In the ship hull optimization design based on simulation-based design (SBD) technology, low precision
of the approximate model leads to an uncertainty form of optimization model. In order to enable the approximate
model with finite precision to maximize the effectiveness, uncertainty optimization method is introduced here.
Wave resistance coefficient approximation model, built by back propagation (BP) neural network, is represented
as a form of interval. Afterwards, a minimum resistance optimization model is established with the design space
constituted by principal dimensions and ship form coefficients. Double-level nested optimization architecture is
proposed: for outer layer, improved particle swarm optimization (IPSO) algorithm with learning factor improvement
strategy is used to generate design variables, and for inner layer, modified very fast simulated annealing
(MVFSA) algorithm is used to solve the objective function interval with uncertainty region. Cases calculation
proves the effectiveness and superiority of uncertainty optimization method for ship hull SBD optimization design,
thus providing a good way for finding optimal designs.
HOU Yuanhang* (候远杭), YOU Yuan (游园), LIANG Xiao (梁霄)
. Minimum Resistance Ship Hull Uncertainty Optimization Design Based on Simulation-Based Design Method[J]. Journal of Shanghai Jiaotong University(Science), 2017
, 22(6)
: 657
-663
.
DOI: 10.1007/s12204-017-1882-9
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