Mode Pursuing Sampling Method for Multidisciplinary Deisgn Optimization in Ship Conceptual Design

The design of complex engineering systems often involves several coupled disciplines or subsystems. Multidisciplinary design optimization (MDO) is an effective design methodology for complex systems by fully exploring the interaction of subsystems. Generally, numerous computation-intensive black-box functions exist in the MDO problem. Mode pursuing sampling (MPS) method is an efficient global optimization method developed for the expensive black-box functions. In this work, the MPS method is successfully applied to solve a ship MDO problem in conceptual design. The results indicate that the MPS method is capable of efficiently addressing MDO problems.

[1]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[2]  Fred Glover,et al.  Tabu Search - Part II , 1989, INFORMS J. Comput..

[3]  P. Groenen,et al.  The tunneling method for global optimization in multidimensional scaling , 1996 .

[4]  G. G. Wang,et al.  Mode Pursuing Sampling Method for Discrete Variable Optimization on Expensive Black-Box Functions , 2008 .

[5]  Soonhung Han,et al.  A method and tool for human-human interaction and instant collaboration in CSCW-based CAD , 2006, Comput. Ind..

[6]  Layne T. Watson,et al.  Aircraft Design Markup Language for Multidisciplinary Aircraft Design and Analysis , 2015, J. Aerosp. Inf. Syst..

[7]  Jaroslaw Sobieszczanski-Sobieski,et al.  Multidisciplinary aerospace design optimization - Survey of recent developments , 1996 .

[8]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[9]  Marcus A. Lobbia.  Multidisciplinary Design Optimization of Waverider-Derived Crew Reentry Vehicles , 2017 .

[10]  Adrian E. Raftery,et al.  How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..

[11]  G. G. Wang,et al.  Mode-pursuing sampling method for global optimization on expensive black-box functions , 2004 .

[12]  Charlie C. L. Wang,et al.  Real-Time Collaborative Design With Heterogeneous CAD Systems Based on Neutral Modeling Commands , 2007, J. Comput. Inf. Sci. Eng..

[13]  Valerio Lattarulo,et al.  Multidisciplinary Optimization Under High-Dimensional Uncertainty for Small Satellite System Design , 2016 .

[14]  Yoke San Wong,et al.  Collaborative computer-aided design - research and development status , 2005, Comput. Aided Des..

[15]  David S. Goodsell,et al.  Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function , 1998 .

[16]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[17]  Keinosuke Fukunaga,et al.  A Branch and Bound Algorithm for Computing k-Nearest Neighbors , 1975, IEEE Transactions on Computers.

[18]  Michael G. Parsons,et al.  Formulation of Multicriterion Design Optimization Problems for Solution With Scalar Numerical Optimization Methods , 2004 .

[19]  Fred W. Glover,et al.  Tabu Search - Part I , 1989, INFORMS J. Comput..