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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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Remora Optimization Algorithm Step-by-Step Learning with Example ~xRay Pixy

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Remora Optimization Algorithm (ROA)


Remora Optimization Algorithm (ROA) is recently proposed Bionics based, Nature Inspired Metaheuristic Optimization Algorithm used to solve Global Optimization Problems. Remora Optimization Algorithm is proposed by Heming Jia, Xiaoxu Peng and Chunbo Lang in 2021. Remora Optimization Algorithm is basically inspired by the Parasitic features of remora and Random Host Replacement of remora. Remora use suction technique for their survival. They attached themselves to the host animals such as Whales, Sea Turtles, Sharks, Swordfish and other. They use their suction disk to easily attach themselves with host.

Remora clean host body from Parasites, Bacteria's, and in return they get their food for survival. They also eat the leftover food from their host. In ROA, Whale Optimization Algorithm and Swordfish Optimization Algorithm is used to update remora position in the search space. In ROA, the fusion framework is used by switching between Remora and two host (Whale, Swordfish). Remora follow 2 host Whale and Swordfish in this algorithm.
Remora Optimization Algorithm Advantages:
  • Solve Global Optimization Problems.
  • Better as compare to heuristic algorithms.
Remora Optimization Algorithm Limitations:
  • Slow Convergence Rate.
  • Poor Solution Accuracy.
  • For some engineering problems stuck in local optima.
Remora Optimization Algorithm Steps:
  • Initialize population for N remora.
  • Using fitness function evaluate performance for each remora.
  • Find out the best and worst remora in the current population.
  • Update algorithm parameters.
  • Update Remora Position
  • Again evaluate performance for updated remoras.
  • Compare solution and display best among all.
  • Find out the best and worst remora in the current population.
  • Update algorithm parameters.
  • Update Remora Position.
  • Again evaluate performance for updated remoras.
  • Compare solution and display best among all.


Meta-heuristic Algorithms   CLICK HERE...

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