fixes an warning un a model (Agent movement)

msi.gama.models/models/Modeling/Spatial Topology/Agent movement/models/Goto Network.gaml

@@ -16,23 +16,30 @@ global {
 	bool load_shortest_paths <- false;
 	string shortest_paths_file <- "../includes/shortest_paths.csv";
 	bool memorize_shortest_paths <- true;
-
-	/*6 types of optimizer (algorithms) can be used for the shortest path computation:
+		
+	/*11 shortest path algorithms can be used for the shortest path computation:
 	 *    - Dijkstra: ensure to find the best shortest path - compute one shortest path at a time: https://en.wikipedia.org/wiki/Dijkstra%27s_algorithm
-	 * 	  - BellmannFord: ensure to find the best shortest path - compute one shortest path at a time: https://en.wikipedia.org/wiki/Bellman-Ford_algorithm
-	 * 	  - AStar: ensure to find the best shortest path - compute one shortest path at a time: https://en.wikipedia.org/wiki/A*_search_algorithm
-	 *    - NBAStar: the default one - ensure to find the best shortest path - compute one shortest path at a time: http://repub.eur.nl/pub/16100/ei2009-10.pdf
-	 *    - NBAStarApprox : does not ensure to find the best shortest path - compute one shortest path at a time: http://repub.eur.nl/pub/16100/ei2009-10.pdf
+	 * 	  - BidirectionalDijkstra: default one - ensure to find the best shortest path - compute one shortest path at a time: https://www.homepages.ucl.ac.uk/~ucahmto/math/2020/05/30/bidirectional-dijkstra.html
+	 *    - BellmannFord: ensure to find the best shortest path - compute one shortest path at a time: https://en.wikipedia.org/wiki/Bellman-Ford_algorithm
+	 * 	  - AStar: ensure to find the best shortest path if for any edge, the weight of the edge is greater than or equal to the distance between the source and the target of the edge. - compute one shortest path at a time: https://en.wikipedia.org/wiki/A*_search_algorithm
+	 *    - NBAStar: ensure to find the best shortest path if for any edge, the weight of the edge is greater than or equal to the distance between the source and the target of the edge.- compute one shortest path at a time: http://repub.eur.nl/pub/16100/ei2009-10.pdf
+	 *    - NBAStarApprox: does not ensure to find the best shortest path - compute one shortest path at a time: http://repub.eur.nl/pub/16100/ei2009-10.pdf
 	 *    - FloydWarshall: ensure to find the best shortest path - compute all the shortest paths at the same time (and keep them in memory): https://en.wikipedia.org/wiki/Floyd-Warshall_algorithm
+	 * 	  - BellmannFord: ensure to find the best shortest path - compute one shortest path at a time: https://en.wikipedia.org/wiki/Bellman-Ford_algorithm
+	 *    - DeltaStepping: ensure to find the best shortest path - compute one shortest path at a time: The delta-stepping algorithm is described in the paper: U. Meyer, P. Sanders, $\Delta$-stepping:  a parallelizable shortest path algorithm, Journal of Algorithms, Volume 49, Issue 1, 2003, Pages 114-152, ISSN 0196-6774
+	 *    - CHBidirectionalDijkstra: ensure to find the best shortest path - compute one shortest path at a time. Based on precomputations (first call of the algorithm). Implementation of the hierarchical query algorithm based on the bidirectional Dijkstra search. The query algorithm is originally described the article: Robert Geisberger, Peter Sanders, Dominik Schultes, and Daniel Delling. 2008. Contraction hierarchies: faster and simpler hierarchical routing in road networks. In Proceedings of the 7th international conference on Experimental algorithms (WEA'08), Catherine C. McGeoch (Ed.). Springer-Verlag, Berlin, Heidelberg, 319-333
+	 *    - TransitNodeRouting: ensure to find the best shortest path - compute one shortest path at a time. Based on precomputations (first call of the algorithm). The algorithm is designed to operate on sparse graphs with low average outdegree. the algorithm is originally described the article: Arz, Julian &amp; Luxen, Dennis &amp; Sanders, Peter. (2013). Transit Node Routing Reconsidered. 7933. 10.1007/978-3-642-38527-8_7.
 	 */
-	string optimizer_type <- "NBAStar" among: ["NBAStar", "NBAStarApprox", "Dijkstra", "AStar", "BellmannFord", "FloydWarshall"];
+
+	string shortest_path_algo <- #BidirectionalDijkstra among: [#NBAStar, #NBAStarApprox, #Dijkstra, #AStar, #BellmannFord, #FloydWarshall, #BidirectionalDijkstra, #CHBidirectionalDijkstra, #TransitNodeRouting];
+
 	int nb_people <- 100;
 	init {    
 		create road from: shape_file_in ;
 		the_graph <- as_edge_graph(list(road));
 
 		//allows to choose the type of algorithm to use compute the shortest paths
-		the_graph <- the_graph with_optimizer_type optimizer_type;
+		the_graph <- the_graph with_shortest_path_algorithm shortest_path_algo;
 
 		//allows to define if the shortest paths computed should be memorized (in a cache) or not
 		the_graph <- the_graph use_cache memorize_shortest_paths;
@@ -84,7 +91,7 @@ species people skills: [moving] {
 
 
 experiment goto_network type: gui {
-	parameter "Type of optimizer" var: optimizer_type ;
+	parameter "Shortest path algorithm" var: shortest_path_algo ;
 	parameter "Number of people" var: nb_people min: 1 max: 10000;
 	parameter "Computed all the shortest paths and save the results" var: save_shortest_paths;
 	parameter "Load the shortest paths from the file" var: load_shortest_paths;