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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JAYA Optimization Algorithm Step-by-Step with Numerical Example ~xRay Pixy
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JAYA Optimization Algorithm Step-by-Step with Numerical Example
JAYA Algorithm is very simple and new optimization algorithm used for solving constrained and unconstrained optimization problems.
Jaya: An Advanced Optimization Algorithm.
JAYA Algorithm is very simple and new optimization algorithm used for solving constrained and unconstrained optimization problems. It is Simple, Unique and Powerful Optimization Algorithm.
JAYA algorithm is introduced by R.V. Rao in 2016. JAYA is a SANSKRIT word it means VICTORY. That's why this algorithm always tries to get closer to the source (i.e., reaching the BEST Solution) and at the same time tries to avoid the WORST Solution. All values at the end of iteration are maintained and these values become the INPUT to the next iteration. JAYA Algorithm is simpler than TLBO (Teaching Learning Based Optimization) algorithm. Author also compared this algorithm with latest approaches and found JAYA algorithm at RANK 01 for BEST and MEAN solution for different Constrained Problems.
JAYA Algorithm KEY CONCEPT: It is based on the concept that solution obtained for any given problem should move towards the BEST Solution and should avoid WORST Solution.
ADVANTAGES: Using JAYA optimization algorithm we can solve different Engineering Design Problems, Constrained and Unconstrained optimization problems. We can use this algorithm in different research areas such as: Economic Load Dispatch Problems, Optimal Power Flow Solution, In Linear Power System (to find interconnection), In Modern Matching Process, and Optimization Heat Exchanger.
JAYA Algorithm : PSEUDOCODE
Initialize Parameters [JAYA Algorithm, Optimization Problem
Initialize Population Size (N) Randomly.
Calcuate Fitness Values for each candidate.
Sort the Population (Best and Worst Soltuion respectively).
Set Current_Iteration = 1
Check While (Current_Iteration <= Maximum_Iteration)
For i = 1,2,...,N do
For j = 1,2,...,D do
Set r1, r2 [0,1].
Using Equation update values for each candidate.
End For
If (New Solution)<=(Old Solution)
Update Solution.
End If
End For
Current_Iteration = Current_Iteration + 1.
End While
JAYA ALGORITHM Numerical Example
Step 01: Initialize the Algorithm Parameters.
Suppose, Population Size = 05;
Design Variable = 02;
Maximum Iteration = 06;
LB = -100, UB =100;
Step 02: Initialize Population for 5 individuals.
Step 03: Using Cost Function Calculate the Fitness values for each individual.
Here, sphere function is used for the Cost Calculation.
Step 04: Select Best and Worst Solution in current population.
Step 05: Update Current Solution [Position, Fitness Values].
Step 06: Compare New Solution with Old Solution and Replace if New solutions are better else no change.
Step 07: Find out Best and Worst Solution among all.
Step 08: Increment counter. If stopping criteria is not satisfy repeat loop.
Step 09: Display Best Solution obtained.
Swarm Intelligence based Population-based MetaheuristicsWATCH NOW!
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Particle swarm optimization (PSO) What is meant by PSO? PSO is a computational method that Optimizes a problem. It is a Population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. n 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. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration. How PSO will optimize? By Improving a Candidate Solution. How PSO Solve Problems? PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). The population of Candidate Solutions (i.e., Particles). What is Search Space in PSO? It is the range in which the algorithm computes th...
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