Particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle's position and velocity. Each particle's movement is influenced by its local best known position but, is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions.
This code is based on the original PSO code in "file exchange" by Wesam Elshamy.
Reza Ahmadzadeh (2023). Particle Swarm Optimization (Vectorized Code) (https://www.mathworks.com/matlabcentral/fileexchange/46985-particle-swarm-optimization-vectorized-code), MATLAB Central File Exchange. Retrieved .
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Inspired by: Particle Swarm Optimization Simulation
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