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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 ...

Dragonfly Optimization Algorithm Step-by-Step with example

Dragonfly Optimization Algorithm (DOA)

Dragonfly Algorithm is developed by Mirjalili in 2016. Dragonfly Algorithm is a metaheuristic algorithm inspired by the behavior of dragonflies in nature. There are about 5000 known species of dragonflies. Dragonfly is a symbol of Strength, Courage, and Happiness in Japan. 

Dragonfly Algorithm Step-by-Step: -
Step 01: Initialize Dragonfly Population Randomly (š‘‹_š‘–, Where i = 1,2,3,4,ā€¦n). 
Step 02: Initialize Step vector / Size for dragonfly (怖āˆ†š‘‹ć€—_š‘–).
Step 03: While(CurrentIteration < MaximumIteration)
Step 04: Computer Fitness Values for each dragonfly.
Step 05: Update Food sources and enemy. 
Step 06: Update parameters w, s, a, c, f, and e.
Step 07: Calculate S, A, C, and F.
Step 08: Update neighboring radius. 
Step 09: If the dragonfly has at least one neighboring dragonfly. { 
   Update Velocity and Position;
else { Update Position; }
Elseif { Check and correct new position based on boundaries of variable; }

Note: To Improve randomness, we can update the dragonfly position using random walk (i.e., Levyā€™s Flight).




Dragonfly Optimization Algorithm on Different Engineering Design Problems

Engineering Design Problem
Engineering design problems include different complicated Cost Function (aka Fitness Function / Objective Functions). Engineering Optimization Techniques Aim is to ā€œFind out Optimum solution from all feasible solutionsā€.

How Metaheuristic Algorithms Solve Engineering Design Problems?
Metaheuristic algorithms used randomization process. Metaheuristic algorithms are suitable for global optimization. For difficult engineering problems, develop and utilize metaheuristic algorithms (which may obtain good results).

Engineering Design Problems Example
A dragonfly optimization algorithm is applied to different engineering design problems: 
Welded Beam Design Optimization Problem
Speed reducer design optimization problem
Compression spring design optimization problem

Dragonfly Optimization Algorithm on Different Engineering Design Problems ~xRay Pixy

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Meta-heuristic Algorithms
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