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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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Fireworks Algorithm For Optimization || Step-By-Step || ~xRay Pixy

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Fireworks Algorithm For Optimization


Learn Fireworks Algorithm For Optimization || Step-By-Step ||
Video Chapters: Firework Algorithm For Optimization 00:00 Introduction 00:55 About Fireworks 04:07 Fireworks Algorithm Steps 05:41 Set Off Fireworks 08:11 Calculate the Total Number of Spark 10:02 Sparks Location Calculation 14:00 conclusion

Firework Algorithm For Optimization Key Points
It is a Swarm intelligence Based Metaheuristic Algorithm. We can use Fireworks Algorithm to solve complex optimization problems in real life. The Fireworks Algorithm is basically inspired by the explosion process of Fireworks in real life. Fireworks Algorithm mimics this Fireworks explosion behavior to find out the optimal solution.
 The fireworks algorithm simulates a simple process. 1. Initialize the population for (N) fireworks. 2. Evaluate fireworks performance using an objective function. 3. Set Off N fireworks. 4. Calculate the number of sparks each firework yield and their location. 5. Evaluate new location quality using an objective function. 6. Check the stopping condition. 7. Keep the best solution and select the (n-1) location for an explosion in the next iteration. 7. Display the best solution found.

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PARTICLE SWARM OPTIMIZATION ALGORITHM NUMERICAL EXAMPLE

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Cuckoo Search Algorithm for Optimization Problems

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Particle Swarm Optimization (PSO)

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PSO (Particle Swarm Optimization) Example Step-by-Step

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 Particle swarm optimization (PSO) What is meant by PSO? PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. How PSO will optimize? By Improving a Candidate Solution. How PSO Solve Problems? PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles). What is Search Space in PSO? It is the range in which the algorithm computes th...
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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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Particle Swarm Optimization Implementation in Python Video Chapters: 00:00 Introduction 02:01 Code 05:55 Position Initialization 08:06 PSO Main Loop 08:42 Velocity Calculation 10:02 Position Update 10:36 Fitness Evaluation 13:21 Objective Function 17:44 Result 19:00 Conclusion .....................................................SOURCE CODE......................................................................... import random import numpy as np from tkinter import messagebox #Define Class Particles class Particle: def __init__ (self,position): self.position=position self.velocity=np.zeros_like(position) self.best_position=position self.best_fitness=float('inf') def PSO(ObjF,Pop_Size,D,MaxT): swarm_best_position=None swarm_best_fitness=float('inf') particles=[] #Posotion Initialization position=np.random.uniform(-0.5,0.5,D) particle=Particle(position) particles.append(particle) #Fit...
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how is the LBP |Local Binary Pattern| values calculated? Step-by-Step with Example

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

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Grey Wolf Optimization Algorithm Numerical Example

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Bat algorithm Explanation Step by Step with example

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