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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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Aquila Optimization Algorithm Step-by-Step Explanation ~xRay Pixy

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Video Chapters:
Introduction: 00:00
Aquila Optimizer: 00:31
Aquila Hunting Methods: 02:09
Aquila Optimizer Steps: 03:33
Aquila Optimizer Mathematical Models: 06:07
Conclusion: 13:00

Aquila Optimization Algorithm is inspired by the Aquila Behavior in the nature. This algorithm is basically inspired by the aquilas hunting methods. How they catch their prey in the real life?

Aquila Hunting Methods:
Method 01: High Soar with Vertical Stoop. [i.e., Expanded Exploration]
Method 02: Contour Flight with Short Glide Attack. [i.e., Narrowed Exploration]
Method 03: Low Flight with Slow Decent Attack. [i.e., Expanded Exploitation]
Method 04: Walking and Grab the Prey. [i.e., Narrowed Exploitation]

Aquila Optimization Algorithm Steps:
Step 01: Initialize Algorithm Parameters and Population Randomly.
Step 02: Check While (Current Iteration <= Maximum Iteration)
Step 03: Evaluate Agents Performance using Fitness Function.
Step 04: For all agents update Location mean value.
Step 05: Update Levy's Flight.
Step 06: Check IF (Current Iteration <= (2/3) * Maximum Iteration)
Step 07: Check IF (rand<= 0.5) then
Method 01: High Soar with Vertical Stoop. [i.e., Expanded Exploration]
Else
Method 02: Contour Flight with Short Glide Attack. [i.e., Narrowed Exploration]
End IF
Else
Step 08: Check IF (rand<= 0.5) then
Method 03: Low Flight with Slow Decent Attack. [i.e., Expanded Exploitation]
Else
Method 04: Walking and Grab the Prey. [i.e., Narrowed Exploitation]
End IF
End IF
Step 09: End While
Step 10: Display Best Solution.






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