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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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Teaching Learning Based Optimization Algorithm | TLBO Numerical Example|

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Teaching Learning Based Optimization Algorithm

 | TLBO Numerical Example |

Learn Teaching Learning Based Optimization Algorithm Step-by-Step with Numerical Example.

Teaching Learning Based Optimization Algorithm is based on the effect of Teacher on the Learners in the class. Teaching Learning Based Optimization Algorithm is basically inspired by the behavior of learners in the classroom. In Teaching Learning Based Optimization Algorithm 2 main procedure are followed:

  1. Teaching Phase: Learners study from the Teacher.
  2. Learner Phase: Learners can interact with each other and they can randomly interact with each other.
Teaching Learning Based Optimization Algorithm Steps:
  1. Population Initialization Phase.
  2. Agents Performance Evaluation using Cost Function.
  3. Select Best Solution Among all.
  4. Calculate Mean Value.
  5. Teacher Phase.
  6. Mathematical Model
  7. Learner Phase
  8. MathematicalModel
  9. Evaluate New Solutions.
  10. Update Current Best Solution.
  11. Check Stopping Criteria.
  12. Display Best Solution.




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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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