Abstract
Multi-objective optimization yields multiple solutions each of which is no better or worse than the others when the objectives are conflicting. These solutions lie on the Pareto-optimal front which is a lower-dimensional slice of the objective space. Together, the solutions may possess special properties that make them optimal over other feasible solutions. Innovization is the process of extracting such special properties (or design principles) from a trade-off dataset in the form of mathematical relationships between the variables and objective functions. In this paper, we deal with a closely related concept called temporal innovization. While innovization concerns the design principles obtained from the trade-off front, temporal innovization refers to the evolution of these design principles during the optimization process. Our study indicates that not only do different design principles evolve at different rates, but that they start evolving at different times. We illustrate temporal innovization using several examples.
Keywords
- Topology Optimization
- Design Principle
- Topology Optimization Problem
- Multidisciplinary Optimization
- Engineering Design Problem
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Bandaru, S., Deb, K.: Towards automating the discovery of certain innovative design principles through a clustering-based optimization technique. Engineering Optimization 43(9), 911–941 (2011)
Bendsøe, M.: Optimal shape design as a material distribution problem. Structural and Multidisciplinary Optimization 1(4), 193–202 (1989)
Datta, D., Deb, K.: Design of optimum cross-sections for load-carrying members using multi-objective evolutionary algorithms. In: Proceedings of International Conference on Systemics, Cybernetics and Informatics, pp. 571–577 (2005)
Deb, K., Agarwal, S., Pratap, A., Meyarivan, T.: A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Deb, K., Bandaru, S., Tutum, C.C.: Temporal evolution of design principles in engineering systems: Analogies with human evolution. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds.) PPSN 2012, Part II. LNCS, vol. 7492, pp. 1–10. Springer, Heidelberg (2012)
Deb, K., Gupta, S., Daum, D., Branke, J., Mall, A., Padmanabhan, D.: Reliability-based optimization using evolutionary algorithms. IEEE Trans. on Evolutionary Computation 13(5), 1054–1074 (2009)
Deb, K., Srinivasan, A.: Innovization: Innovating design principles through optimization. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, GECCO 2006, pp. 1629–1634. ACM, New York (2006)
Deb, K., Bandaru, S., Greiner, D., Gaspar-Cunha, A., Tutum, C.C.: An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering. Applied Soft Computing 15, 42–56 (2014)
Fedder, G., Iyer, S., Mukherjee, T.: Automated optimal synthesis of microresonators. In: Proceedings of the Ninth Int. Conf. Solid State Sens. Actuators, Chicago, IL, pp. 1109–1112, April 1997
Fedder, G., Mukherjee, T.: Physical design for surface-micromachined MEMS. In: Proceedings of the Fifth ACM SIGDA Physical Design Workshop, Virginia, USA, April 1996
Haeckel, E.: The evolution of man, vol. 1. Kessinger Publishing (1879)
Kreimer, G.: The green algal eyespot apparatus: A primordial visual system and more? Current Genetics 55(1), 19–43 (2009)
Land, M., Fernald, R.: The evolution of eyes. Annual Review of Neuroscience 15(1), 1–29 (1992)
Quiza Sardiñas, R., Rivas Santana, M., Alfonso Brindis, E.: Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Engineering Applications of Artificial Intelligence 19(2), 127–133 (2006)
Rozvany, G.: Aims, scope, methods, history and unified terminology of computer-aided topology optimization in structural mechanics. Structural and Multidisciplinary Optimization 21(2), 90–108 (2001)
Rozvany, G.: A critical review of established methods of structural topology optimization. Structural and Multidisciplinary Optimization 37(3), 217–237 (2009)
Sigmund, O.: A 99 line topology optimization code written in matlab. Structural and Multidisciplinary Optimization 21(2), 120–127 (2001)
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Bandaru, S., Deb, K. (2015). Temporal Innovization: Evolution of Design Principles Using Multi-objective Optimization. In: Gaspar-Cunha, A., Henggeler Antunes, C., Coello, C. (eds) Evolutionary Multi-Criterion Optimization. EMO 2015. Lecture Notes in Computer Science(), vol 9018. Springer, Cham. https://doi.org/10.1007/978-3-319-15934-8_6
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DOI: https://doi.org/10.1007/978-3-319-15934-8_6
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