Looking at articles or conference papers published since the turn of the century, Pareto optimization is the dominating assessment method for multi-objective nonlinear optimization problems. However, is it always the method of choice for real-world applications, where either more than four objectives have to be considered, or the same type of task is repeated again and again with only minor modifications, in an automated optimization or planning process? This paper presents a classification of application scenarios and compares the Pareto approach with an extended version of the weighted sum, called cascaded weighted sum, for the different scenarios. Its range of application within the field of multi-objective optimization is discussed as well as its strengths and weaknesses.

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Power networks will change from a rigid hierarchic architecture to dynamic interconnected smart grids. In traditional power grids, the frequency is the controlled quantity to maintain supply and load power balance. Thereby, high rotating mass inertia ensures for stability. In the future, system stability will have to rely more on real-time measurements and sophisticated control, especially when integrating fluctuating renewable power sources or high-load consumers like electrical vehicles to the low-voltage distribution grid. In the present contribution, we describe a data processing network for the in-house developed low-voltage, high-rate measurement devices called electrical data recorder (EDR). These capture units are capable of sending the full high-rate acquisition data for permanent storage in a large-scale database. The EDR network is specifically designed to serve for reliable and secured transport of large data, live performance monitoring, and deep data mining. We integrate dedicated different interfaces for statistical evaluation, big data queries, comparative analysis, and data integrity tests in order to provide a wide range of useful post-processing methods for smart grid analysis. We implemented the developed EDR network architecture for high-rate measurement data processing and management at different locations in the power grid of our Institute. The system runs stable and successfully collects data since several years. The results of the implemented evaluation functionalities show the feasibility of the implemented methods for signal processing, in view of enhanced smart grid operation.

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As Memetic Algorithms (MA) are a crossbreed between local searchers and Evolutionary Algorithms (EA) spreading of computational resources between evolutionary and local search is a key issue for a good performance, if not for success at all. This paper summarises and continues previous work on a general cost-benefit-based adaptation scheme for the choice of local searchers (memes), the frequency of their usage, and their search depth. This scheme eliminates the MA strategy parameters controlling meme usage, but raises new ones for steering the adaptation itself. Their impact is analysed and it will be shown that in the end the number of strategy parameters is decreased significantly as well as their range of meaningful values. In addition to this the number of fitness evaluations is reduced drastically. Both are necessary prerequisites for many practical applications as well as for the acceptance of the method by practitioners.Although the introduced framework is tailored to EAs producing more than one offspring per mating, it is also suited for those with only one child per pairing. So there are no preconditions to the EA for the described adaptation scheme to be applied. The benefit of LS within an MA can be summarised as follows: firstly, the already mentioned reduction of fitness evaluations required to achieve a certain quality and secondly, the possibility to include domain-specific knowledge into the search process. Additionally, offspring of poor performance, which are located in a region of attraction of a (local) optimum, are likely to be ignored in standard EAs, but can survive, if locally improved and support subsequent search. Furthermore, local search can work as a repair mechanism, if the genetic operators produce infeasible solutions in constraint optimisation and these solutions are penalised by reducing their fitness. The drawback consists in the following design questions, the last two of which mainly control the balance between global and local search or between exploration and exploitation: Keywords1. Which local searcher should be used?2. Should the LS result be used to update the genotype (Lamarckian evolution)or not (Baldwinian evolution)?3. How are offspring selected to undergo local search?4. How often should local improvement be applied or to which fraction of the generated offspring per generation? This is often referred to as local search frequency. [14,55,44]. When introducing the basic algorithms used for the experimental study, our answer to this question will be given. 3In this paper we present a cost-benefit-based adaptation scheme, which controls the parameters resulting from the remaining four of the above five questions. The beneficial effect of the adaptation scheme was reported about in [24,25], where also the new strategy parameters resulting from the scheme were introduced and discussed. In this work, the adaptation scheme is extended and the effect of the new strategy parameters is investigated empirically in more detail. The main goal is to find out their rele...

Abstract. When applied to real-world problems, the powerful optimization tool of Evolutionary Algorithms frequently turns out to be too time-consuming due to elaborate fitness calculations that are often based on run-time-intensive simulations. Incorporating domain-specific knowledge by problem-tailored heuristics or local searchers is a commonly used solution, but turns the generally applicable Evolutionary Algorithm into a problem-specific tool. The new method of hybridization implemented in HyGLEAM is aimed at overcoming this limitation and getting the best of both algorithm classes: A fast, globally searching, and robust procedure that preserves the convergence reliability of evolutionary search. Extensive tests demonstrate the superiority of the approach, but also show a drawback: No common parameterization can be drawn from the experiments. As a solution, a new concept of a self-adapting hybrid is introduced. It is stressed that the methods presented can be applied to Evolutionary Algorithms other than the one used here with no or minor modifications being required only.

This paper is motivated by, but not limited to, the task of scheduling jobs organized in workflows to a computational grid. Due to the dynamic nature of grid computing, more or less permanent replanning is required so that only very limited time is available to come up with a revised plan. To meet the requirements of both users and resource owners, a multi-objective optimization comprising execution time and costs is needed. This paper summarizes our work over the last six years in this field, and reports new results obtained by the combination of heuristics and evolutionary search in an adaptive Memetic Algorithm. We will show how different heuristics contribute to solving varying replanning scenarios and investigate the question of the maximum manageable work load for a grid of growing size starting with a load of 200 jobs and 20 resources up to 7000 jobs and 700 resources. Furthermore, the effect of four different local searchers incorporated into the evolutionary search is studied. We will also report briefly on approaches that failed within the short time frame given for planning.