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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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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.
š‘¤_š‘– = Object i has weight 
 m = Knapsack Capacity

IF a fraction š‘„_š‘–, of object i is placed into the knapsack. 0 ā‰¤ š‘„_š‘– ā‰¤ 1 than Profit Earned.
Objective: Obtain filling of Knapsack and Gain maximum profit.


n = 3;                         //Objects
m = 20;                                 //Knapsack Capacity
š‘¤1,š‘¤2,š‘¤3 = 18, 15,10; //Objects Weight
š‘ƒ1,š‘ƒ2,š‘ƒ3 = 25, 24, 15; //Profits

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