Metaheuristics for Optimisation of Organic Computing Systems (MOOCS)

有机计算系统优化的元启发法 (MOOCS)

• 批准号: 467799632
• 负责人: Professor Dr. Jörg Hähner
• 金额: --
• 依托单位: Lehrstuhl für Organic Computing
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/467799632?language=en
• 关键词: Metaheuristics; Optimisation; Organic; Computing; Systems

Organic computing (OC) provides techniques for the design of complex autonomous and self-adaptive systems. The approaches are often inspired by natural phenomena mimicking nature’seffectiveness in sustaining systems of high sophistication, though their functionality may even be distributed between several less sophisticated subsystems. To this end, OC utilises machine learning approaches and a central role is assigned to metaheuristics, which are necessary for the internal optimisation, self-adaptation and overall functionality of the system.Metaheuristics are essential when trying to solve complex optimisation problems. They are more versatile in their application compared to simple heuristics, they require less or even no knowledge of the search space and they provide very good results in little computational time. These advantages, combined with the No-free-lunch theorem, which states that no metaheuristic can be equally effective on all optimisation problems, resulted in a vast amount of newly developed metaheuristics.The application of metaheuristics in OC systems still poses difficulties, especially when trying to find the most suitable metaheuristic or metaheuristics. They should be applicable to all optimisation problems in the OC system and, in addition, self-adaptively respond to changes in the system’s environment. Furthermore, they should also be applicable in distributed systems with little computational resources.In this project, we want to improve the application of metaheuristics in OC systems. We will examine which metaheuristics are suitable for specific problems and, thereby, advance the state of the art through analysing the components of the metaheuristic responsible for these relations. We will furthermore develop self-adaptive strategies for applying and exchanging these components, which will lead to a more general optimiser, suitable for many problems common to OC. Additionally, we will facilitate the direct application on distributed systems by analysing adequate strategies for the distribution and parallelisation of the algorithms.

有机计算（OC）为复杂的自治和自适应系统的设计提供了技术。这些方法经常受到自然现象的启发，这些自然现象模仿了维持高度复杂系统的自然有效性，尽管它们的功能甚至可能分布在几个不那么复杂的子系统之间。为此，OC利用机器学习方法，并将中心角色分配给元启发式，这对于系统的内部优化、自适应和整体功能是必要的。当试图解决复杂的优化问题时，元启发式是必不可少的。与简单的启发式相比，它们在应用中更加通用，它们需要更少甚至不需要搜索空间的知识，并且在很少的计算时间内提供非常好的结果。这些优势，再加上“无免费午餐定理”（即没有一种元启发式方法可以对所有优化问题都同样有效），导致了大量新开发的元启发式。元启发式在OC系统中的应用仍然存在困难，特别是在试图找到最合适的元启发式或元启发式时。它们应适用于OC系统中的所有优化问题，此外，还应自适应地响应系统环境的变化。此外，它们还应适用于计算资源较少的分布式系统。在这个项目中，我们想要改进元启发式在OC系统中的应用。我们将检查哪些元启发式适合于具体问题，从而通过分析负责这些关系的元启发式的组成部分来推进技术的发展。我们将进一步开发用于应用和交换这些组件的自适应策略，这将导致更通用的优化器，适用于OC常见的许多问题。此外，我们将通过分析算法的分布和并行化的适当策略，促进在分布式系统上的直接应用。