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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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Teaching Learning Based Optimization Algorithm | TLBO Numerical Example|

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Teaching Learning Based Optimization Algorithm

 | TLBO Numerical Example |

Learn Teaching Learning Based Optimization Algorithm Step-by-Step with Numerical Example.

Teaching Learning Based Optimization Algorithm is based on the effect of Teacher on the Learners in the class. Teaching Learning Based Optimization Algorithm is basically inspired by the behavior of learners in the classroom. In Teaching Learning Based Optimization Algorithm 2 main procedure are followed:

  1. Teaching Phase: Learners study from the Teacher.
  2. Learner Phase: Learners can interact with each other and they can randomly interact with each other.
Teaching Learning Based Optimization Algorithm Steps:
  1. Population Initialization Phase.
  2. Agents Performance Evaluation using Cost Function.
  3. Select Best Solution Among all.
  4. Calculate Mean Value.
  5. Teacher Phase.
  6. Mathematical Model
  7. Learner Phase
  8. MathematicalModel
  9. Evaluate New Solutions.
  10. Update Current Best Solution.
  11. Check Stopping Criteria.
  12. Display Best Solution.




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