Towards the design and implementation of optimization networks in heuristiclab

Combining multiple algorithms to cooperate in solving different optimization problems or process other workflows can be done in various problem domains, e.g. combinatorial optimization and data analysis. Optimization networks allow us to create such cooperative approaches by connecting multiple algorithms and lefting them work together. In this paper, we propose an optimization network architecture for HeuristicLab. Networks are built using nodes that perform arbitrary tasks. We introduce the concepts of messages and ports, which can be used to exchange data between nodes. The application of such optimization networks is shown for two different applications. One is to solve the Traveling Thief Problem, where we substitute parts of the original problem with subproblems that are optimized interdependently. In another scenario, feature selection is combined with linear regression to find the best combination of features in order to achieve the best linear regression model.