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Python Code || Path Planning with Grey Wolf Optimization (GWO) ~xRay Pixy

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Learn how to implement an obstacle-avoiding path planning for a robot using the Grey Wolf Optimization (GWO) in a static environment. #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python
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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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