Design of Complex Engineered Systems Using Multi-Agent Coordination

Abstract

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.

Solution of Linear and Non-Linear Boundary Value Problems Using Population-Distributed Parallel Differential Evolution

“…Distributed evolutionary algorithms (dEAs) improve the efficiency of EAs, and on the other hand they enhance the global search ability. In this sense, the proposed PDDE technique improves the availability for solving real-world problems with largescale, high-dimensional, and complex features [24].…”

Modeling multidisciplinary design with multiagent learning

“…Multiagent systems have also had success designing policies for other complex systems domains, such as power grids (Dimeas and Hatziargyriou, 2005; Pipattanasomporn et al ., 2009), air traffic control (Agogino and Tumer, 2012), and multi-robot coordination (Yliniemi et al ., 2014). Consequentially, a multiagent approach has been used as a framework both for designing complex engineered systems (Manion et al ., 2015; Hulse et al ., 2017; Soria et al ., 2017) and for modelling design organizations (Jin and Levitt, 1993; Jin and Levitt, 1996).…”

“…Previous work by the authors using multiagent systems to design complex engineered systems involved applying a coevolutionary algorithm to racecar design (Soria et al ., 2017) and applying multiagent learning to the design of a self-replicating robotic manufacturing factory (Manion et al, 2015). For the racecar application, agents designed sets of component solutions, which were combined into designs.…”

Model‐based architecture and programmatic optimization for satellite system‐of‐systems architectures

“…Techniques have also been developed to ensure genetic algorithms can be used to solve complex optimizations in a computationally efficiently manner by leveraging parallel processors . Additionally, for situations when there are too many subobjectives to optimize efficiently with one optimization, techniques have recently been developed to leverage cooperative coevolutionary algorithms …”