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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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Metaheuristics Search Space Design and Standard Testing Functions ~xRay ...

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Metaheuristics Search Space Design and Standard Testing Functions


Video Chapters: Introduction: 00:00 Metaheuristics Testing Functions: 01:05 Metaheuristics Search Space: 05:40 Conclusion: 10:00

Metaheuristics Standard Benchmark Functions for Testing - Unimodel Test Functions - Multimodel Test Functions - Fixed Dimensions Test Functions - Hybrid Test Functions - Composition Test Functions Metaheuristics Search Space Design - Search Space - Search Space Bounds

Metaheuristic Search Space Design and Meta-heuristic testing functions:
Different testing functions that we can use for the comparison between different metaheuristic algorithms and we can analyze algorithm Performance, Stability, Convergence Speed, Accuracy, Efficiency and other.

Unimodal test functions are used to check algorithm convergence property and Exploitation capability and the fitness curve in the unimodal test function is used to check the algorithm convergence speed.


Multimodal test functions are used to check algorithm global search and local search.

Fixed dimension test functions to verify algorithm performance. We can verify accuracy, speed, and algorithm stability. In the fixed dimension test function, the dimension is fixed and you can see we have different types of fixed dimension test functions.


Search Space: search space in the metaheuristics plays a major role. Metaheuristic search is carried over the whole search space and search space is also known as Decision space State space or Search area or you can say Configuration space. Search space size represents the number of possibilities for the solution and in the search space boundary, Search space boundary we have a lower bound and upper bound.


The fitness function guides the whole search process toward the global optimal solution. In the search space for example you can see here suppose this is the search space and here we have 10 random solutions in search space you can see here:













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