Fitness landscape analysis provides insights into optimization problems, informing algorithms' design and identifying properties that influence performance. While understanding global landscape structure is critical, tools for analyzing and visualizing multi-objective, high-dimensional optimization problems remain limited. Recent models, such as Pareto local optima solution networks (PLOS-nets), primarily focus on small instances and binary representations, posing challenges for extension to more complex domains. To address this gap, we introduce mo-LON/D, a decomposition-based local optima network model for multi-objective landscapes. This model partitions a multi-objective problem into scalar sub-problems, constructs standard single-objective local optima networks (LONs) for each, and integrates them via a graph union. We validate mo-LON/D on fully enumerated bi-objective ρmnk-landscapes and contrast its structural features against PLOS-nets both visually and quantitatively. Despite the inherent sampling involved in scalarization, our results indicate that mo-LON/D offers comparable explanatory power (and even higher correlations) with respect to the performance of state-of-the-art algorithms. By harnessing established sampling techniques from single-objective research, mo-LON/D could potentially provide a scalable framework for characterizing complex multi-objective landscapes.

In many real-world problems, instances arrive in a stream which is likely to experience drift in the instance space over time. If a classical algorithm selector is trained offline, i.e., on an initial part of the instance stream, downstream performance is often negatively impacted due to drift in the instance data. To overcome this limitation of classical algorithm selectors, we propose a novel online automated algorithm selection framework that first uses instance features to detect drift, and then periodically retrains a selector if drift occurs, ensuring continuity of performance in face of data-drift. To further improve both the effectiveness and efficiency of retraining, we also propose a process to continuously gather new training samples on the fly. Empirical comparison using a bin-packing scenario under three different drift scenarios shows that our framework is efficient in terms of the computational effort required to train a selector while maintaining good performance with respect to accuracy compared to several baselines.

This paper presents a distributed resource selection mechanism for diverse cloud-edge environments, enabling dynamic and context-aware allocation of resources to meet the demands of complex distributed applications. By distributing the decision-making process, our approach ensures efficiency, scalability, and resilience in highly dynamic cloud-edge environments where centralised coordination becomes a bottleneck. The proposed mechanism aims to function as a core component of a broader, distributed, and self-organising orchestration system that facilitates the intelligent placement and adaptation of applications in real-time. This work leverages a consensus-based mechanism utilising local knowledge and inter-agent collaboration to achieve efficient results without relying on a central controller, thus paving the way for distributed orchestration. Our results indicate that computation time is the key factor influencing allocation decisions. Our approach consistently delivers rapid allocations without compromising optimality or incurring additional cost, achieving timely results at scale where exhaustive search is infeasible and centralised heuristics run up to 30 times slower.

The choice of input-data used to train algorithm-selection models is recognised as being a critical part of the model success. Recently, feature-free methods for algorithm-selection that use short trajectories obtained from running a solver as input have shown promise. However, it is unclear to what extent these trajectories reliably discriminate between solvers. We propose a meta approach to generating discriminatory trajectories with respect to a portfolio of solvers. The algorithm-configuration tool irace is used to tune the parameters of a simple Simulated Annealing algorithm (SA) to produce trajectories that maximise the performance metrics of ML models trained on this data. We show that when the trajectories obtained from the tuned SA algorithm are used in ML models for algorithm-selection and performance prediction, we obtain significantly improved performance metrics compared to models trained both on raw trajectory data and on exploratory landscape features.

The configuration of radar networks is a complex problem that is often performed manually by experts with the help of a simulator. Different numbers and types of radars as well as different locations that the radars shall cover give rise to different instances of the radar configuration problem. The exact modeling of these instances is complex, as the quality of the configurations depends on a large number of parameters, on internal radar processing, and on the terrains on which the radars need to be placed. Classic optimization algorithms can therefore not be applied to this problem, and we rely on "trial-and-error" black-box approaches.