Adjusting algorithm parameters to a given problem is of crucial importance for performance comparisons as well as for reliable (first) results on previously unknown problems, or with new algorithms. This also holds for parameters controlling adaptability features, as long as the optimization algorithm is not able to completely self-adapt itself to the posed problem and thereby get rid of all pa...

An important issue in time series classification problems is to find representative prototypes. Especially for roughly segmented time series with spatial distortions, such as human gestures, it is complicated to find templates, which optimally represent signal classes. In this paper we present an approach to find optimal time series prototypes in subseries of class templates. Our optimization a...

one of the mostly used enhanced oil recovery methods is the injection of water or gas under pressure to maintain or reverse the declining pressure in a reservoir. several parameters should be optimized in a fluid injection process. the usual optimizing methods evaluate several scenarios to find the best solution. since it is required to run the reservoir simulator hundreds of times, the process...

Noise is a common problem encountered in real-world optimization. Although it is folklore that evolution strategies perform well in the presence of noise, even their performance is degraded. One effect on which we will focus in this paper is the reaching of a steady state that deviates from the actual optimal solution. The quality gain is a local progress measure, describing the expected one-ge...

This contribution deals with the optimization of the design of reactive distillation columns by using a memetic algorithm (MA) which is a combination of an evolution strategy (ES) and a mathematical programming (MP) solver. The focus of this paper is on the restriction on the number of feed trays that introduces discrete variables into the problem formulation. The optimization of the number and...

Retail is traditionally labour-intensive. Demand-oriented workforce management has great significance due to the amount of competition which enforces a strict cost management while keeping a good service level. Thus, highly flexible working time models are of particular importance. Our project addresses the question how to automatically and simultaneously assign staff to workstations and genera...

We address the following sensor selection problem. We assume that a dynamic system possesses a certain property, call it Property D, when a set G of sensors is used. There is a cost c A associated with each set A of sensors that is a subset of G. Given any set of sensors that is a subset of G, it is possible to determine, via a test, whether the resulting system-sensor combination possesses Pro...

549 Based on clonal selection principle, this paper proposes an immune-inspired evolution strategy (IIES) for constrained optimization problems with two improvements. Firstly, in order to enhance global search capability, more clones are produced by individuals that have far-off nearest neighbors in the less-crowed regions. On the other hand, immune update mechanism is proposed to replace the w...

This paper describes the application of evolution strategies to the design of interacting multiple model (IMM) tracking filters in order to fulfill a large table of performance specifications. These specifications define the desired filter performance in a thorough set of selected test scenarios, for different figures of merit and input conditions, imposing hundreds of performance goals. The de...

The paper illustrates the application of a combined adaptive particle swarm optimization (A-PSO) algorithm and the finite strip method (FSM) to the lay-up optimization of symmetrically fiber-metal laminated (FML) composite shallow shell panels for maximizing the fundamental frequency. To improve the speed of the optimization process, adaptive inertia weight was used in the particle swarm optimiz...