This thesis involves three topics: benchmarking discrete optimization algorithms, empirical analyses of evolutionary computation, and automatic algorithm configuration. The objective is... Show moreThis thesis involves three topics: benchmarking discrete optimization algorithms, empirical analyses of evolutionary computation, and automatic algorithm configuration. The objective is benchmarking EAs on discrete optimization for the selection and design of better optimizers.In practice, we start with building the IOHprofiler benchmark software, which supports us in testing algorithms on a wide range of problems and allows us to perform and visualize the statistical analysis on algorithms' performance.While performing numerous benchmark studies, we study the impact of mutation rate and population size on the EAs and investigate how crossover and mutation interplay with each other and the impact of population size on the GAs. Moreover, we analyze a smooth way of interpolating between local and non-local search by proposing a new normalized bit mutation.We apply Irace, MIP-EGO, and MIES to configure the GA for ERT and AUC, respectively. Our results suggest that even when interested in ERT, it might be preferable to tune for AUC for the configuration task. We also observe that tuning for ERT is much more sensitive with respect to the budget that is allocated to the target algorithms.At last, we leverage our benchmark data of static algorithms to study dynamic algorithm selection. Show less
The research topic of the thesis is the extension of evolutionary multi-objective optimization for real-world scheduling problems. Several novel algorithms are proposed: the diversity indicator... Show moreThe research topic of the thesis is the extension of evolutionary multi-objective optimization for real-world scheduling problems. Several novel algorithms are proposed: the diversity indicator-based multi-objective evolutionary algorithm (DI-MOEA) can achieve a uniformly distributed solution set; the preference-based MOEA can obtain preferred solutions; the edge-rotated cone can improve the performance of MOEAs for many-objective optimization; and dynamic MOEA takes the stability as an extra objective. Besides the classical flexible job shop scheduling, the thesis proposes solutions for the novel problem domain of vehicle fleet maintenance scheduling optimization (VFMSO). The problem originated from the CIMPLO (Cross-Industry Predictive Maintenance Optimization Platform) project and the project partners Honda and KLM. The VFMSO problem is to determine the maintenance schedule for the vehicle fleet, meaning to find the best maintenance order, location and time for each component in the vehicle fleet based on the predicted remaining useful lifetimes of components and conditions of available workshops. The maintenance schedule is optimized to bring business advantages to industries, i.e., to reduce maintenance time, increase safety and save repair expenses. After formulating the problem as a scalable benchmark in an industrially relevant setting, the proposed algorithms have been successfully used to solve VFMSO problem instances. Show less
