An Immune Genetic Algorithm to Buildings Displacement in Cartographic Generalization

Spatial conflicts may occur when map data are displayed at a scale smaller than that of the source map. This study applies the displacement operator in cartographic generalization to resolve such spatial conflicts and to improve the clarity and legibility of map. The immune genetic algorithm (IGA) is used in this study for buildings displacement to solve conflicts. IGA is based on the genetic algorithm (GA) and employs the self-adjusting mechanism of antibody concentration to enhance population diversity. Meanwhile, the elitism retention strategy is adopted in IGA to guarantee that the best individual (antibody) is not lost and destroyed in the next generation to strengthen convergence efficiency. The compared experiment between IGA and GA shows that the displacement result produced by IGA performs better than GA. Finally, in order to make the displaced map more attractive to cartographers, two constraints - the building alignment constraint and building tangent relation constraint - are applied in IGA to restrict the buildings' displacement. The same experimental data are adopted to prove that the improved IGA is useful for maintaining the two constraints.