Design und Management komplexer technischer Prozesse und Systeme mit Methoden der Computational Intelligence Pareto Set and EMOA Behavior for Simple Multimodal Multiobjective Functions

Design und Management komplexer technischer Prozesse und Systeme mit Methoden der Computational Intelligence Pareto Set and EMOA Bahavior for Simple Multimodal Multiobjective Functions

Recent research on evolutionary multiobjective optimization has mainly focused on Pareto-fronts. However, we state that proper behavior of the utilized algorithms in decision/search space is necessary for obtaining good results if multimodal objective functions are concerned. Therefore, it makes sense to observe the development of Pareto-sets as well. We do so on a simple, configurable problem,...

Design und Management komplexer technischer Prozesse und Systeme mit Methoden der Computational Intelligence Evolutionary Optimization of Dynamic Multiobjective Functions

Many real-world problems show both multiobjective as well as dynamic characteristics. In order to use multiobjective evolutionary optimization algorithms (MOEA) efficiently, a systematic analysis of the behavior of these algorithms in dynamic environments is necessary. Dynamic fitness functions can be classified into problems with moving Pareto fronts and Pareto sets having varying speed, shape...

Sonderforschungsbereich 531: Design und Management komplexer Prozesse und Systeme mit Methoden der Computational Intelligence

Design und Management komplexer technischer Prozesse und Systeme mit Methoden der Computational Intelligence TAKEOVER TIME IN PARALLEL POPULATIONS WITH MIGRATION

The term takeover time regarding selection methods used in evolutionary algorithms denotes the (expected) number of iterations of the selection method until the entire population consists of copies of the best individual, provided that the initial population consists of a single copy of the best individual whereas the remaining individuals are worse. Here, this notion is extended to parallel su...

Design und Management komplexer technischer Prozesse und Systeme mit Methoden der Computational Intelligence Faster S-Metric Calculation by Considering Dominated Hypervolume as Klee‘s Measure Problem

The dominated hypervolume (or S-metric) is a commonly accepted quality measure for comparing approximations of Pareto fronts generated by multi-objective optimizers. Since optimizers exist, namely evolutionary algorithms, that use the S-metric internally several times per iteration, a faster determination of the S-metric value is of essential importance. This paper describes how to consider the...