Tuning Optimization Algorithms Under Multiple Objective Function Evaluation Budgets
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.