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Enhanced Targeted Initial Populations for Multiobjective Product Line Optimization

[+] Author Affiliations
Garrett Foster, Scott Ferguson

North Carolina State University, Raleigh, NC

Paper No. DETC2013-13303, pp. V03AT03A032; 13 pages
doi:10.1115/DETC2013-13303
From:
  • ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 3A: 39th Design Automation Conference
  • Portland, Oregon, USA, August 4–7, 2013
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5588-1
  • Copyright © 2013 by ASME

abstract

Initial populations for genetic algorithms are often created using randomly generated designs in an effort to maximize the genetic diversity in the design space. However, research indicates that the inclusion of solutions generated based on domain knowledge (i.e. non-random solutions) can notably improve the performance of the genetic algorithm with respect to solution performance and/or computational cost for convergence. This performance increase is extremely valuable for computationally expensive problems, such as product line optimization. In prior research, the authors demonstrated these improvements for product line design problems where market share of preference was the performance objective. Initial product line solutions were constructed from products that had the largest product-level utility for individual respondents. However, this simple product identification strategy did not adequately scale to accommodate the richer design problem associated with multiple objectives. This paper extends the creation of targeted initial populations to multiobjective product line design problems by using the objectives of the problem, instead of product level utility, to identify candidate designs. A MP3 player and vehicle feature packaging product line design problems are used to demonstrate this approach and assess the improvement of this modification.

Copyright © 2013 by ASME
Topics: Optimization

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