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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 ...

 Benchmarking Optimization Algorithms Watch Now:  https://youtu.be/uBlACmRLv14 Learn about Benchmark Functions & Role of Mean & Standard Deviation in Metaheuristics Video Chapters: Mean & SD Analysis in Optimization Algorithms 00:00 Introduction 00:33 Why Benchmarking is used in Metaheuristic Algorithms? 03:26 Benchmark Function Testing 07:53 Calculate Mean and SD from Benchmark Functions 12:12 Calculation using Python 12:30 Algorithms Comparison 13:40 Conclusion Benchmarking is essential in metaheuristic algorithms to evaluate and compare their performance using standardized test functions. It helps measure accuracy, stability, and efficiency before applying these algorithms to real-world problems. Key concepts include: Mean (μ): Indicates the average performance of an algorithm. Standard Deviation (σ): Measures result in variability across multiple runs, reflecting stability. Benchmark Functions: Artificial test functions (e.g., Sphere, Rastrigin, Ackl...