By Ryan D. Moniz
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The Particle Swarm Optimization (PSO) algorithm is classified as a population-based stochastic optimization technique. A set of potential solutions are evolved to approach an approximate solution (or set of solutions) for a problem. With PSO being an optimization strategy, the aim is to discover the global optimum that satisfies the constraints of the fitness criteria defined in the given search space for the problem. This application demonstrates how the Particle Swarm Optimization (PSO) algorithm works through the use of 2D visualization. By selecting and moving the large orange sphere in the application, blue particles will continually swarm towards it. Users can also alter the behaviour of the swarm by specifying the global best (gBest), personal best (pBest), and delta-time parameters and view their effects.