Abstract:
Control parameter studies assist practitioners to select optimization algorithm parameter values which are appropriate
for the problem at hand. Parameters values are well-suited to a problem if they result in a search which is
effective given that problem’s objective function(s), constraints and termination criteria. Given these considerations
a many objective tuning algorithm named MOTA is presented. MOTA is specialized for tuning a stochastic optimization
algorithm according to multiple performance measures each over a range of objective function evaluation
budgets. MOTA’s specialization consist of four aspects; 1) a tuning problem formulation which consists of both a
speed objective and a speed decision variable, 2) a control parameter tuple assessment procedure which utilizes information
from a single assessment run’s history to gauge that tuple’s performance at multiple evaluation budgets,
3) a preemptively terminating resampling strategy for handling the noise present when tuning stochastic algorithms,
and 4) the use of bi-objective decomposition to assist in many objective optimization. MOTA combines these aspects
together with DE operators to search for effective control parameter values. Numerical experiments which consisted
of tuning NSGA-II and MOEA/D demonstrate that MOTA is effective at many objective tuning.
