A universal prototype design framework of the stem and stern contours of hull surface and the self-adaptive solving strategy

Abstract

Ship geometry reconstruction technology is a key technique and research focus in the ship design. And the optimization algorithm used in the ship design is always a hot research area. To establish the control points’ distribution model database of characteristic curves, based on the NURBS control mesh of the hull surface, an approach to approximate the initial stem and stern contours of hull form modeling using the concept of parametric generation is proposed. The optimization model is constructed with the homogeneous coordinate component of the fitted contour’s control points as the design variables. The objective function is set to minimize the maximum relative difference among the longitudinal coordinates of the approximated contour and the original ones corresponding to the same drafts. The appropriate constraints are set according to the characteristics of the contours and the geometrical characteristic of NURBS. Considering a number of empirical knowledge existing in the process of ship design, and the conventional quantum-behaved particle swarm optimization (QPSO) algorithm depending upon good initial population, the immune quantum-behaved particle swarm optimization (IQPSO) is specifically designed and used to solve this complex nonlinear constrained optimization problem through the immune operator of the immune genetic algorithm combining with the QPSO algorithm. Meanwhile, the self-adaptive constraint evolution strategy is proposed to improve the IQPSO algorithm. The simulation results of the full-scale ship’s contours demonstrate the feasibility and effectiveness of the proposed algorithm and method.