tag:blogger.com,1999:blog-6568604983751690472.post7668042336200440906..comments2024-09-14T19:22:28.560+10:00Comments on Jayasekara Blog: Machine Learning - Particle Swarm Optimization (PSO) and TwitterDilan Jayasekarahttp://www.blogger.com/profile/01664583272448485349noreply@blogger.comBlogger1125tag:blogger.com,1999:blog-6568604983751690472.post-1495824260730303702024-07-18T14:24:05.705+10:002024-07-18T14:24:05.705+10:00Particle Swarm Optimization (PSO) is a powerful co...Particle Swarm Optimization (PSO) is a powerful computational optimization technique inspired by the collective behavior of social creatures like swarming birds or schools of fish. In machine learning, PSO is used to find optimal solutions for various problems, particularly when dealing with complex, multi-dimensional functions.<br /><br />Here's how PSO works in machine learning:<br /><br />Particle Swarm: Imagine a swarm of particles representing potential solutions to your problem. Each particle has a position in the solution space and a velocity that determines its movement.<br />Fitness Function: Every position in the solution space is evaluated using a fitness function that measures how good a solution it is for your problem (e.g., minimizing error in a classification task).<br /><br /><a href="http://projectcentersinchennai.co.in/Final-Year-Projects-for-CSE/Final-Year-Projects-for-CSE-Machine-learning-Domain" title="Machine Learning Final Year Projects" rel="nofollow">Machine Learning Final Year Projects </a><br /><br /><a href="http://projectcentersinchennai.co.in/Final-Year-Projects-for-CSE/Final-Year-Projects-for-CSE-Deep-learning-Domain" title="Deep Learning Projects for Final Year Students" rel="nofollow">Deep Learning Projects for Final Year Students</a><br /><br />Personal Best: Each particle keeps track of its own best-known position (pBest) based on the fitness function. This represents the best solution it has encountered so far.<br />Global Best: The entire swarm also tracks the global best position (gBest), which is the best position discovered by any particle in the swarm. This allows particles to learn from each other and explore promising areas of the solution space.<br />Velocity Update: Based on its current position, pBest, and gBest, each particle updates its velocity. This update considers how close the particle is to its own best solution and the swarm's best solution, encouraging movement towards optimal regions.<br />Position Update: Using the updated velocity, each particle updates its position in the solution space. This iterative process continues until a stopping criterion is met (e.g., a certain number of iterations or reaching a sufficiently good solution).Vale Co Xeniahttps://www.blogger.com/profile/11880818841213921783noreply@blogger.com