title: MOTA: A Many-Objective Tuning Algorithm Specialized for Tuning under Multiple Objective Function Evaluation Budgets.
abstract: Control parameter studies assist practitioners to select optimization algorithm parameter values that are appropriate for the problem at hand. Parameter values are well suited to a problem if they result in a search that 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 consists of four aspects: (1) a tuning problem formulation that consists of both a speed objective and a speed decision variable; (2) a control parameter tuple assessment procedure that 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 differential evolution operators to search for effective control parameter values. Numerical experiments consisting of tuning NSGA-II and MOEA/D demonstrate that MOTA is effective at many-objective tuning.
authors.affiliation: Department of Mechanical and Aeronautical Engineering, University of Pretoria, South Africa antoine.dymond@gmail.com.
authors.forename: Antoine S
authors.initials: AS
authors.lastname: Dymond
authors.affiliation: Department of Mechanical and Aeronautical Engineering, University of Pretoria, South Africa schalk.kok@up.ac.za.
authors.forename: Schalk
authors.initials: S
authors.lastname: Kok
authors.affiliation: Department of Mechanical and Aeronautical Engineering, University of Pretoria, South Africa stephan.heyns@up.ac.za.
authors.forename: P Stephan
authors.initials: PS
authors.lastname: Heyns
mesh_terms: D000465:Algorithms; D005075:Biological Evolution; D003198:Computer Simulation; D008962:Models, Theoretical; D013269:Stochastic Processes
keywords: Tuning; many-objective optimization; multiobjective optimization; objective function evaluation budgets.