I want to start building an app for public transport. I know I should use Dijkstra as the algorithm to find the shortest path between two points.
How can I get more than one path? I would like to give the user at least 3 or 4 options, not only the best route. The reason is that I want to include more variables, time, cost and bus capacity.
Is there any algorithm that could help me to do this? Or naively I was thinking I should modify Dijkstra to give me more than one path.
Cheers.
have a look at K shortest path routing, a generalization of dijkstra.
Related
The question itself is pretty simple... This is a vechicle routing problem.
I have a directed graph
I need to get a linear programming model that will somehow tell me the shortest circuit that visits all nodes, and starts and ends at the star. You are allowed to go over an edge more than once. A node is any crossing on the image.
We had like 4 hours of this in class, I have no idea where to even start...
I'm not expection anyone to give me the full model, but I was hoping someone would tell a strategy so I could do this.
Thank you in advance.
I would start from Dijkstra's algorithm for undirected graphs. There are some variants with similar or better performance. Take a look at https://en.wikipedia.org/wiki/Shortest_path_problem#Undirected_graphs, pick a choice, and keep us informed ... :)
The answer here seems to be very easy. (That is, very easy in theory, very hard and a lot of work to code in practice).
This seems like a straight-forward TSP (Travelling Salesperson Problem). Read about some general literature on TSP. You need to set up and solve a TSP where your nodes are the "cities" in a TSP. You also need to include your star as a city in the TSP.
The Dijkstra algorithm will not give you a solution. The Dijkstra algorithm is used for finding fastest/shortest paths between a node and other nodes in a (typically road-) network. However, distance-wise your problem is super-simple: Getting the cheapest travel cost (and path) from one node to another node in your problem is (almost) trivial.
If you are to solve this problem "for real" (not just discuss it), you need to acquire a TSP solver that is able to take your network (both edges and nodes) as input. Your input needs to specify which edges are directed and which are uni-directed. A lot of work in practice: Even if you use a tool, you still need to familiarize yourself with the tool.
I would like to test my recently created algorithm on large (50+ node) graphs. Preferrably, they would specifically be challenging graphs, and known tours would exist (for at least most of them).
Problem sets for this problem do not seem as easy to find as with the TSP. I am aware of the Flinder's challenge set available at http://www.flinders.edu.au/science_engineering/csem/research/programs/flinders-hamiltonian-cycle-project/fhcpcs.cfm
However, they seem to be directed. I can probably alter my algorithm to work for directed, but it will take time and likely induce bugs. I'd prefer to know if it can work for undirected first.
Does anyone know where problem sets are available? Thank you.
quick edit:
Now I am unsure if the flinder's set is directed or not.... It doesn't say. Examples make it seem like maybe it actually is undirected.
Check this video:
https://www.youtube.com/watch?v=G1m7goLCJDY
Also check the in depth sequel to the video.
You can determine yourself how many nodes you want to add to the graph.
It does require you to construct the data yourself, which should be deable.
One note: the problem is about a path, not a cycle, but you can overcome this by connecting the start and end node.
In this example (please see picture), the Manhattan heuristic delays the path because of the unwalkable block west of the destination block.
My question is how can I fix this? Do I need to keep checking the blocks on the open list (the gray blocks) even after I found the destination? I might as well use dijkstra if I have to do that. Do I have to live with an imperfection like this if I go with a-star or is there a solution?
I have done my research on it and implemented my own algorithm that works exactly as the tutorials / articles on the web have explained, but I keep running into specific instances like this where a* fails to find the shortest path.
Your heuristic needs to be admissible but is not. Use Diagonal or Euclidean distance instead.
I am studying graph databases (Neo4J).
I know graph databases come from graph theory, which has basis on Euler paper to solve the 7 bridges problem.. eulerian path/eulerian circuit.
I am trying to find an example, something, to show how graph databases use the Eulerian path to solve some problem.
Ideally I would need a real example from a source, but even any help to understand this better.. thanks!
I believe you have a misimpression. Even though Euler wrote a paper based on the 7 bridges problem (which was probably the first paper on graph theory), that does not mean that the design of neo4j uses his solution to that specific problem in any way.
I want to use a Genetic Algorithm for finding the shortest path within an undirected graph. I have two questions about this regarding Crossover and Mutation. I've been researching how crossover can be performed in similar situations to this, and the most popular algorithm out there seems to be PMX, my understanding of this is a partial path is swapped between 2 parent chromosomes to make the offspring. The issue I'm having with this is, there is massive scope for almost all of the offspring to become invalid isn't there? I was wondering if you could confirm this for me, and if I'm wrong, please correct me and explain it.
On separate but relevant note; I did have an idea for how to do this, but I don't know if its a good idea, simply select 2 parents where they share the same node in their path and crossover at that point, so all of the offspring remain valid.
My second issue is with Mutation; I have a general idea for how this can be done; would it be wise to select one node and remove it, and relink the path by an alternative means?
Thanks :)!
First of all you study about the cross over and mutation, because you said that you may loss some efficient parent, but it the concept of " Elitism " in genetic algorithm. that you should overcome by your proposed cross over method. because in cross over itself we are having n method to do, i suggest you to do second variation cross over order. This is not murphy's law, so try hard you will achieve.