Particle Swarm Optimization algorithm is an evolutionary, Bio-inspired, Swarm-intelligence-based algorithm that simulates the collective behavior of a swarm of insects/animals, in searching for food. It was first developed by Eberhart and Kennedy in 1995, and since then, it has been modified and enhanced to fit a wide range of engineering and scientific problems, therefore there are many variants of PSO algorithm. However, Standard PSO algorithm is still the origin from which all variants have been developed.
In this code I have implemented Standard PSO algorithm in a clear and simple script, and applied it on Eggcrate function, which is a widely known benchmark function used for validation of Global Optimization algorithms.
The user can determine the inertia, Cognitive and Social coefficients, number of iterations, number of particles and initial velocity of particles, as well as determine the plot type as Surf or Contour.
Haydar Khayou (2021). Particle Swarm Optimization (https://www.mathworks.com/matlabcentral/fileexchange/77119-particle-swarm-optimization), MATLAB Central File Exchange. Retrieved .
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