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 (CPVs) 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 paper presents a new algorithm tuning multiobjective particle swarm optimization (tMOPSO) for tuning the CPVs of stochastic optimization algorithms under a range of OFE budget constraints. Specifically, for a given problem tMOPSO aims to determine multiple groups of CPVs, each of which results in optimal performance at a different OFE budget. To achieve this, the control parameter tuning problem is formulated as a multiobjective optimization problem. Additionally, tMOPSO uses a noise-handling strategy and CPV 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.