Posts

Showing posts from June 6, 2021

New Post

Nash Equilibrium In Game Theory ~xRay Pixy

Image
 Video Link  CLICK HERE... Learn Nash Equilibrium In Game Theory Step-By-Step Using Examples. Video Chapters: Nash Equilibrium  00:00 Introduction 00:19 Topics Covered 00:33 Nash Equilibrium  01:55 Example 1  02:30 Example 2 04:46 Game Core Elements 06:41 Types of Game Strategies 06:55  Prisoner’s Dilemma  07:17  Prisoner’s Dilemma Example 3 09:16 Dominated Strategy  10:56 Applications 11:34 Conclusion The Nash Equilibrium is a concept in game theory that describes a situation where no player can benefit by changing their strategy while the other players keep their strategies unchanged.  No player can increase their payoff by changing their choice alone while others keep theirs the same. Example : If Chrysler, Ford, and GM each choose their production levels so that no company can make more money by changing their choice, it’s a Nash Equilibrium Prisoner’s Dilemma : Two criminals are arrested and interrogated separately. Each has two ...

Dragonfly Optimization Algorithm Step-by-Step with example

Image
Dragonfly Optimization Algorithm (DOA) Dragonfly Algorithm is developed by Mirjalili in 2016. Dragonfly Algorithm is a metaheuristic algorithm inspired by the behavior of dragonflies in nature. There are about 5000 known species of dragonflies. Dragonfly is a symbol of Strength, Courage, and Happiness in Japan.  Dragonfly Algorithm Step-by-Step: - Step 01: Initialize Dragonfly Population Randomly (𝑋_𝑖, Where i = 1,2,3,4,…n).  Step 02: Initialize Step vector / Size for dragonfly (〖∆𝑋〗_𝑖). Step 03: While(CurrentIteration < MaximumIteration) Step 04: Computer Fitness Values for each dragonfly. Step 05: Update Food sources and enemy.  Step 06: Update parameters w, s, a, c, f, and e. Step 07: Calculate S, A, C, and F. Step 08: Update neighboring radius.  Step 09: If the dragonfly has at least one neighboring dragonfly. {     Update Velocity and Position; }  else { Update Position; } Elseif { Check and correct new position based on boundari...

Optimization Engineering | Metaheuristic Optimization Algorithm Basic Fundamentals

Image
 Q. What is Optimization?  A.  Optimization means Optimum Point Where conditions are best and most favorable. Optimization algorithms help to obtain the best solutions for complex problems. Different numerical methods for optimization are used to design better systems.  Q. Why we do Optimization? A. To Find the better/best among different possible solutions Q. Why Objective functions are used? A. Objective functions are used to Maximize or Minimize values that you are trying to Optimize. Using objective functions you can obtain a minimum or maximum value. Q. Define Meta-heuristic optimization.  A. Metaheuristic algorithms plays important role in solving real-life problems. Metaheuristic algorithms are Optimization methods used to solve complex engineering problems. A Metaheuristic is an advanced technique for finding good solutions to a complex problem.  Q. Define multi-objective optimization problems?     A. When designers want to optimize...
More posts