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Evolutionary algorithms for optimizing pessimistic bilevel problems and min-max problems

Author(s): Margarita Antoniou (Author), Gregor Papa (Supervisor)

Year: 2025

Type: Doctoral dissertation

This thesis investigates two distinct but related optimization problems: bilevel and minmax problems, in the context of evolutionary algorithms (EAs). Bilevel optimization involves hierarchical decision-making, where decisions at the upper level are subject to constraints defined by the solutions of an optimization problem at the lower level. These problems are …

Characterization of constrained continuous multiobjective optimization problems

Author(s): Aljoša Vodopija (Author), Bogdan Filipič (Supervisor)

Year: 2024

Type: Doctoral dissertation

Despite the large volume of recently published papers in the field of constrained multiobjective optimization, the understanding and characterization of constrained multiobjective optimization problems (CMOPs) for benchmarking multiobjective evolutionary algorithms (MOEAs) and the related constraint handling techniques (CHTs) remain unsatisfactory. Therefore, selecting appropriate CMOPs for benchmarking is challenging and lacks …

Towards understanding the impact of problem landscapes in numerical black-box optimization

Author(s): Urban Škvorc (Author), Peter Korošec (Supervisor), Tome Eftimov (Co-Supervisor)

Year: 2023

Type: Doctoral dissertation

In optimization, it is well known that algorithm performance is dependent on the problem being solved. As a consequence of this, achieving good optimization results requires correctly matching an optimization problem to a specific optimization algorithm that performs well on that problem. For this to be possible, knowledge of both …

Parallelization of an Evolutionary Algorithm for Multiobjective Optimization

Author(s): Matjaž Depolli (Author), Bogdan Filipič (Supervisor), Roman Trobec (Co-Supervisor)

Year: 2010

Type: Doctoral dissertation

Solving real-life optimization problems numerically is often very time demanding, because of high complexity of the simulations that are usually involved. Solving such problems becomes highly impractical for this reason and can even lead to use of less complex and also less accurate models. Fortunately, evolutionary algorithms, often used in …

Stigmergy as an Approach to Metaheuristic Optimization

Author(s): Peter Korošec (Author), Bogdan Filipič (Supervisor)

Year: 2006

Type: Doctoral dissertation

Developing metaheuristics to solve optimization problems is a rapidly growing field of research. This is due to the importance of optimization problems in the scientific as well as the industrial world. The methods developed in this dissertation are based on stigmergy: a method of communication in emergent systems, where the …