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## A GREEDY RANDOMIZED ADAPTIVE SEARCH PROCEDURE FOR THE POINT-FEATURE CARTOGRAPHIC LABEL PLACEMENT

Citations: | 8 - 0 self |

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Citation Context ...02). The second formulation proposed by the authors has a compact number of constraints and is reproduced bellow. Where: Subject to: ∑∑ ∑ = = ∈ ⎟ ⎟⎟ N Pi ⎛ ⎞ ⎜ v( MNCP) = Min ⎜ wi, j xi, j + yi, j, c =-=(1)-=- i 1 j 1 ⎜ ⎝ c Ci, j ⎠ Pi ∑ j= 1 C x x i, j i, j i , j x , x i, j k, t = 1 ∀ i = 1... N + ∑ x − ∑ y ≤ C ∀ i = 1... N k, t i, j, c i, j ( k, t) ∈ S c∈C (3) and y i, j i, j, c ∈ { 0, 1} i, j ∀ i = 1... ... |

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Citation Context ...position. It allowed us to prioritize some candidate positions as shown in Figure 2; • Si,j is a set of index pairs (k,t):k>i of candidate positions such that xk,t has potential conflict with xi,j; i =-=(2)-=- (4) 7s• Ci,j is a set with all points that contain candidate positions in conflict with the candidate position xi,j; and • yi,j,c is a conflict variable between the candidate position xi,j and the po... |

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Citation Context ...lect the restricted list, the method CreateRCL calculates the weight of each vertex considering the following equation: Weight(x) = CartographicPreference(x) + Degree(x) +M* PotentialConflicts(Sol,x) =-=(5)-=- Where: • CartographicPreference(x) returns the cartographic preference of the vertex x; • Degree(x) returns the degree of the vertex x considering the current conflict graph; • PotentialConflicts(Sol... |

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Citation Context ...⎛ ⎞ ⎜ v( MNCP) = Min ⎜ wi, j xi, j + yi, j, c (1) i 1 j 1 ⎜ ⎝ c Ci, j ⎠ Pi ∑ j= 1 C x x i, j i, j i , j x , x i, j k, t = 1 ∀ i = 1... N + ∑ x − ∑ y ≤ C ∀ i = 1... N k, t i, j, c i, j ( k, t) ∈ S c∈C =-=(3)-=- and y i, j i, j, c ∈ { 0, 1} i, j ∀ i = 1... N ∀ j = 1... P c ∈ C i, j i ∀ j = 1... P • N is the number of points to be labeled and Pi is the set of candidate positions of point i; • xi,j is a binary... |

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