Nonlinear mixed integer based Optimization Technique for Space Applications

In this thesis a new algorithm for mixed integer nonlinear programming (MINLP) is developed and applied to several real world applications with special focus on space ap-plications. The algorithm is based on two main components, which are an extension of the Ant Colony Optimization metaheuristic and the Oracle Penalty Method for con-straint handling. A sophisticated implementation (named MIDACO) of the algorithm is used to numerically demonstrate the usefulness and performance capabilities of the here developed novel approach on MINLP. An extensive amount of numerical results on both, comprehensive sets of benchmark problems (with up to 100 test instances) and several real world applications, are presented and compared to results obtained by concurrent methods. It can be shown, that the here developed approach is not only fully competi-tive with established MINLP algorithms, but is even able to outperform those regarding global optimization capabilities and cpu runtime performance. Furthermore, the algo-rithm is able to solve challenging space applications, that are considered here as mixed integer problems for the very first time