A Parameter Tuning Framework for Metaheuristics Based on Design of Experiments and Artificial Neural Networks

In this paper, a framework for the simplification andstandardization of metaheuristic related parameter tuning by applyinga four phase methodology, utilizing Design of Experiments andArtificial Neural Networks, is presented. Metaheuristics are multipurposeproblem solvers that are utilized on computational optimizationproblems for which no efficient problem-specific algorithmexists. Their successful application to concrete problems requires thefinding of a good initial parameter setting, which is a tedious andtime-consuming task. Recent research reveals the lack of approachwhen it comes to this so called parameter tuning process. In themajority of publications, researchers do have a weak motivation fortheir respective choices, if any. Because initial parameter settingshave a significant impact on the solutions quality, this course ofaction could lead to suboptimal experimental results, and therebya fraudulent basis for the drawing of conclusions