MOTA : a many-objective tuning algorithm specialized for tuning undermultiple objective function evaluation budgets

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.