PARTICLE SWARM OPTIMIZATION (PSO) MATLAB CODE EXPLANATION

Particle Swarm Optimization is one of the most important algorithms used in modern data analysis and mathematical programming. This algorithm aims to find the best solution or feasible solution for given optimization problems. The basic idea is that you keep a set of similar differentiable functions placed in a cluster, and then you try to optimize the objective function until it converges to a solution.

PSO problem seeks to find the most efficient way to screen through scenarios that occur in the real world, to quickly find the most efficient path for a specific set of business challenges. Optimization algorithms are used for industrial applications in the field of AI.

For different optimization problems, there are different parameters

1. Swarm size

2. Iteration number

3. Velocity components

4. Acceleration Coefficients

The inertial weight greatly impacts the PSO algorithm code in the convergence of optimization. This factor maintains the particle/swarms inertial motion and redirects the change of particle velocity. PSO code correction factor is a suitable approach to ensure convergence of the Matlab particle swarm optimization. Let’s demonstrate the PSO code in Matlab

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https://simulationtutor.com/particle-swarm-optimization-pso-matlab-code-explanation/


PSO Explanation Audio Version: IEEE 69 Bus System load flow - 8 October - 2021
https://youtu.be/T93_wNfNZyE

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