Wilfried Jakob

A general cost-benefit-based adaptation framework for multimeme algorithms

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.

Data processing of high-rate low-voltage distribution grid recordings for smart grid monitoring and analysis

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.

Towards an adaptive multimeme algorithm for parameter optimisation suiting the engineers' needs

Important factors for the easy usage of an Evolutionary Algorithm (EA) are numbers of fitness calculations as low as possible, its robustness, and the reduction of its strategy parameters as far as possible. Multimeme Algorithms (MMA) are good candidates for the first two properties. In this paper a cost-benefit-based approach shall be introduced for the adaptive control of both meme selection and the ratio between local and global search. The latter is achieved by adaptively adjusting the intensity of the search of the memes and the frequency of their usage. It will be shown in which way the proposed kind of adaptation fills the gap previous work leaves. Detailed experiments in the field of continuous parameter optimisation demonstrate the superiority of the adaptive MMA over the simple MA and the pure EA.

HyGLEAM - An Approach to Generally Applicable Hybridization of Evolutionary Algorithms

Most successful applications of Evolutionary Algorithms to real world problems employ some sort of hybridization, thus speeding up the optimization process but turning the general applicable Evolutionary Algorithm into a problem-specific tool. This paper proposes to combine Evolutionary Algorithms and generally applicable local searchers to get the best of both approaches: A fast, but robust tool for global optimization. The approach consists of four different kinds of hybridization and combinations thereof, which are tested and compared using five commonly used benchmark functions and three real world applications. The results show the superiority of two hybridization types, with which reductions in the number evaluations of up to a factor of 100 could be achieved.

Solving scheduling problems in grid resource management using an evolutionary algorithm

Evolutionary Algorithms (EA) are well suited for solving optimisation problems, especially NP-complete problems This paper presents the application of the Evolutionary Algorithm GLEAM (General Learning and Evolutionary Algorithm and Method) in the field of grid computing Here, grid resources like computing power, software, or storage have to be allocated to jobs that are running in heterogeneous computing environments The problem is similar to industrial resource scheduling, but has additional characteristics like co-scheduling and high dynamics within the resource pool and the set of requesting jobs The paper describes the deployment of GLEAM in the global optimising grid resource broker GORBA (Global Optimising Resource Broker and Allocator) and the first promising results in a grid simulation environment.

Pareto Optimization or Cascaded Weighted Sum: A Comparison of Concepts

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.

First Evaluation Results Using the New Electrical Data Recorder for Power Grid Analysis

Today, power systems are subject to fundamental changes concerning functionality and dynamics due to new energy sources and increasing demand. However, detailed information of the system and the system state is the property of the suppliers or distributors, and is not comprehensively available to researchers at present. We propose to capture easily accessible, high-rate, low-voltage (LV) time series at different locations, and to store the whole data for subsequent usage in a large database. A system state simulation shall use these data and provide for load information without knowing the currents. For this purpose, we develop the electrical data recorder (EDR) and perform measurements in our first starting test site, the island network like Karlsruhe Institute of Technology campus. We first present comparison results between captured voltage characteristics and campus smart meter measurements that we use as an indication of the load status. We develop the Electrical Grid Analysis Simulation Modeling and Visualization Tool (eASiMoV) and show feasibility in a simple simulation. We measure the storage transfer to the large-scale database facility and give evaluation results. The EDR device, the eASiMoV software, and data handling methods are exhibited as valuable components of a promising new approach.

Fast Multi-objective Scheduling of Jobs to Constrained Resources Using a Hybrid Evolutionary Algorithm

The problem tackled here combines three properties of scheduling tasks, each of which makes the basic task more challenging: job scheduling with precedence rules, co-allocation of restricted resources of different performances and costs, and a multi-objective fitness function. As the algorithm must come up with results within a few minutes runtime, EA techniques must be tuned to this limitation. The paper describes how this was achieved and compares the results with a common scheduling algorithm, the Giffler-Thompson procedure.

Introducing the Electrical Data Recorder as a new capturing device for power grid analysis

Power systems are facing fundamental changes concerning functionality and dynamics today. However, detailed data of the system are property of the suppliers or distributors and are not easily available to researchers at present. In order to understand the system dependencies we propose to derive state information by combining high-rate low-voltage time series captures at different locations together with a simulation model of the grid. We take the island network like KIT campus as our starting investigation site. In a first step we developed the Electrical Data Recorder (EDR), which is capable of recording three phase voltage time series at up to 25 kHz synchronously. All data are stored in a large scale database facility (LSDF) for subsequent usage. We intend to derive a simulation model from the comparison of the voltage characteristics to periodic smart meter measurements as the indication of the load status. In this article we introduce the new recording device and present first results.

Concept of Action

It is essential that part of a robot program deals with the execution of physical actions. That is to say that a program must not only do something with its data, but also control the robot moves and gripper actions. The traditional program flow control in Pascal is done using statements, while robot and gripper control is performed by system procedure calls.