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Technical Brief

Drawing Inspiration From Human Design Teams for Better Search and Optimization: The Heterogeneous Simulated Annealing Teams Algorithm

[+] Author and Article Information
Christopher McComb

Mem. ASME
Department of Mechanical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: ccm@cmu.edu

Jonathan Cagan

Fellow ASME
Department of Mechanical Engineering,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: cagan@cmu.edu

Kenneth Kotovsky

Department of Psychology,
Carnegie Mellon University,
Pittsburgh, PA 15213
e-mail: kotovsky@cmu.edu

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received June 3, 2015; final manuscript received February 6, 2016; published online March 2, 2016. Assoc. Editor: Kazuhiro Saitou.

J. Mech. Des 138(4), 044501 (Mar 02, 2016) (6 pages) Paper No: MD-15-1409; doi: 10.1115/1.4032810 History: Received June 03, 2015; Revised February 06, 2016

Insights uncovered by research in design cognition are often utilized to develop methods used by human designers; in this work, such insights are used to inform and improve computational methodologies. This paper introduces the heterogeneous simulated annealing team (HSAT) algorithm, a multiagent simulated annealing (MSA) algorithm. HSAT is based on a validated computational model of human-based engineering design and retains characteristics of the model that structure interaction between team members and allow for heterogeneous search strategies to be employed within a team. The performance of this new algorithm is compared to several other simulated annealing (SA) based algorithms on three carefully selected benchmarking functions. The HSAT algorithm provides terminal solutions that are better on average than other algorithms explored in this work.

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References

Figures

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Fig. 1

Generalized flowchart for conventional SA

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Fig. 2

Flowchart for the HSAT algorithm

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Fig. 3

Two-dimensional representations of benchmarking functions: (a) Ackley function, (b) Griewank, global, (c) Griewank, local, and (d) Rastrigin function

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Fig. 4

Comparison of optimization results for Ackley function (error bars omitted for clarity): (a) cumulative distribution of terminal solutions and (b) geometric mean of best solution found over time

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Fig. 5

Comparison of optimization results for Griewank function (error bars omitted for clarity): (a) cumulative distribution of terminal solutions and (b) geometric mean of best solution found over time

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Fig. 6

Comparison of optimization results for Rastrigin function (error bars omitted for clarity): (a) cumulative distribution of terminal solutions and (b) geometric mean of best solution found over time

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