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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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Krill Herd Optimization Algorithm

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 Krill Herd Optimization Algorithm Numerical Example

What is Krill Herd Optimization Algorithm? 

Krill herd optimization algorithm is introduced in 2012 to solve the Global Optimization Function. This is a population-based Swarm Intelligence Search Algorithm based on the Herding behavior of krill.  In the Krill herd optimization algorithm, we have a Group of Krill individuals and they are Searching for Food. 

Krill Herd Optimization Algorithm Working.

In real life, Krill move through Multidimensional Space to search for Food and High-Density herd. Three main Calculations to update Krill's Position. 

  1. Movement Induced by the position of other Krillā€™s.
  2. Foraging Activity: Krillā€™s Searching for Food.
  3. Random Diffusion: Net movement of each Krill based on Density.
KRILL MOTION CALCULATION
  • The movement led by Other Krill
  • Foraging Activity/Motion
  • Random Physical Diffusion
FOR KRILL Individual Movement is Calculated as:
š‘_š‘–^š‘š‘’š‘¤=š‘^š‘€š‘Žš‘„ š›¼_š‘–+šœ”_š‘› š‘_š‘–^š‘œš‘™š‘‘
Where,
š‘^š‘€š‘Žš‘„ = Maximum Induced Motion
 šœ”_š‘› =Inertia Weight [0,1]
š‘_š‘–^š‘œš‘™š‘‘ =Inertia Weight [0,1]
 i = Krill Individual
N = Movement 
w = Weight Inertia
Ī± = Local Swarm Density

Krill Movement is influenced by other krillā€™s.

What is šœ¶_š’Š?
š›¼_š‘– = Local Swarm Density. 
š›¼_š‘–= š›¼_š‘–^š‘™š‘œš‘š‘Žš‘™+ š›¼_š‘–^š‘”š‘Žš‘Ÿš‘”š‘’š‘”
 š›¼_š‘–^š‘”š‘Žš‘Ÿš‘”š‘’š‘”: Target Direction affected by jth Individual movement. 

If ( krill is Closest to the Food )

{

Then Density is High.

}

Each Krill Move towards the Best Solution by searching for the herd (group) with high density (similar groups) & Closest Food


FOR KRILL Individual Foraging Activity/Motion is Formulated by using 2 main parameters: 
  • The Food Location.
  • Prior knowledge about the Food Location.

Krill Herd Optimization Algorithm Steps
Step 1. Initialize the Population of N Krill Randomly.
Step 2. Calculate Fitness Value for Each Krill.
Step 3. Perform Motion Calculations. 
Step 4. Update Each Krill Position.
Step 5. Find New Position (t+1) for each Krill.
Step 6. Evaluate Each Krill according to their New Position.

Crossover Component: Each member of krill update its current position using the position of other
Mutation Component: Controlled by mutation parameter.  [change in best solution]

For more details please watch Krill herd optimization Part 1 and Part 2.
Check here to Watch Now
. 

#Metaheuristic #Algorithms
Meta-heuristic Algorithms
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