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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 ...

Horse Herd Optimization Algorithm | Step-By-Step | ~xRay Pixy

Horse Herd Optimization Algorithm


Learn the Horse herd optimization Algorithm (HOA) Step-by-Step. - Nature Inspired Metaheuristic Optimization Algorithm - Inspired by Horse Herd Behavior. - A large number of Controlling Parameters are Used. - Used to Solve Higher Dimensional Optimization Problems in real life.

Video Chapters: Introduction: 00:00 Horse Herd Optimization Algorithm: 00:39 Horse Age Classification: 02:31 Horse Behavior: 04:28 Horse Position Update: 06:21 Horse Velocity Vectors: 08:26 Horse Grazing Vector: 09:28 Horse Hierarchy Vector: 10:38 Horse Sociability Vector: 11:45 Horse Imitation Vector: 12:30 Horse Defense Meachnism: 13:05 Horse Herd Optimization Algorithm Step: 15:06 Horse Velocity Vectors: 15:23 Horse Herd Optimization Algorithm Flowchart: 18:18 Conclusion: 19:00

A horse herd optimization algorithm is introduced in 2021. It is the nature-inspired population-based metaheuristic optimization algorithm that is basically inspired by the horse herding behavior in nature.

Disadvantage: Due to horse different behaviors, a large number of control parameters are used in this algorithm.
Advantage: We can use this algorithm to solve complex high-dimensional optimization problems.

Few Facts About Horses
  • Horse Lifespan: 15-30
  • Vision: 360 Degree
  • Brain Weight: 623 Gram
  • Male Horse Teeth: 40
  • Female Horse Teeth: 36
  • Horse Breeds: More than 300
  • Predators: Lion, Wolf, and other
Horses Behavior: Horses reveal different behavior at different ages. Different horses behavior used in this algorithm include:
  • Grazing (G)
  • Hierarchy (H)
  • Sociability (S)
  • Imitation (I)
  • Roam (R)
  • Defense Mechanism (D)
Horse Age Classification
  1. Delta Horse: Age Range Between 0-5 Years.
  2. Gama Horse: Age Range Between 5-10 Years.
  3. Beta Horse: Age Range Between 10-15 Years.
  4. Alpha Horse: Older than15 years.
Horse Herd Optimization Algorithm Pseudocode:
  1. Initialize important algorithm parameters.
  2. Initialize Population for N horses Randomly.
  3. Calculate the Fitness value for each horse in the current population.
  4. Select the best horse among all.
  5. Determine horse age: Calculate Alpha, Beta, Gama, and Delta Horse.
  6. Calculate horse velocity according to their age.
  7. Update Horse Position in the Search Space.
  8. Calculate fitness values for new solutions.
  9. Compare New Solutions with Older Solutions.
  10. Check stopping criteria.
  11. Display Best Solution Found.














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