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The Effect of Probability Distributions on the Performance of Quantum Particle Swarm Optimization for Solving Dynamic Optimization Problems

The quantum particle swarm optimization (QPSO) algorithm was developed to address the limitations of the traditional particle swarm optimization (PSO) algorithm in dynamic environments. Some particles in the QPSO algorithm are chosen as "quantum" particles, and the positions of these are sampled uniformly within a radius (i.e., A hyper sphere) centred around the global best particle. The remainder of particles follow standard PSO behaviour. This paper proposes sampling various alternative probability distributions to update the positions of quantum particles. Ten probability distributions are examined on dynamic environments with varying dimensionalities, temporal change severities, and spatial change severities, with both single-peak and five-peak environments considered. Results indicated that the most effective distribution to use is dependent upon the type of dynamism present. In general, it was observed that a small quantum radius was preferable to a large radius, indicating that exploitation is more beneficial than exploration with regards to QPSO performance. Finally, despite having been commonly used in various QPSO applications, the performance of the uniform distribution was found to be sub-par.