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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Grasshopper Optimization Algorithm (G.O.A.) Step-by-Step with Numerical ...
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Grasshopper Optimization Algorithm (G.O.A.)
Grasshoppers are also known as pests. They destroy fields and crop production. Grasshopper lifecycle contains Eggs, Nymph Phases, and Adult Grasshopper. Grasshopper Optimization Algorithm is a Nature-inspired swarm-based optimization algorithm. Grasshopper Optimization Algorithm (GOA) is inspired by the foraging and swarming behavior of grasshoppers in nature. The grasshopper optimization algorithm is basically inspired by the behavior of adult grasshoppers in nature. Adult grasshoppers can make sudden jumps and cover long-range as compare to nymphs.
This is the mathematical model used to represent grasshopper behavior in this algorithm :
𝑥_𝑖 = 𝑆_𝑖 + 𝐺_𝑖 + 𝐴_i
GrasshopperCurrentPosition = Social Interaction in the group + Force of gravity + Wind Direction.
Normally distributed random values are used in the grasshopper optimization algorithm for grasshopper random behavior in nature.
Grasshopper Optimization Algorithm Steps.
1.) Parameter Initialization.
2.) Population Initialization Phase.
3.) Compute Fitness Value for each grasshopper.
4.) Select the Best Solution Among All.
5.) Check While (CurrentIteration (t) < MaximumIteration (MaxT)).
6.) Normalize distance between grasshoppers in the range [1, 4].
7.) Update the position of the current grasshopper.
8.) Bring the Current grasshopper back if it goes outside boundaries.
9.) Update Current Best Solution if there any new Best solution.
10.) CurrentIteration = CurrentIteration + 1; // End While Loop
11.) Return Best Solution.
Grasshopper Optimization Algorithm Advantages.
Obtain better solution as compare to other metaheuristic algorithms
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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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