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

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Peer-Reviewed Research
  • SDG 13
  • 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.