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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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Objective Function Evaluation | Greedy Method | Knapsack Problem Example...

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Knapsack Problem using Greedy Method


Algorithm Design Techniques
  • Divide and Conquer
  • Greedy Method
  • Dynamic Programming
  • Back Tracing
  • Branch and Bound
Divide and Conquer: Many algorithms are recursive in structure. To solve any problem, they call themselves recursively again and again [one or more times]. Three steps are followed by divide and conquer algorithms.

1.) Divide the problem into the number of sub-problems.
2.) Conquer the sub-problems by solving them recursively.
3.) Combine the solution to the sub-problems into the solution for the original problem.

The greedy method is the Straight design technique. It can be applied to a wide variety of problems. Obtain a subset that satisfies the same constraints.  Feasible Solution: If any subset satisfies these constraints. 
Our GOAL: Find a feasible solution that either Maximize or Minimize the given Objective Function. A feasible solution that does this is known as OPTIMAL SOLUTION.  A feasible Solution is any subset that satisfies these constraints.

Greedy Method Example : KNAPSACK PROBLEM
SUPPOSE: We have 
        n  = Objects and a Knapsack.
𝑤_𝑖 = Object i has weight 
 m = Knapsack Capacity

IF a fraction 𝑥_𝑖, of object i is placed into the knapsack. 0 ≤ 𝑥_𝑖 ≤ 1 than Profit Earned.
Objective: Obtain filling of Knapsack and Gain maximum profit.


n = 3;                         //Objects
m = 20;                                 //Knapsack Capacity
𝑤1,𝑤2,𝑤3 = 18, 15,10; //Objects Weight
𝑃1,𝑃2,𝑃3 = 25, 24, 15; //Profits

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