文章基本信息

标题：Optimization of Swarm-Based Simulations
本地全文：下载
作者：Sebastian von Mammen ; Abbas Sarraf Shirazi ; Vladimir Sarpe 等
期刊名称：ISRN Artificial Intelligence
印刷版ISSN：2090-7435
电子版ISSN：2090-7443
出版年度：2012
卷号：2012
DOI：10.5402/2012/365791
出版社：Hindawi Publishing Corporation
摘要：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.