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

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Abstract

Many cognitive and computational challenges accompany the design of complex engineered systems. This study proposes the many-objective visual analytics (MOVA) framework as a new approach to the design of complex engineered systems. MOVA emphasizes learning through problem reformulation, enabled by visual analytics and many-objective search. This study demonstrates insights gained by evolving the formulation of a General Aviation Aircraft (GAA) product family design problem. This problem’s considerable complexity and difficulty, along with a history encompassing several formulations, make it well-suited to demonstrate the MOVA framework. The MOVA framework results compare a single objective, a two objective, and a ten objective formulation for optimizing the GAA product family. Highly interactive visual analytics are exploited to demonstrate how decision biases can arise for lower dimensional, highly aggregated problem formulations.

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References

  • Arrow K (1950) A difficulty in the concept of social welfare. J Polit Econ 58(4):328–346

    Article  Google Scholar 

  • 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

  • Balling RJ, Taber JT, Brown MR, Day K (1999) Multiobjective urban planning using genetic algorithms. J Urban Plan Dev 125(2):86–99

    Article  Google Scholar 

  • Bloebaum C, McGowan A-M (2010) Design of complex engineered systems. J Mech Des 132(12):120301–1–120301–2

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

  • 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

  • Climaco J (2004) A critical reflection on optimal decision. Eur J Oper Res 153(2):506–516

    Article  MATH  Google Scholar 

  • 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

  • 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

  • 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

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

  • English K, Bloebaum C (2008) Visual dependency structure matrix for multidisciplinary design optimization tradeoff studies. J Aerosp Comput Inf Commun 5(1):274–297

    Article  Google Scholar 

  • ESTECO SpA (2012) modeFrontier. http://www.modefrontier.com/. Accessed 5 Apr 2012

  • 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

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Franssen M (2005) Arrow’s theorem, multi-criteria decision problems and multi-attribute preferences in engineering design. Res Eng Des 16(1):42–56

    Article  Google Scholar 

  • Goldberg D (2002) The design of innovation: lessons from and for competent genetic algorithms, vol 7. Springer, New York

    Google Scholar 

  • Hadka D, Reed P (2012a) Borg: an auto-adaptive many-objective evolutionary computing framework. Evol Comput. doi:10.1162/EVCO_a_00075

    Google Scholar 

  • Hadka D, Reed P (2012b) Diagnostic assessment of search controls and failure modes in many-objective evolutionary optimization. Evol Comput 20(3):423–452

    Article  Google Scholar 

  • 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

  • Hitch CJ (1960) On the choice of objectives in systems studies. Tech. Rep. P-1955, The RAND Corporation

  • Hogarth R (1981) Beyond discrete biases: functional and dysfunctional aspects of judgmental heuristics. Psychol Bull 90(2):197–217

    Article  Google Scholar 

  • Inselberg A (1997) Multidimensional detective. In: IEEE symposium on information visualization, 1997. Proceedings, IEEE, pp 100–107

  • Inselberg A (2009) Parallel coordinates: visual multidimensional geometry and its applications. Springer, New York

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

  • 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

  • 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

  • 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

    Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multiobjective optimization. Evol Comput 10(3):263–282

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Miller GA (1956) The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97

    Article  Google Scholar 

  • 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

  • NASA (1978) GASP—general aviation synthesis program. Tech. Rep. NASA-CR-152303National Aeronautics and Space Administration Ames Research Center, Moffet Field, California

  • NASA, FAA (1994) General aviation design competition guidelines. Virginia Space Grant Consortium, Hampton, VA

    Google Scholar 

  • Nolan D, Thal J, Henry K, Sandy M (1995) NASA/FAA announce aviation design competition winners [press release]

  • R project (2012) The R project for statistical computing. http://www.r-project.org. Accessed 5 Apr 2012

  • Raymer D (1999) Aircraft design: a conceptual approach. American Institute of Aeronautics and Astronautics, Inc., Reston, VA

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Roy B (1971) Problems and methods with multiple objective functions. Math Program 1(1):239–266

    Article  MATH  Google Scholar 

  • Saaty TL (1990) How to make a decision: the analytic hierarchy process. Eur J Oper Res 48(1):9–26

    Article  MATH  Google Scholar 

  • SAS Institute (2012) JMP statistical discovery software. http://jmp.com. Accessed 5 Apr 2012

  • 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

  • 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

    Article  Google Scholar 

  • 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

    Chapter  Google Scholar 

  • Simpson T (1995) Development of a design process for realizing open engineering systems. Master’s Thesis, Georgia Institute of Technology

  • 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

    Article  Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • 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

  • 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

    Article  Google Scholar 

  • 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

  • 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

    Article  MathSciNet  MATH  Google Scholar 

  • 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

  • Teytaud O (2006) How entropy-theorems can show that on-line approximating high-dim pareto fronts is too hard. Technical Report, Inria Saclay (CR1)

  • Teytaud O (2007) On the hardness of offline multi-objective optimization. Evol Comput 15(4):475–491

    Article  Google Scholar 

  • 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

  • TIBCO (2012) Spotfire. http://spotfire.tibco.com. Accessed 5 Apr 2012

  • Tsoukiàs A (2008) From decision theory to decision aiding methodology. Eur J Oper Res 187:138–161

    Article  Google Scholar 

  • 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)

  • Venkataraman S, Haftka RT (2004) Structural optimization complexity: what has moore’s law done for us? Struct Multidisc Optim 28:275–287

    Article  Google Scholar 

  • Vrugt J, Robinson B (2007) Improved evolutionary optimization from genetically adaptive multimethod search. Proc Natl Acad Sci USA 104(3):708

    Article  Google Scholar 

  • Ware C (2004) Information visualization: perception for design, 2nd edn. Morgan-Kauffman

  • 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

    Article  Google Scholar 

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  • 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

  • Zeleny M (1986) Optimal system design with multiple criteria: de novo programming approach. Eng Costs Prod Econ 10(1):89–94

    Article  MathSciNet  Google Scholar 

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Acknowledgments

The second author of this work was partially supported by the US National Science Foundation under Grant CBET-0640443. The computational resources for this work were provided in part through instrumentation funded by the National Science Foundation through Grant OCI-0821527. Any opinions,findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the US National Science Foundation.

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Correspondence to Patrick M. Reed.

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Woodruff, M.J., Reed, P.M. & Simpson, T.W. Many objective visual analytics: rethinking the design of complex engineered systems. Struct Multidisc Optim 48, 201–219 (2013). https://doi.org/10.1007/s00158-013-0891-z

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