Multi-layer local optima networks for the analysis of advanced local search-based algorithms

A Local Optima Network (LON) is a graph model that compresses the fitness landscape of a particular combinatorial optimization problem based on a specific neighborhood operator and a local search algorithm. Determining which and how landscape features affect the effectiveness of search algorithms is relevant for both predicting their performance and improving the design process. This paper proposes the concept of multi-layer LONs as well as a methodology to explore these models aiming at extracting metrics for fitness landscape analysis. Constructing such models, extracting and analyzing their metrics are the preliminary steps into the direction of extending the study on single neighborhood operator heuristics to more sophisticated ones that use multiple operators. Therefore, in the present paper we investigate a twolayer LON obtained from instances of a combinatorial problem using bitflip and swap operators. First, we enumerate instances of NK-landscape model and use the hill climbing heuristic to build the corresponding LONs. Then, using LON metrics, we analyze how efficiently the search might be when combining both strategies. The experiments show promising results and demonstrate the ability of multi-layer LONs to provide useful information that could be used for in metaheuristics based on multiple operators such as Variable Neighborhood Search.Comment: Accepted in GECCO202

Monitor, anticipate, respond, and learn: developing and interpreting a multilayer social network of resilience abilities

Resilient performance is influenced by social interactions of several types, which may be analysed as layers of interwoven networks. The combination of these layers gives rise to a “network of networks”, also known as a multilayer network. This study presents an approach to develop and interpret multilayer networks in light of resilience engineering. Layers correspond to the four abilities of resilient systems: monitor, anticipate, respond, and learn. The proposal is applied in a 34-bed intensive care unit. To map relationships between actors in each layer, a questionnaire was devised and answered by 133 staff members, including doctors, nurses, nurse technicians, and allied health professionals. Two multilayer networks were developed: one considering that actors are 100% available and reliable (work-as-imagined) and another considering suboptimal availability and reliability (work-as-done). The multilayer networks were analysed through actor-centred (Katz centrality, degree deviation, and neighbourhood centrality) and layer-centred metrics (inter-layer correlation, and assortativity correlation). Strengths and weaknesses of social interactions at the ICU are discussed based on the adopted metrics