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Bermuda Triangle Optimizer

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VIDEO LINK The Bermuda Triangle Optimizer (BTO) is a nature-inspired algorithm that simulates a gravity-like pull in the Bermuda Triangle to find optimal solutions. Learn Bermuda Triangle Optimizer (BTO) Step-By-Step using Examples. Video Chapters: Bermuda Triangle Optimizer (BTO) 00:00 Introduction 00:34 About the Bermuda Triangle 02:06 Bermuda Triangle Optimizer  05:44 BTO STEPS 09:30 BTO Advantages 10:17 BTO Limitations 10:42 BTO Applications 11:07 Conclusion Bermuda Triangle Optimizer || Step-By-Step || ~xRay Pixy Video Link:  https://youtu.be/bBnsd7BBttg #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python #optimizationproblem #optimizationalgorithms 
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Brain Storm Optimization Algorithm || Step-By-Step || ~xRay Pixy

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Learn Brainstorm optimization (BSO) algorithm step-by-step using examples.
Video Chapters: BSO Algorithm
00:00 Introduction
02:33 Brainstorming Process
03:10 Example 01: Brainstorming Process in Real-Life
05:25 Brain Strom Optimization
08:39 Example 02
14:09 BSO Steps
17:51 Conclusion

The Brain Storm Optimization (BSO) algorithm is a swarm intelligence method inspired by human brainstorming. It aims to find optimal solutions by combining clustering, exploration, and exploitation techniques.

Why is BSO Useful?

  • It balances global search (exploration) and local search (exploitation) efficiently.

  • It avoids getting stuck in local optima by introducing diversity in solution generation.

  • It is adaptable and can be integrated with machine learning techniques like clustering.

Key Concepts:

  • Solution Clustering: Solutions are grouped into clusters to refine the search space.

  • Exploration (Divergent Thinking): New solutions are generated far from existing clusters to discover new possibilities.

  • Exploitation (Convergent Thinking): Refining solutions near the best-known solutions to improve precision.

  • Selection: The best solutions are kept for the next iteration, ensuring continuous improvement.

Applications:

  • Optimization Problems (Engineering, Logistics, Scheduling)

  • Machine Learning & Data Mining (Pattern Recognition, Feature Selection)

  • Swarm Intelligence Research (Enhancing AI-driven problem-solving)

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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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

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Grey Wolf Optimization Algorithm

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Grey Wolf Optimization Algorithm Numerical Example

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Whale Optimization Algorithm Code Implementation || WOA CODE || ~xRay Pixy

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