size ranges from 5-12.  4 different ranks of wolves in a pack: Alpha Wolf, Beta Wolf, Delta Wolf, and Omega Wolf. How Grey Wolf Optimiza...