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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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Dragonfly Optimization Algorithm Step-by-Step with example

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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 (𝑋_𝑖, Where i = 1,2,3,4,…n).  Step 02: Initialize Step vector / Size for dragonfly (〖∆𝑋〗_𝑖). 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 boundari...
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Optimization Engineering | Metaheuristic Optimization Algorithm Basic Fundamentals

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 Q. What is Optimization?  A.  Optimization means Optimum Point Where conditions are best and most favorable. Optimization algorithms help to obtain the best solutions for complex problems. Different numerical methods for optimization are used to design better systems.  Q. Why we do Optimization? A. To Find the better/best among different possible solutions Q. Why Objective functions are used? A. Objective functions are used to Maximize or Minimize values that you are trying to Optimize. Using objective functions you can obtain a minimum or maximum value. Q. Define Meta-heuristic optimization.  A. Metaheuristic algorithms plays important role in solving real-life problems. Metaheuristic algorithms are Optimization methods used to solve complex engineering problems. A Metaheuristic is an advanced technique for finding good solutions to a complex problem.  Q. Define multi-objective optimization problems?     A. When designers want to optimize...
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