Multi-objective optimization of semi-submersible platforms based on a support vector machine with grid search optimized mixed kernels surrogate model

Abstract

Determination of optimal hull configurations in the semi-submersible platform (SEMI) should account for several objectives. These objectives are pertinent to hydrodynamic performances of SEMI under wave action but also total structure cost. They are often contradictory and cannot achieve the minimum simultaneously. Hence, a group of relative optimal and balanced solutions is introduced as optimization results, called Pareto-optimal solutions. This paper presents a surrogate-assisted technique to seek the optimal configuration of SEMI for minimal heave and roll response and the lightest weight. Design variables samples are generated by means of multidimensional Ladin hypercube design, and then these inputs are employed for hydrodynamic simulation to acquire the response data. To determine the relationship between objectives and hull structure size, Support Vector Machine with Grid Search optimized mixed kernels (SVM-GSM) is constructed as a surrogate model, and triple verification in terms of errors and robustness warrants its reliability. Three categories of Pareto optimal solutions are obtained by Non-dominated Sorting Genetic Algorithm II (NSGA-II), which correspond to three optimal goals. For optimization results, a presented comprehensive verification approach integrates frequency-domain analysis (FD), time-domain analysis (TD), convergence analysis, and main factors screening. This combination renders sufficient and reliable validation to optimization results. Results from FD and TD for SEMI indicate that the optimized effect of Pareto solutions is satisfactory. Besides, the main influence factors in design variables for hydrodynamic response are screened and investigated. Finally, the ranking of the influence degree of each variable is obtained and evaluated. The proposed framework in this paper provides a comprehensive validation idea for the construction of the surrogate model and optimization results for SEMI hull structure optimization.