Application of Multi-Objective Optimization Algorithm Based on Physical Programming in Ship Conceptual Design

Abstract:

Ship optimization design is a typical multi-objective problem. The multi-objective optimization algorithm based on physical programming is able to obtain evenly distributed Pareto front. But the number of Pareto solutions and the search positions of pseudo-preference structures still exit some disadvantages that are improved in this paper. Firstly uniform design for mixture experiments is used to arbitrarily set the number of Pareto solutions and evenly distribute the search positions of pseudo-preference structures. Then the objective space is searched by shrinking of search domain and rotation of pseudo-preference structure technology. The optimization quality is able to be improved. Finally, the improved multi-objective optimization algorithm is applied to ship conceptual design optimization and compared with the multi-objective evolutionary algorithm to verify the effectiveness of the improved algorithm.

• Periodical: Advanced Materials Research (Volume 904)
• Pages: 408-413
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.904.408
• Online since: March 2014
• Authors: Zhai Liu Hao*, Zu Yuan Liu, Bai Wei Feng
• Keywords: Conceptual Design, Even Distribution, Multi-Objective Optimization (MOP), Pareto Front, Physical Programming (PP)

References

[1] ACHILLE MESSAC, CHRISTOPHER A. MATTSON, Generating Well-Distributed Sets of Pareto Points for Engineering Design Using Physical Programming, Optimization and Engineering. Vol. 3 (2002), 431-450.

[2] R.T. Marler, J.S. Arora, Survey of multi-objective optimization methods for engineering, Struct Multidisc Optim. Vol. 26 (2004), 369-395.

[3] Wang Yun-liang, Evolutionary Algorithm and Its Application in Aircraft Conceptual Design, China: Northwestern Polytechnical University, (2006).

[4] S.V. Utyuzhnikov, P. Fantini, M.D. Guenov, A method for generating a well-distributed Pareto set in nonlinear multiobjective optimization, Journal of Computational and Applied Mathematics. Vol. 223 (2009), 820-841.

DOI: 10.1016/j.cam.2008.03.011

[5] Hamid Hefazi, Igor Mizine, Adeline Schmitz, Stephen Klomparens, Stephen Wiley, Multidisciplinary Synthesis Optimization Process in Multihull Ship Design, Naval Engineers Journal. Vol. 122 (2010) No. 3, 29-47.

DOI: 10.1111/j.1559-3584.2010.00240.x

[6] Apostolos D. Papanikolaou, Holistic Ship Design Optimization: Merchant and Naval Ships, Ship Science & Technology. Vol. 5 (2011) No. 9, 9-26.

DOI: 10.25043/19098642.48

[7] Mark A. Gammon, Optimization of fishing vessels using a Multi-Objective Genetic Algorithm, Ocean Engineering. Vol. 38 (2011), 1054-1064.

DOI: 10.1016/j.oceaneng.2011.03.001

[8] Matteo Diez, Daniele Peri, Robust optimization for ship conceptual design, Ocean Engineering. Vol. 37 (2010), 966-977.

DOI: 10.1016/j.oceaneng.2010.03.010

[9] Messac A, Hattis P D, High Speed Civil Transport (HSCT) Plane Design Using Physical Programming, AIAA/ASME/ASCE/AHS Structures, Structural Dynamics & Materials Conference. Vol. 4 (1995) No. 3, 2018-(2031).

DOI: 10.2514/6.1995-1401

[10] Fang, K.T., Ma, C.X. Orthogonal and Uniform Experimental Designs, Science Press, Beijing, (2001).

[11] Ning Jianhui, Uniform Designs for Mixture Experiments, China: School of Mathematics and Statistics Central China Normal University, (2008).

[12] Christopher Gregory Hart, MULTIDISCIPLINARY DESIGN OPTIMIZATION OF COMPLEX ENGINEERING SYSTEMS FOR COST ASSESSMENT UNDER UNCERTAINTY, USA: The University of Michigan, (2010).