Multiobjective Particle Swarm Optimization of a Planing Craft with Uncertainty

Abstract

Uncertainty exists in many of the design variables and system parameters for planing craft. This is especially true in the early stages of design. For this reason, and others, optimization of a craft's performance characteristics is often delayed until later in the design process, after uncertainties have been at least partially resolved. However, delaying optimization can also limit its potential, because freedom to make changes to a design is also highly limited in the later stages. This article demonstrates how uncertainty can be directly incorporated into optimization using particle swarm. A simple synthesis model for a planing craft is built, and a deterministic Pareto front of optimal solutions is found, minimizing two objectives: drag and vertical acceleration at the center of gravity. The craft's weight is then modeled as a normally distributed random variable and sampling methods are used to quantify the uncertainty in the estimated drag for points along the Pareto front. Preliminary results reveal that drag uncertainty is not constant along the Pareto front, presenting useful tradeoff information for designers and decision-makers.

ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints

“…In order to design a product using computational methods, the product needs to be represented in a way that a computer can understand. For ship design, the two most popular modes are parameterized vectors [1,[4][5][6][7][8][9][10][11][12], and free-form deformation techniques [13][14][15][16][17][18]. The benefit of using parameterized design representations for a hull is that the design is defined by a set of tunable parameters that both human designers and computers can interpret.…”

“…This can also lead to greater revenue through a ship's operation. MaxBox is not on a logarithmic scale like the other measures, and it is calculated with Equation (10).…”

Design Improvement for Complex Systems with Uncertainty

“…. , 1.0000), R low ck = 0.9995, R up ck = 0.9997, K = {2, 3,4,5,10,11,12,13,15,16,17,18,22,23,26,28,30,31,32,35,36,37,38,39,42,43,45,51,55,56,57,59,63,65,66,68,69,71,72,73, 82, 83}, f c = 0.9900 is the reliability threshold, and f (R) is the system reliability function which is evaluated by the GO method in [45]. Note that the volume here is replaced by the log-volume (logarithmic transformation of the volume) in order to calculate conveniently.…”

“…Besides, swarm intelligence-based algorithms are frequently-used techniques in design optimization problems [17][18][19][20]. The uncertainty is directly incorporated into optimization using particle swarm, and a deterministic Pareto front of optimal designs is found in [17].…”

“…Besides, swarm intelligence-based algorithms are frequently-used techniques in design optimization problems [17][18][19][20]. The uncertainty is directly incorporated into optimization using particle swarm, and a deterministic Pareto front of optimal designs is found in [17]. The ant colony optimization is combined with reliability-based design optimization to achieve the global optimal design of a crane metallic structure under parametric uncertainty in [18].…”