Summary: The aim of this project is to develop, from the present simple state of art, an extensive parameterized analysis of bio-inspired computing and transfer these new insights into better performing algorithms. We do this by examining features of important combinatorial optimization problems and their influence on the performance of bio-inspired computing methods. These theoretical investigations are carried out by feature-based analysis and by using parameterized computational complexity analyses of bio-inspired computing methods. Such fundamental investigations allow us to get new insights on how different parameters of combinatorial optimization problems influence the runtime behaviour of bio-inspired computing methods. Our studies will mainly focus on problems related to supply chain management such as vehicle routing, packing, and scheduling and problems motivated by computer vision research such as the multi-cut problem.