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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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Sparrow Search Algorithm: New Optimization Algorithm 2021

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 Sparrow Search Algorithm Sparrow Search Algorithm is inspired by the foraging behaviors of Sparrows. 2 Types of Sparrow according to their roles in foraging:  Producers (they collect food from different sources) Scroungers ( obtain food discovered by producers ) OUTPUT: Best Position and Fitness value. Viedo Link:  https://youtu.be/Yxy0kszRzdY Sparrow Search Algorithm Steps Step 01: Initialize Sparrows population Randomly & its Parameters.  Step 02: Calculate fitness values for each agent.  Step 03: Update Sparrow Location for Producers and Scroungers in the search space.  Step 04 : Update Current New Location. Step 05 : If New Location is Better than before. [Update it] Step 06 : Increment Counter i.e., t = t + 1. [until condition satisfy (t<MaxT)]. Step 07: Return Current Best Position (𝑋_𝐵𝑒𝑠𝑡) and Fitness Value (𝑓_𝑔).  New Optimization Algorithm 2021 #SSA #Sparrowsearchalgorithm #OptimizationAlgorithm  #Metaheuristic #Algorithms ...
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Crow Search Algorithm (CSA) / Crow Search Optimization (CSO) Algorithm

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What is the Crow search algorithm? Crow search algorithm (CSA) is a population-based algorithm. Crow search algorithm is similar to Particle Swarm Optimization (PSO) algorithm. Crow search algorithm mimics the crow's intellegent behavior. CSA is based on crow's intelligent behavior.  Key Point About Crow's Crows live in large families and care for younger ones. They eat insects, worms, nuts, fruits, food, birds, non-insects, etc. They can hide excess food in hiding places and retrieve it when needed. Age: 14-17 years. Crow can memorize the hiding place positions. They follow each other to steal their food. Crows protect their hiding places from attackers. Two main parameters used in the CSA algorithm : Flights Length, Awareness Probability.  Crow Search Optimization Algorithm Main Concepts  Crow store excess food in hiding places and retrieve it when needed.  Crow cheat each other (i.e., they steal each other food). Crow Search Algorithm Step-by-Step with Example ~x...
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Cuckoo Search Algorithm for Optimization Problems

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 Cuckoo Search Algorithm - Metaheuristic Optimization Algorithm What is Cuckoo Search Algorithm? Cuckoo Search Algorithm is a Meta-Heuristic Algorithm. Cuckoo Search Algorithm is inspired by some Cuckoo species laying their eggs in the nest of other species of birds. In this algorithm, we have 2 bird Species.  1.) Cuckoo birds   2.) Host Birds (Other Species) What if Host Bird discovered cuckoo eggs? Cuckoo eggs can be found by Host Bird.  Host bird discovers cuckoos egg with Probability of discovery of alien eggs.  If Host Bird Discovered Cuckoo Bird Eggs. The host bird can throw the egg away. Abandon the nest and build a completely new nest. Mathematically, Each egg represent a solution and it is stored in the host bird nest. In this algorithm Artificial Cuckoo Birds are used. Artificial Cuckoo can lay one egg at a time. We will replace New and better solutions with less fit solutions. It means eggs that are more similar to host bird has opportunity to de...
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Particle Swarm Optimization (PSO)

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Particle Swarm Optimization (PSO) is a p opulation-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. PSO is a computational method that Optimizes a problem. PSO searches for Optima by updating generations. It is popular is an intelligent metaheuristic algorithm.  In Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. After a number of iterations agents / particles will find out optimal solution in the search space. Q. What is PSO? A. PSO is a computational method that Optimizes a problem. Q. How PSO will optimize? A. By Improving a Candidate Solution. Q. How PSO Solve Problems? A. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to ...
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