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Temporal Innovization: Evolution of Design Principles Using Multi-objective Optimization

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9018)

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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  • DOI: 10.1007/978-3-319-15934-8_6
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Correspondence to Sunith Bandaru .

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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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