Particle Swarm Optimization

Particle Swarm Optimization (PSO) is patterned to replicate population stochastic algorithms observed in animals such as birds and fish. It is a population-based algorithm leveraging a swarm of candidate solutions or particles to explore the solution space. Each particle adjusts its position based on personal and global best-known positions, making it an adaptive search process. Its features include swarm intelligence, where particles move using mathematical equations that consider their best-known positions.

PSO has seen advancements like hybrid algorithms for improved performance and adaptability, parallel computing implementations for handling high-dimensional problems, and the integration of other optimization techniques. Industries such as engineering, finance, biotechnology, robotics, and artificial intelligence have effectively used it in applications like portfolio optimization, genetic network modeling, path planning, and training neural networks.

The math is super cool. Also, since PSO algorithms can be used to optimize neural network, random forest, and many other models, it will be featured from time to time in my real world data coding examples.

For now, here are a few great PSO resource I recommend checking out:

• https://machinelearningmastery.com/a-gentle-introduction-to-particle-swarm-optimization/
• https://www.youtube.com/watch?v=8xycqWWqz50&list=PLZgdMIFoNTxn8sH7ldSpS-e7bFGn1jiDC
• https://www.youtube.com/watch?v=JhgDMAm-imI 
• https://www.youtube.com/watch?v=HmDjfL3R39M&list=PLVLAu9B7VtkalMRDfFRT_Bzf45VE3OGBF