Abstract:
Most sensitivity analysis studies of optimization algorithm control parameters are restricted to a single
objective function evaluation (OFE) budget. This restriction is problematic because the optimality of
control parameter values is dependent not only on the problem’s fitness landscape, but also on the OFE
budget available to explore that landscape. Therefore the OFE budget needs to be taken into consideration
when performing control parameter tuning. This article presents a new algorithm (tMOPSO) for
tuning the control parameter values of stochastic optimization algorithms under a range of OFE budget
constraints. Specifically, for a given problem tMOPSO aims to determine multiple groups of control parameter
values, each of which results in optimal performance at a different OFE budget. To achieve this,
the control parameter tuning problem is formulated as a multi-objective optimization problem. Additionally,
tMOPSO uses a noise-handling strategy and control parameter value assessment procedure, which
are specialized for tuning stochastic optimization algorithms. Conducted numerical experiments provide
evidence that tMOPSO is effective at tuning under multiple OFE budget constraints.
