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Poplar Optimization Algorithm || Step-By-Step || ~xRay Pixy

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The Poplar Optimization Algorithm (POA) is a nature-inspired optimization method based on how poplar trees reproduce. It uses sexual propagation (seed dispersal by wind) for exploration and asexual reproduction (cutting and regrowth) for exploitation. Mutation and chaos factors help maintain diversity and prevent premature convergence, making POA efficient for solving complex optimization problems. Learn the Poplar Optimization Algorithm Step-By-Step using Examples. Video Chapters: Poplar Optimization Algorithm (POA) 00:00 Introduction 02:12 POA Applications 03:32 POA Steps 05:50 Execute Algorithm 1 13:45 Execute Algorithm 2 16:38 Execute Algorithm 3 18:15 Conclusion Main Points of the Poplar Optimization Algorithm (POA) Nature-Inspired Algorithm ā€“ Based on the reproductive mechanisms of poplar trees. Two Key Processes : Sexual Propagation (Seed Dispersal) ā€“ Uses wind to spread seeds, allowing broad exploration. Asexual Reproduction (Cuttings) ā€“ Strong branches grow ...
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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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