Metaheuristics for Optimisation of Organic Computing Systems (MOOCS)

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。这些方法往往受到自然现象的启发，模仿自然在维持高度复杂系统方面的有效性，尽管它们的功能甚至可能分布在几个不太复杂的子系统之间。为此，OC利用机器学习方法，并将核心角色分配给元分析，这对于系统的内部优化，自适应和整体功能是必要的。元分析在试图解决复杂的优化问题时至关重要。它们在应用中比简单的算法更通用，它们需要更少甚至不需要搜索空间的知识，并且它们在很短的计算时间内提供了非常好的结果。这些优点，再加上没有免费的午餐定理，即没有一种元启发式算法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算法。元启发式算法在OC系统中的应用仍然存在困难，特别是当试图找到最合适的元启发式算法或元启发式算法时。它们应该适用于OC系统中的所有优化问题，此外，自适应地响应系统环境的变化。在本计画中，我们希望能改善元分析在OC系统中的应用。我们将研究哪些元启发式适合于特定的问题，从而通过分析负责这些关系的元启发式的组成部分，推进最先进的技术。我们将进一步开发自适应策略，用于应用和交换这些组件，这将导致更通用的优化器，适用于OC的许多常见问题。此外，我们将促进分布式系统上的直接应用程序，通过分析足够的策略，算法的分布和并行化。

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.