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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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BAT ALGORITHM || PYTHON CODE || ~xRay Pixy

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Learn Bat Algorithm Implementation in Python. Video Chapters: Bat Algorithm 00:00 Introduction 00:42 Bat Algorithm Key Concepts 01:58 Bat Algorithm Pseudocode 02:35 Objective Function 02:49 Parameters 03:09 Python Code 06:30 BA Main Loop Start 12:30 Result The Bat Algorithm is a nature-inspired optimization algorithm developed by Xin-She Yang in 2010. It is based on the echolocation behavior of bats. Bats use echolocation to detect prey, avoid obstacles, and navigate in the dark. The algorithm simulates this behavior to find optimal solutions in complex optimization problems. Applications: The Bat Algorithm has been used in various fields, including engineering design, image processing, data mining, and robotics, for solving complex optimization problems. PYTHON CODE: import numpy as np # Define the objective function  def objective_function(x):     return np.sum(x**2) # Initialize the bat population def initialize_bats(n_bats, dim, lower_bound, upper_bound, f_min, f_m...
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