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Research Papers

Design of Complex Engineered Systems Using Multi-Agent Coordination

[+] Author and Article Information
Nicolás F. Soria Zurita

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331;
Colegio de Ciencias e Ingeniería,
Universidad San Francisco de Quito,
Quito EC 170157, Ecuador
e-mail: soriazun@oregonstate.edu

Mitchell K. Colby

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: colbym@engr.orst.edu

Irem Y. Tumer

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: irem.tumer@oregonstate.edu

Christopher Hoyle

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: chris.hoyle@oregonstate.edu

Kagan Tumer

School of Mechanical, Industrial
and Manufacturing Engineering,
Oregon State University,
Corvallis, OR 97331
e-mail: kagan.tumer@oregonstate.edu

1Corresponding author.

Contributed by the Computer-Aided Product Development Committee of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received October 5, 2016; final manuscript received October 2, 2017; published online November 28, 2017. Assoc. Editor: Yong Chen.

J. Comput. Inf. Sci. Eng 18(1), 011003 (Nov 28, 2017) (13 pages) Paper No: JCISE-16-2099; doi: 10.1115/1.4038158 History: Received October 05, 2016; Revised October 02, 2017

In complex engineering systems, complexity may arise by design, or as a by-product of the system's operation. In either case, the cause of complexity is the same: the unpredictable manner in which interactions among components modify system behavior. Traditionally, two different approaches are used to handle such complexity: (i) a centralized design approach where the impacts of all potential system states and behaviors resulting from design decisions must be accurately modeled and (ii) an approach based on externally legislating design decisions, which avoid such difficulties, but at the cost of expensive external mechanisms to determine trade-offs among competing design decisions. Our approach is a hybrid of the two approaches, providing a method in which decisions can be reconciled without the need for either detailed interaction models or external mechanisms. A key insight of this approach is that complex system design, undertaken with respect to a variety of design objectives, is fundamentally similar to the multi-agent coordination problem, where component decisions and their interactions lead to global behavior. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm (CCEA) can effectively design a complex engineered system. This paper uses a system model of a Formula SAE racing vehicle to illustrate and simulate the methods and potential results. By designing complex systems with a multi-agent coordination approach, a design methodology can be developed to reduce design uncertainty and provide mechanisms through which the system level impact of decisions can be estimated without explicitly modeling such interactions.

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Figures

Grahic Jump Location
Fig. 2

Design process for agents

Grahic Jump Location
Fig. 3

Racing vehicle model: 1—rear wing, 2—front wing, 3—side wing, 4—rear tire, 5—front tire, 6—engine, 7—cabin, 8—impact attenuator, 9—brake system, 10—rear suspension, and 11—front suspension

Grahic Jump Location
Fig. 4

Cooperative coevolutionary algorithm methodology

Grahic Jump Location
Fig. 5

System performance of difference evaluations Di(z) and global evaluations G(z)

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