Cheating on the N Queens benchmark
Many Solver distributions include an N Queens example,
in which n
queens need to be placed on a n*n
sized chessboard, with no attack opportunities.
So when you’re looking for the fastest Solver,
it’s tempting to use the N Queens example as a benchmark to compare those solvers.
That’s a tragic mistake, because the N Queens problem is solvable in polynomial time,
which means there’s a way to cheat.
That being said, OptaPlanner solves the 1 000 000
queens problem in less than 3 seconds :)
Here’s a log to prove it (with time spent in milliseconds):
INFO Opened: data/nqueens/unsolved/10000queens.xml INFO Solving ended: time spent (23), best score (0), ... INFO Opened: data/nqueens/unsolved/100000queens.xml INFO Solving ended: time spent (159), best score (0), ... INFO Opened: data/nqueens/unsolved/1000000queens.xml INFO Solving ended: time spent (2981), best score (0), ...
How to cheat on the N Queens problem
The N Queens problem is not NPcomplete, nor NPhard.
That is math speak for stating that there’s a perfect algorithm to solve this problem:
the Explicits Solutions algorithm.
Implemented with a CustomPhaseCommand
in OptaPlanner it looks like this:
public class CheatingNQueensPhaseCommand implements CustomPhaseCommand {
public void changeWorkingSolution(ScoreDirector scoreDirector) {
NQueens nQueens = (NQueens) scoreDirector.getWorkingSolution();
int n = nQueens.getN();
List<Queen> queenList = nQueens.getQueenList();
List<Row> rowList = nQueens.getRowList();
if (n % 2 == 1) {
Queen a = queenList.get(n  1);
scoreDirector.beforeVariableChanged(a, "row");
a.setRow(rowList.get(n  1));
scoreDirector.afterVariableChanged(a, "row");
n;
}
int halfN = n / 2;
if (n % 6 != 2) {
for (int i = 0; i < halfN; i++) {
Queen a = queenList.get(i);
scoreDirector.beforeVariableChanged(a, "row");
a.setRow(rowList.get((2 * i) + 1));
scoreDirector.afterVariableChanged(a, "row");
Queen b = queenList.get(halfN + i);
scoreDirector.beforeVariableChanged(b, "row");
b.setRow(rowList.get(2 * i));
scoreDirector.afterVariableChanged(b, "row");
}
} else {
for (int i = 0; i < halfN; i++) {
Queen a = queenList.get(i);
scoreDirector.beforeVariableChanged(a, "row");
a.setRow(rowList.get((halfN + (2 * i)  1) % n));
scoreDirector.afterVariableChanged(a, "row");
Queen b = queenList.get(n  i  1);
scoreDirector.beforeVariableChanged(b, "row");
b.setRow(rowList.get(n  1  ((halfN + (2 * i)  1) % n)));
scoreDirector.afterVariableChanged(b, "row");
}
}
}
}
Now, one could argue that this implementation doesn’t use any of OptaPlanner’s algorithms (such as the Construction Heuristics or Local Search). But it’s straightforward to mimic this approach in a Construction Heuristic (or even a Local Search). So, in a benchmark, any Solver which simulates that approach the most, is guaranteed to win when scaling out.
Why doesn’t that work for other planning problems?
This algorithm is perfect for N Queens, so why don’t we use a perfect algorithm on other planning problems? Well, simply because there are none!
Most planning problems, such as vehicle routing, employee rostering, cloud optimization, bin packing, … are proven to be NPcomplete (or NPhard). This means that these problems are in essence the same: a perfect algorithm for one, would work for all of them. But no human has ever found such an algorithm (and most experts believe no such algorithm exists).
Note: There are a few notable exceptions of planning problems that are not NPcomplete, nor NPhard. For example, finding the shortest distance between 2 points can be solved in polynomial time with A*Search. But their scope is narrow: finding the shortest distance to visit n points (TSP), on the other hand, is not solvable in polynomial time.
Because N Queens differs intrinsically from real planning problems, it is a terrible use case to benchmark.
Conclusion
Benchmarks on the N Queens problem are meaningless. Instead, benchmark implementations of a realistic competition. A realistic competition is an official, independent competition:

that clearly defines a realword use case

with realworld constraints

with multiple, realworld datasets

that expects reproducible results within a specific time limit on specific hardware

that has had serious participation from the academic and/or enterprise Operations Research community
OptaPlanner's examples implement several cases of realistic competitions.
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