The
k-assignment problem (or, the
k-matching problem) on
k-partite graphs is an NP-hard problem for
. In this paper we introduce five new heuristics. Two algorithms,
and
, arise as natural improvements of Algorithm
from (He et al., in: Graph Algorithms And Applications 2, World Scientific, 2004). The other three algorithms,
,
, and
, incorporate randomization. Algorithm
can be considered as a greedy version of
, whereas
and
are versions of local search algorithm, specialized for the
k-matching problem. The algorithms are implemented in Python and are run on three datasets. On the datasets available, all the algorithms clearly outperform Algorithm
in terms of solution quality. On the first dataset with known optimal values the average relative error ranges from 1.47% over optimum (algorithm
) to 0.08% over optimum (algorithm
). On the second dataset with known optimal values the average relative error ranges from 4.41% over optimum (algorithm
) to 0.45% over optimum (algorithm
). Better quality of solutions demands higher computation times, thus the new algorithms provide a good compromise between quality of solutions and computation time.
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