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Nash Equilibrium In Game Theory ~xRay Pixy

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 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 ...
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Cat and Mouse Optimization Algorithm

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 Cat and Mouse Optimization Algorithm (CMOA)

Cat and Mouse Optimization Algorithm is a population based metaheuristic optimization algorithm. Cat and Mouse Optimization Algorithm mimic the natural behavior of Cat attack on the mouse and Mouse escape from the Cat. In this algorithm population is divided into 2 groups: Group of Cats and Group of Mice. Cat and Mice scan the whole search space in this algorithm with their random movements. Each member in the population is a solution to the given problem. Initial population is evaluated using objective function and based on their fitness values population is sorted. Best values in the population as calculated using objective function are considered as Population for Mice and worst values in the population are considered as Population for Cats.

Position Update Procedure in Cat and Mouse Optimization Algorithm (CMOA):

Position Update in CMOA is divided into 2 phases as given below:

  1. First, Move Cats Towards Mice.
  2. Second, Move Mice away from the Cats to save life (i.e., Escape Mice from the Cat).
Cat and Mouse Optimization Algorithm (CMOA) Pseudocode:
  1. Parameter Initialization Phase: Population Size, Maximum Iterations, Design Variables, Fitness Function and Problem Information.
  2. Initialize Population Randomly in the search space.
  3. Evaluate initial population using fitness function.
  4. Rank Population based on fitness values.
  5. Select Population for Mice.
  6. Select population of Cats.
  7. Update Cats Position.
  8. Update Mice Position.
  9. Display best solution. 

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