Abstract:
Particle swarm optimization algorithms have been successfully applied to discrete-
valued optimization problems. However, in many cases the algorithms have been tailored
specifically for the problem at hand. This paper proposes a generic set-based particle
swarm optimization algorithm for use on discrete-valued optimization problems that can
be formulated as set-based problems. A detailed sensitivity analysis of the parameters of
the algorithm is conducted. The performance of the proposed algorithm is then compared
against three other discrete particle swarm optimization algorithms from literature using the
multidimensional knapsack problem, and is shown to statistically outperform the existing
algorithms.
