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Research Papers

Protocol-Based Multi-Agent Systems: Examining the Effect of Diversity, Dynamism, and Cooperation in Heuristic Optimization Approaches

[+] Author and Article Information
Lindsay Hanna Landry

Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213lindsay.h.landry@gmail.com

Jonathan Cagan

Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213cagan@cmu.edu

Calculated by taking the average solution quality of all runs of PMAS implementations that are in the upper right corner of the taxonomy versus the average solution quality of all runs of other PMAS implementations.

J. Mech. Des 133(2), 021001 (Jan 24, 2011) (11 pages) doi:10.1115/1.4003290 History: Received February 21, 2010; Revised December 08, 2010; Published January 24, 2011; Online January 24, 2011

Many heuristic optimization approaches have been developed to combat the ever-increasing complexity of engineering problems. In general, these approaches can be classified based on the diversity of the search strategies used, the amount of change in these search strategies during the optimization process, and the level of cooperation between these strategies. A review of the literature indicates that approaches that are simultaneously very diverse, highly dynamic, and cooperative are rare but have immense potential for finding high quality final solutions. In this work, a taxonomy of heuristic optimization approaches is introduced and used to motivate a new approach called protocol-based multi-agent systems. This approach is found to produce final solutions of much higher quality when its implementation includes the use of multiple search protocols, the adaptation of these protocols during the optimization, and the cooperation between these protocols than when these characteristics are absent.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

Taxonomy for optimization approaches based on diversity, dynamism, and cooperation

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Figure 2

Average time per objective function evaluation (ms) versus final average objective function value for different optimization classifications using the PMAS platform with identical agent population sizes and number of iterations executed on the one-max problem

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Figure 3

Computation time versus mean and standard error range when both a heterogeneous, fully dynamic, and fully cooperative approach and a homogeneous, static, and noncooperative approach are implemented in PMAS and applied to the one-max problem

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Figure 4

Average time per objective function evaluation (s) versus final average objective function value for different optimization classifications using the PMAS platform with identical agent population sizes and number of iterations executed on the 3D packing problem

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Figure 5

Computation time versus mean and standard error of objective function value when both a heterogeneous, fully dynamic, and fully cooperative approach and a homogeneous, static, and noncooperative approach are implemented in PMAS and applied to the three-dimensional packing problem

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Figure 6

Emergent trends and frontier based on PMAS data

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Figure 7

One-max and 3D layout data with frontier overlaid and classifications from Fig. 1 indicated

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Figure 8

(a) Generating agent and (b) selecting agent population trends in fully dynamic PMAS executed on the one-max problem

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Figure 9

(a) Generating agent and (b) selecting agent population trends in fully dynamic PMAS executed on the 3D packing problem

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Figure 10

Example of single iteration of heterogeneous, fully dynamic, and fully cooperative PMAS

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