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ISRN Artificial Intelligence
Volume 2012 (2012), Article ID 365791, 13 pages
Research Article

Optimization of Swarm-Based Simulations

1Department of Computer Science, Faculty of Science, University of Calgary, Calgary, AB, Canada T2N 1N4
2Department of Biochemistry & Molecular Biology, Faculty of Medicine, University of Calgary, Calgary, AB, Canada T2N 1N4

Received 14 March 2012; Accepted 8 April 2012

Academic Editors: F. Camastra, K. W. Chau, and K. Rasheed

Copyright © 2012 Sebastian von Mammen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In computational swarms, large numbers of reactive agents are simulated. The swarm individuals may coordinate their movements in a “search space” to create efficient routes, to occupy niches, or to find the highest peaks. From a more general perspective though, swarms are a means of representation and computation to bridge the gap between local, individual interactions, and global, emergent phenomena. Computational swarms bear great advantages over other numeric methods, for instance, regarding their extensibility, potential for real-time interaction, dynamic interaction topologies, close translation between natural science theory and the computational model, and the integration of multiscale and multiphysics aspects. However, the more comprehensive a swarm-based model becomes, the more demanding its configuration and the more costly its computation become. In this paper, we present an approach to effectively configure and efficiently compute swarm-based simulations by means of heuristic, population-based optimization techniques. We emphasize the commonalities of several of our recent studies that shed light on top-down model optimization and bottom-up abstraction techniques, culminating in a postulation of a general concept of self-organized optimization in swarm-based simulations.