Problemas de otimização combinatória para união explícita de arestas

Furkejuvvon:
Bibliográfalaš dieđut
Váldodahkki: Ferreira, Joelma de Moura
Almmustuhttinbeaivi: 2018
Materiálatiipa: Doctoral thesis
Giella: por
Gáldu: Repositório Institucional da UFG
Download full: http://repositorio.bc.ufg.br/tede/handle/tede/8348
Čoahkkáigeassu: Edge bundling is a technique to group, align, coordinate and position the depiction of edges in a graph drawing, so that sets of edges appear to be brought together into shared visual structures, i.e. bundles. The ultimate goal is to reduce clutter to improve how it conveys information. This thesis provides a general formulation for the explicity edge bundling problems, as a formal combinatorial optimization problem. This allows for the definition and comparison of edge bundling problems. In addition, we present four explicity edge bundling optimization problems that address minimizing the total number of bundles, in conjunction with other aspects, as the main goal. An evolutionary edge bundling algorithm is described. The algorithm was successfully tested by solving three related problems applied to real-world instances. The reported experimental results demonstrate the effectiveness and the applicability of the proposed evolutionary algorithm to help resolve edge bundling problems formally defined as optimization models.
Govvádus
Čoahkkáigeassu:Edge bundling is a technique to group, align, coordinate and position the depiction of edges in a graph drawing, so that sets of edges appear to be brought together into shared visual structures, i.e. bundles. The ultimate goal is to reduce clutter to improve how it conveys information. This thesis provides a general formulation for the explicity edge bundling problems, as a formal combinatorial optimization problem. This allows for the definition and comparison of edge bundling problems. In addition, we present four explicity edge bundling optimization problems that address minimizing the total number of bundles, in conjunction with other aspects, as the main goal. An evolutionary edge bundling algorithm is described. The algorithm was successfully tested by solving three related problems applied to real-world instances. The reported experimental results demonstrate the effectiveness and the applicability of the proposed evolutionary algorithm to help resolve edge bundling problems formally defined as optimization models.