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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 – Based on the reproductive mechanisms of poplar trees. Two Key Processes : Sexual Propagation (Seed Dispersal) – Uses wind to spread seeds, allowing broad exploration. Asexual Reproduction (Cuttings) – Strong branches grow ...
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BAT ALGORITHM || PYTHON CODE || ~xRay Pixy

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Learn Bat Algorithm Implementation in Python. Video Chapters: Bat Algorithm 00:00 Introduction 00:42 Bat Algorithm Key Concepts 01:58 Bat Algorithm Pseudocode 02:35 Objective Function 02:49 Parameters 03:09 Python Code 06:30 BA Main Loop Start 12:30 Result The Bat Algorithm is a nature-inspired optimization algorithm developed by Xin-She Yang in 2010. It is based on the echolocation behavior of bats. Bats use echolocation to detect prey, avoid obstacles, and navigate in the dark. The algorithm simulates this behavior to find optimal solutions in complex optimization problems. Applications: The Bat Algorithm has been used in various fields, including engineering design, image processing, data mining, and robotics, for solving complex optimization problems. PYTHON CODE: import numpy as np # Define the objective function  def objective_function(x):     return np.sum(x**2) # Initialize the bat population def initialize_bats(n_bats, dim, lower_bound, upper_bound, f_min, f_m...
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