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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 – 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 ...
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GWO In Hindi || Step-By-Step|| ~xRay Pixy

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Learn Grey Wolf Optimizer Step-by-Step using examples in Hindi. Video Chapters: Grey Wolf Optimizer 00:00 Introduction 00:47 Topics Covered 01:28 Grey Wolf Real-life Behavior 04:35 GWO Simulation 09:36 GWO Step-By-Step 16:50  GWO Applications 17:07 GWO Advantages 17:22 GWO Disadvantages 17:29 Conclusion Grey wolves, in the wild, have a natural ability to locate prey and encircle it during a hunt. This process is led by the alpha wolf , with occasional help from the beta and delta wolves . The remaining wolves (omegas) follow the leaders' guidance. In optimization problems, however, the location of the optimal solution (the "prey") is unknown. To mimic this behavior in the Grey Wolf Optimizer (GWO), we make some assumptions: Alpha, beta, and delta are considered the top three best solutions found so far. These three "leader wolves" guide the movement of all other solutions (search agents or omegas). Grey Wolf Optimizer (GWO) is directly inspired by the social ...
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Markov Chains || Step-By-Step || ~xRay Pixy

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Learn Markov Chains step-by-step using real-life examples. Click Here   Video Link Video Chapters: Markov Chains 00:00 Introduction 00:19 Topics Covered 01:49 Markov Chains Applications 02:04 Markov Property 03:18 Example 1 03:54 States, State Space, Transition Probabilities 06:17 Transition Matrix 08:17 Example 02 09:17 Example 03 10:26 Example 04 12:25 Example 05 14:16 Example 06 16:49 Example 07 18:11 Example 08 24:56 Conclusion In computer science, Markov problems are typically associated with Markov processes or Markov models . These are related to topics involving stochastic processes and probabilistic systems where future states depend only on the current state, not on the sequence of states that preceded it. Artificial Intelligence (AI): Markov Decision Processes (MDP): Used in decision-making problems, especially in reinforcement learning. Hidden Markov Models (HMM): Widely used in speech recognition, handwriting recognition, and natural language processing. Machine Le...
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Algorithms Behind Space Missions ~xRay Pixy

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Learn different algorithms used in Space Missions. Video Link Video Chapters: Algorithms Behind Space Missions 00:00 Introduction 00:52 Space Missions 04:26 Space Missions Challenges 07:04 Algorithms Used in Space Missions 10:36 Optimization Techniques 11:44 Conclusion  NASA conducts space missions to explore the universe for various scientific, technological, and practical reasons: Understanding Our Place in the Universe Search for Life Beyond Earth Studying Earth from Space Advancing Technology Supporting Human Exploration Resource Utilization Inspiring Humanity Examples of NASA Space Missions Apollo Program: Sent humans to the Moon (1969–1972). Mars Rovers (Spirit, Opportunity, Perseverance): Explored Mars' surface and geology. Voyager Missions: Studied the outer planets and interstellar space. Hubble Space Telescope: Captured breathtaking images of the universe. International Space Station (ISS): Supports research in microgravity and international collaboration. Different ...
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Genetic Algorithm In Hindi ~xRay Pixy

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Transient Search Optimization Algorithm || Step-By-Step || ~xRay Pixy https://youtu.be/T2lVQ8mYFoM Video Chapters: TSO Algorithm 00:00 Introduction 00:44 Topics Covered 01:14 Transient Behavior 02:57 Transient Search Optimization Algorithm 06:10 TSOA Mathematical Models 10:30 TSOA Step-By-Step 15:32 TSOA Applications 15:58 TSOA Advantages 16:22 TSOA Disadvantages 16:28 Conclusion Evolutionary algorithms (EAs) are optimization methods inspired by the process of evolution in nature. They aim to find the best solutions to problems by mimicking natural selection and genetics. Key Steps in Evolutionary Algorithms : Start with a Population: Think of a population as a group of random guesses or potential solutions to your problem. Each "individual" in the population represents one solution. Evaluate Fitness: Just like in nature, some individuals are better suited to survive in their environment. In EAs, the "fitness" of a solution tells us how good it is at solving the pr...
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