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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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All Members-Based Optimizer (AMBO) || STEP-BY-STEP || ~xRay Pixy

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All Members-Based Optimizer (AMBO)


Learn All Members-Based Optimizer Step-by-Step with Examples.
Algorithm Type: Metaheuristic Optimization Technique
Algorithm Main Idea: Make more use of the Population Matrix.
Tested on Different Benchmark Test Functions.
Algorithm Performance: Provide Better results in comparison with different metaheuristic optimization algorithms.
Used for Solving Optimization Problems.

ALGORITHM MAIN IDEA

Make use of the Population Matrix and All Members can play role in Updating Algorithm Population.

ALL MEMBERS-BASED OPTIMIZER STEPS

STEP 01: Initialize Algorithm Important Parameters. STEP 02: Initialize Population Randomly in the Search Space. STEP 03: Evaluate Initial Population using the Fitness Function. STEP 04: Check While (Current Iteration < Maximum Iteration) Do STEP 05: Update Members Position and Best Member Position. STEP 06: Update Population Members using STAGE 01. STEP 07: Update Population Members using STAGE 02. STEP 08: Save Best Solution in the Memory. STEP 09: Increment Counter. STEP 10: Best Solution Found.

ALL MEMBERS-BASED OPTIMIZER FLOWCHART



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