Particle Swarm Optimization and Quantum Particle Swarm Optimization to Multidimensional Function Approximation

This work compares the results of multidimensional
function approximation using two algorithms: the classical Particle
Swarm Optimization (PSO) and the Quantum Particle Swarm
Optimization (QPSO). These algorithms were both tested on three
functions - The Rosenbrock, the Rastrigin, and the sphere functions
- with different characteristics by increasing their number of
dimensions. As a result, this study shows that the higher the function
space, i.e. the larger the function dimension, the more evident the
advantages of using the QPSO method compared to the PSO method
in terms of performance and number of necessary iterations to reach
the stop criterion.

Authors:

• Diogo Silva
• Fadul Rodor
• Carlos Moraes

Keywords:

• PSO
• QPSO
• function approximation
• AI
• optimization
• multidimensional functions.

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