School of Engineering Science
Henrik Smedberg defends his thesis "Knowledge discovery for interactive decision support and knowledge-driven optimization".
The dissertation is held at ASSAR Industrial Innovation Arena but is also streamed online.
Join the livestream at https://play.quickchannel.com/play/cvxxzas
Multi-objective optimization involves the simultaneous optimization of several objective functions. In real-world problems, these objectives are often in conflict, giving rise to trade-offs in the optimal solutions from the optimization process. All these solutions are equally viable, with no single solution being better or worse than the others. Typically, decision makers have certain preferences that guide the selection of a final solution for practical implementation. While most multi-criteria decision analysis methods focus on the performance of solutions in the objective space, it is important to note that practically relevant knowledge is often found in the design space. Access to this knowledge can provide decision makers with meaningful insights into the problem and the optimization process, leading to more informed decision-making.
This thesis develops and employs methods for knowledge discovery in the context of multi-objective optimization. By emphasizing explicit knowledge representations, this thesis investigates how extracted knowledge can be processed and presented to decision-makers in an interactive manner for insightful decision support. This thesis also explores how extracted knowledge from preferred solutions can be integrated into the algorithms or the multi-objective optimization problem itself, to improve the convergence behavior of optimization algorithms. This approach, called Knowledge-Driven Optimization (KDO), can be implemented either offline or online. Offline KDO involves incorporating knowledge obtained from previous optimization runs into future problem scenarios of a similar nature, restricting the search process to preferred regions of the objective space. A main challenge with such approaches is the storage and retrieval of relevant past knowledge, as well as modifications to the optimization problem formulation. In contrast, online KDO involves integrating knowledge discovery methods with optimization algorithms and utilizing the knowledge obtained during their runtime to enhance the search process, driving algorithms towards better convergence in preferred regions of the objective space. This approach necessitates the development of new search operators capable of incorporating and exploiting various forms of knowledge.
In both offline and online KDO, the veracity and accuracy of the extracted knowledge are critical factors. The thesis validates the effectiveness of the developed methods using various benchmark and engineering optimization test problems, and use-cases from the manufacturing industry. A particular focus is given to generating explicit knowledge that is both meaningful to human decision makers, and can easily be processed algorithmically. The main contributions of this thesis are methods for discovering relevant knowledge about the convergence characteristics of problems, a decision support system for interactive knowledge discovery, and algorithms for realizing both offline and online KDO by incorporating knowledge into the optimization process.
Professor Jonathan Fieldsend, University of Exeter, UK
Associate Professor Sunith Bandaru, University of Skövde
Professor Amos H.C. Ng, University of Skövde
Professor Maria Riveiro, Jönköping University
Professor Bogdan Filipič, Jožef Stefan Institute, Slovenia
Doctor (Reader) Elizabeth Wanner, Aston University, UK
Professor Mats Gustafsson, Uppsala University
Docent Gunnar Mathiason, University of Skövde