The sensitivity of single objective optimization algorithm control parameter values under different computational constraints

When solving a single objective optimization problem, a user desires an accurate solution, but may be computationally constrained in terms of the number of objective function evaluations (OFEs) that can be afforded. The OFE budget is application specific, varying depending on the time, computing resources, and the nature of the optimization problem. Control parameter value sensitivity to this OFE budget constraint is investigated for the particle swarm- and differential evolution optimization algorithms. The algorithms are tuned to selected testing problems under different OFE budget constraints, and then their performance is assessed at different OFE budgets from what they were tuned for. The results give evidence that combinations of optimization algorithm control parameter values which perform well for high OFE budgets do not perform well for low OFE budgets and vice versa. This indicates that when selecting control parameter values for these two algorithms, not only should the optimization problem characteristics be taken into account, but also the computational constraints.