Browsing by Author "Sarraf Shirazi, Abbas"
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- ItemOpen AccessAbstraction Mechanisms Towards Large-Scale Agent-Based Simulations(2013-10-02) Sarraf Shirazi, Abbas; Jacob, ChristianThe typically large degrees of interaction in agent-based simulations come at considerable computational costs. In this thesis, we propose an abstraction framework to reduce the run-time of the simulations by learning recurring patterns. We employ machine learning techniques to abstract groups of agents or their behaviours to cut down computational complexity, while preserving the inherent flexibility of agent-based models. The learned abstractions, which subsume the underlying model agents' interactions, are constantly tested for their validity---after all, the dynamics of a system may change over time to such an extent that previously learned patterns would not reoccur. An invalid abstraction is, therefore, removed from the simulation. The creation and removal of abstractions continues throughout the course of a simulation in order to ensure an adequate adaptation to the system dynamics. Experimental results on biological agent-based simulations show that our proposed framework can successfully boost the simulation speed while maintaining the freedom of arbitrary interactions.
- ItemOpen AccessOptimization of Swarm-Based Simulations(2012-08-16) von Mammen, Sebastian; Sarraf Shirazi, Abbas; Sarpe, Vladimir; Jacob, ChristianIn 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.