Many objective visual analytics: Rethinking the design of complex engineered systems

Structural and Multidisciplinary Optimization

Volumn 48, Issue 1, 2013, Pages 201-219

  Woodruff, Matthew J ^a   Reed, Patrick M ^a   Simpson, Timothy W ^a  

^a The Pennsylvania State University   (United States)

Author keywords

Multi objective optimization; Multidimensional data visualization; Product family design

Indexed keywords

COMPLEX ENGINEERED SYSTEMS; COMPUTATIONAL CHALLENGES; GENERAL AVIATION; MULTI-DIMENSIONAL DATA VISUALIZATION; PROBLEM FORMULATION; PROBLEM REFORMULATIONS; PRODUCT FAMILIES; PRODUCT FAMILY DESIGN;

COGNITIVE SYSTEMS; DATA VISUALIZATION; GALLIUM ALLOYS; MULTIOBJECTIVE OPTIMIZATION; VISUALIZATION;

PRODUCT DESIGN;

EID: 84879695508     PISSN: 1615147X     EISSN: 16151488     Source Type: Journal    
DOI: 10.1007/s00158-013-0891-z     Document Type: Article

References (76)

1. Arrow K (1950) A difficulty in the concept of social welfare. J Polit Econ 58(4):328-346
2. Balling R (1999) Design by shopping: a new paradigm? In: Proceedings of the Third World Congress of structural and multidisciplinary optimization (WCSMO-3), pp 295-297
3. Balling RJ, Taber JT, Brown MR, Day K (1999) Multiobjective urban planning using genetic algorithms. J Urban Plan Dev 125(2):86-99
4. Bloebaum C, McGowan A-M (2010) Design of complex engineered systems. J Mech Des 132(12):120301-1-120301-2
5. Brill ED, Flach JM, Hopkins LD, Ranjithan S (1990) MGA: a decision support system for complex, incompletely defined problems. IEEE Trans Syst Man Cybern 20(4):745-757
6. Brockhoff D, Friedrich T, Hebbinghaus N, Klein C, Neumann F, Zitzler E (2007) Do additional objectives make a problem harder? In: Genetic and evolutionary computation conference (GECCO '07), London, England, pp 765-772
7. Chen W, Elliot JG, Simpson TW, Virasak J (1995) Designing a general aviation aircraft as an open engineering system. Design Report for ME8104, Georgia Institute of Technology
8. Climaco J (2004) A critical reflection on optimal decision. Eur J Oper Res 153(2):506-516
9. Coello Coello CA, Van Veldhuizen DA, Lamont GB (2002) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Kluwer Academic Publishers. doi: 10.1007/978-0-387-36797-2
10. Deb K, Agrawal RB (1994) Simulated binary crossover for continuous search space. Tech. Rep. Technical Report IITK/ME/SMD-94027, Indian Institute of Technology, Kanpur, Kanpur, UP, India
11. Deb K, Joshi D, Anand A (2002) Real-coded evolutionary algorithms with parent-centric re-combination. In: Proceedings of the World on Congress on computational intelligence, vol 1, pp 61-66
12. Deb K, Mohan M, Mishra S (2005) Evaluating the \varepsilon-domination based multiobjective evolutionary algorithm for a quick computation of pareto-optimal solutions. Evol Comput J 13(4):501-525
13. Di Pierro F, Khu S-T, Savi DA (2007) An investigation on preference order ranking scheme for multiobjective evolutionary optimization. IEEE Trans Evol Comput 11(1):17-45. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm? arnumber=4079613
14. Eddy J, Lewis K (2002) Visualization of multi-dimensional design and optimization data using cloud visualization In: ASME design technical conferences, design automation conference. Paper No. DETC2002/DAC-34130
15. English K, Bloebaum C (2008) Visual dependency structure matrix for multidisciplinary design optimization tradeoff studies. J Aerosp Comput Inf Commun 5(1):274-297
16. ESTECO SpA (2012) modeFrontier. http://www.modefrontier.com/. Accessed 5 Apr 2012
17. Ferringer M, Spencer D, Reed P (2009) Many-objective reconfiguration of operational satellite constellations with the large-cluster epsilon non-dominated sorting genetic algorithm-ii. In: Proceedings of the 2009 IEEE congress on evolutionary computation. IEEE, pp 340-349
18. Fleming PJ, Purshouse RC, Lygoe RJ (2005) Many-objective optimization: an engineering design perspective. In: In evolutionary multi-criterion optimization. Lecture notes in computer science, vol 3410. Springer, Berlin/Heidelberg, pp 14-32
19. Fonseca CM, Fleming PJ (1998) Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation. IEEE Trans Syst, Man Cybern, A: Syst Humans 28(1):26-37
20. Franssen M (2005) Arrow's theorem, multi-criteria decision problems and multi-attribute preferences in engineering design. Res Eng Des 16(1):42-56
21. Goldberg D (2002) The design of innovation: lessons from and for competent genetic algorithms, vol 7. Springer, New York
22. Hadka D, Reed P (2012a) Borg: an auto-adaptive many-objective evolutionary computing framework. Evol Comput. doi: 10.1162/EVCO-a-00075
23. Hadka D, Reed P (2012b) Diagnostic assessment of search controls and failure modes in many-objective evolutionary optimization. Evol Comput 20(3):423-452
24. Hadka D, Reed PM, Simpson TW (2012) Diagnostic assessment of the borg moea for many-objective product family design problems. In: Proceedings of the 2012 IEEE World Congress on computational intelligence, IEEE, pp 986-995
25. Hitch CJ (1960) On the choice of objectives in systems studies. Tech. Rep. P-1955, The RAND Corporation
26. Hogarth R (1981) Beyond discrete biases: functional and dysfunctional aspects of judgmental heuristics. Psychol Bull 90(2):197-217
27. Inselberg A (1997) Multidimensional detective. In: IEEE symposium on information visualization, 1997. Proceedings, IEEE, pp 100-107
28. Inselberg A (2009) Parallel coordinates: visual multidimensional geometry and its applications. Springer, New York
29. Kanukolanu D, Lewis K, Winer E (2006) A multidimensional visualization interface to aid in trade-off decisions during the solution of coupled subsystems under uncertainty. J Comput Inf Sci Eng 6:288
30. Kasprzyk JR, Reed PM, Kirsch BR, Characklis GW (2009) Managing population and drought risks using many-objective water portfolio planning under uncertainty. Water Resources Research 45:W12401. doi: 10.1029/2009WR008121
31. Kasprzyk JR, Reed PM, Kirsch BR, Characklis GW (2012) Many-objective de novo water supply portfolio planning under deep uncertainty. Environ Model Softw 34:87-104. doi: 10.1016/j.envsoft.2011.04.003
32. Keim DA, Kohlhammer J, Ellis G, Mansmann F (eds) (2010) Mastering the information age - solving problems with visual analytics. Eurographics. http://www.vismaster.eu/wp-content/uploads/2010/11/VisMaster-book-lowres.pdf
33. Kipouros T, Mleczko M, Savill A (2008) Use of parallel-coordinates for post-analyses of multi-objective aerodynamic optimisation in turbomachinery. In: Proceedings of the 4th AIAA multi-disciplinary design optimization specialist conference, Schaumburg, IL. Paper No. AIAA-2008-2138
34. Kita H, Ono I, Kobayashi S (1999) Multi-parental extension of the unimodal normal distribution crossover for real-coded genetic algorithms. In: Proceedings of the 1999 congress on evolutionary computation, pp 1581-1588
35. Kollat J, Reed P (2005) The value of online adaptive search: a performance comparison of NSGAII,\varepsilon-NSGAII and\varepsilon MOEA. In: Coello Coello C, Hernández Aguirre A, Zitzler E (eds) Evolutionary multi-criterion optimization. Lecture notes in computer science, vol 3410. Springer, Berlin/Heidelberg, pp 386-398
36. Kollat JB, Reed P (2007a) A framework for visually interactive decision-making and design using evolutionary multi-objective optimization (VIDEO). Environ Modell Softw 22(12):1691-1704. http://linkinghub.elsevier.com/ retrieve/pii/S1364815207000308
37. Kollat J, Reed P (2007b) A computational scaling analysis of multiobjective evolutionary algorithms in long-term groundwater monitoring applications. Adv Water Resour 30(3):408-419
38. Kollat J, Reed P, Maxwell R (2011) Many-objective groundwater monitoring network design using bias-aware ensemble kalman filtering, evolutionary optimization, and visual analytics. Water Resour Res 47:W02529. doi: 10.1029/2010WR009194
39. Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multiobjective optimization. Evol Comput 10(3):263-282
40. Messac A, Martinez M, Simpson T (2002) A penalty function for product family design using physical programming. ASME J Mech Des 124(2):164-172
41. Miller GA (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81-97
42. Naim A, Chiu P, Bloebaum C, Lewis K (2008) Hyper-radial visualization for multi-objective decision-making support under uncertainty using preference ranges: the PRUF method. In: 12th AIAA/ISSMO multidisciplinary analysis and optimization conference. Paper No. AIAA 2008-6087
43. NASA (1978) GASP - general aviation synthesis program. Tech. Rep. NASA-CR-152303National Aeronautics and Space Administration Ames Research Center, Moffet Field, California
44. NASA, FAA (1994) General aviation design competition guidelines. Virginia Space Grant Consortium, Hampton, VA
45. Nolan D, Thal J, Henry K, Sandy M (1995) NASA/FAA announce aviation design competition winners [press release]
46. R project (2012) The R project for statistical computing. http://www.r-project.org. Accessed 5 Apr 2012
47. Raymer D (1999) Aircraft design: a conceptual approach. American Institute of Aeronautics and Astronautics, Inc., Reston, VA
48. Reed P, Hadka D, Herman J, Kasprzyk J, Kollat J (2013) Evolutionary multiobjective optimization in water resources: the past, present, and future (editor invited submission to 35th anniversary special issue). Adv Water Resour 51:438-456
49. Roy B (1971) Problems and methods with multiple objective functions. Math Program 1(1):239-266
50. Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9-26
51. SAS Institute (2012) JMP statistical discovery software. http://jmp.com. Accessed 5 Apr 2012
52. See T, Gurnani A, Lewis K (2003) An approach to robust multi-attribute concept selection. In: Proceedings of ASME 2003 design engineering technical conferences, ASME. Paper No. DETC2003/DAC-48707
53. Seo J, Shneiderman B (2005) A rank-by-feature framework for interactive exploration of multidimensional data. Inf Vis 4(2):96-113. http://www.palgrave- journals.com/doifinder/10.1057/palgrave.ivs.9500091
54. Shah R, Simpson T, Reed P (2011) Many-objective evolutionary optimisation and visual analytics for product family design. In: Wang L, et al (eds) Multi-objective evolutionary optimisation for product design and manufacturing. Springer-Verlag, London, pp 137-159. doi: 10.1007/978-0-85729-652-8-4
55. Simpson T (1995) Development of a design process for realizing open engineering systems. Master's Thesis, Georgia Institute of Technology
56. Simpson TW, D'Souza B (2004) Assessing variable levels of platform commonality within a product family using a multiobjective genetic algorithm. Concurr Eng: Res Appl 12(2):119-130
57. Simpson T, Martins JRRA (2010) The future of multidisciplinary design optimization (mdo): advancing the design of complex engineered systems. Tech. rep., National Science Foundation, Fort Worth, TX
58. Simpson TW, Martins JRRA (2011) Multidisciplinary design optimization for complex engineered systems: Report from a national science foundation workshop. J Mech Des 133(10):101002. doi: 10.1115/1.4004465. http://link.aip.org/link/? JMD/133/101002/1
59. Simpson TW, Chen W, Allen JK, Mistree F (1996) Conceptual design of a family of products through the use of the robust concept exploration method. In: 6th AIAA/USAF/NASA/ISSMO symposium on multidisciplinary analysis and optimization, AIAA, Bellevue, WA, pp 1535-1545
60. Simpson T, Seepersad C, Mistree F (2001) Balancing commonality and performance within the concurrent design of multiple products in a product family. Concurr Eng 9(3):177-190
61. Slingerland LA, Bobuk A, Simpson TW (2010) Product family optimization using a multidimensional data visualization approach. In: 13th AIAA/ISSMO multidisciplinary analysis and optimization converence, Fort Worth, TX. Paper No. AIAA 2010-9031
62. Storn R, Price K (1997) Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341-359
63. Stump G, Yukish M, Simpson T, Harris E (2003) Design space visualization and its application to a design by shopping paradigm. In: ASME design engineering technical conferences-design automation conference, Chicago, IL, ASME. Paper No. DETC2003/DAC-48785
64. Teytaud O (2006) How entropy-theorems can show that on-line approximating high-dim pareto fronts is too hard. Technical Report, Inria Saclay (CR1)
65. Teytaud O (2007) On the hardness of offline multi-objective optimization. Evol Comput 15(4):475-491
66. Thomas J, Cook K, National Visualization and Analytics Center (2005) Illuminating the path: the research and development agenda for visual analytics. [Book]. IEEE Computer Society
67. TIBCO (2012) Spotfire. http://spotfire.tibco.com. Accessed 5 Apr 2012
68. Tsoukiàs A (2008) From decision theory to decision aiding methodology. Eur J Oper Res 187:138-161
69. Tsutsui S, Yamamura M, Higuchi T (1999) Multi-parent recombination with simplex crossover in real coded genetic algorithms. In: Genetic and evolutionary computation conference (GECCO 1999)
70. Venkataraman S, Haftka RT (2004) Structural optimization complexity: what has moore's law done for us? Struct Multidisc Optim 28:275-287
71. Vrugt J, Robinson B (2007) Improved evolutionary optimization from genetically adaptive multimethod search. Proc Natl Acad Sci USA 104(3):708
72. Ware C (2004) Information visualization: perception for design, 2nd edn. Morgan-Kauffman
73. Winer E, Bloebaum C (2002) Development of visual design steering as an aid in large-scale multidisciplinary design optimization. part i: method development. Struct Multidisc Optim 23(6):412-424
74. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67-82
75. Woodruff M, Hadka D, Reed P, Simpson T (2012) Auto-adaptive search capabilities of the new borg MOEA: a detailed comparison on alternative product family design problem formulations. In: 14th AIAA/ISSMO multidisciplinary analysis and optimization conference, Indianapolis, IA, USA, 17 September 2012
76. Zeleny M (1986) Optimal system design with multiple criteria: de novo programming approach. Eng Costs Prod Econ 10(1):89-94