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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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Fitness Values Calculation in Metaheuristics | Krill Herd Optimizer |

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Video Chapters: Krill Herd Optimization Algorithm Introduction: 00:00 KHO Parameters: 00:51 Krill's Position Initialization: 01:51 Objective Function Calculation: 03:52 Conclusion: 05:22

Learn How to Calculate Objective Function values for Metaheuristic Optimization Algorithm.
Objective Function is also known as Cost Function, Fitness function, or Evaluation Function. Krill herd Optimization Algorithm Introduction, Numerical Examples: https://www.youtube.com/playlist?list=PLVLAu9B7VtkYR8GkHtTHV83AlR0WjGCfi


Initialize the position for search agents randomly in the search space using this equation:

Agent's Position in the Search Space :


Using any Objective Function to calculate fitness values for each agent: Sphere Function is used here

Fitness Values for each agent:

Fitness(1) = 4.11424











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