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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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Particle Swarm Optimization (PSO) |Part - 2| with Numerical Example and ...

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Particle Swarm Optimization (PSO) Algorithm Particle Swarm Optimization (PSO) Algorithm step-by-step explanation with Numerical Example and source code implementation. šŸŒž Particle Swarm Optimization (PSO) Algorithm Matlab code. Particle Swarm Optimization Main File: main.m pso; Particle Swarm Optimization Function File: Sphere(x) function F1 = Sphere(x) F1 = sum(x.^2); end Particle Swarm Optimization File Name Save as: pso.m clear; close all; %% Fitness Function Calling FitnessFunction=@(x) Sphere(x); % Fitness Function Calling % Total Number of Decision Variables Used nVar=10; % Size of Decision Variables Matrix VarSize=[1 nVar]; % Lower Bound LowerBound =-10; % Upper Bound UpperBound = 10; %% Parameters Initialization Phase % Maximum Number of Iterations used. MaxT=100; % Total Number of Search Agents used. PopulationSize = 10; % Initialize PS...
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