Szaroletta, W. (2002, June), Introducing Single Criterion Optimization Methods Into Mechanics Classes  Paper presented at 2002 Annual Conference, Montreal, Canada. 10.18260/1-2--10583

\bibitem{asee_peer_10583}
  Szaroletta, W. (2002, June), \emph{Introducing Single Criterion Optimization Methods Into Mechanics Classes}
  Paper presented at 2002 Annual Conference, Montreal, Canada.
  10.18260/1-2--10583

William Szaroletta.  "Introducing Single Criterion Optimization Methods Into Mechanics Classes".  2002 Annual Conference, Montreal, Canada, 2002, June.  ASEE Conferences, 2002.  https://peer.asee.org/10583 Internet.  15 Dec, 2024

\bibitem{asee_peer_10583}
  William Szaroletta.
  "Introducing Single Criterion Optimization Methods Into Mechanics Classes".
  \emph{2002 Annual Conference, Montreal, Canada, 2002, June}.
  ASEE Conferences, 2002.
  https://peer.asee.org/10583 Internet.  15 Dec, 2024

@INPROCEEDINGS{asee_peer_10583
author = "William Szaroletta"
title = "Introducing Single Criterion Optimization Methods Into Mechanics Classes"
booktitle = "2002 Annual Conference"
year = "2002"
month = "June"
address = "Montreal, Canada"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/10583}
number = {10.18260/1-2--10583}
}

TY  - CPAPER
AB  - Single criterion optimization problems are shown to be readily taught and understood at lower division course levels using algebra/calculus, exhaustive numerical searches, and solver-type tools in standard spreadsheet packages. Genetic algorithms provide another suitable numerical technique that is relatively easily understood by students in high-level form, directly applicable to the single criterion class of optimization problems and very helpful to the multiple criteria class of optimization problems.
This paper describes the methods of lower-division mechanics classroom introduction of optimization methods including algebra/calculus, spreadsheet solver, exhaustive search, and genetic algorithms. A classical solid mechanics problem utilizing the simply supported beam with a central load is used as the baseline in this paper for presenting the optimization methods introduced. Several other more complex problems are described. Multiple criteria optimization problems, which can quickly exceed the capability of typical spreadsheet solver tools, require students to utilize the multiple criteria optimization capability of genetic algorithms software. A possible framework that would support both single criterion and multiple criteria optimization methods, based on using genetic algorithms software, is presented that could allow these powerful methods to be introduced and successfully utilized earlier in the student’s college experience.

AU  - William Szaroletta
CY  - Montreal, Canada
DA  - 2002/06/16
PB  - ASEE Conferences
TI  - Introducing Single Criterion Optimization Methods Into Mechanics Classes
UR  - https://peer.asee.org/10583
DO  - 10.18260/1-2--10583
ER  -