A simple but effective memetic algorithm for the multidimensional assignment problem

Valencia, Carlos E.; Martínez, Francisco Javier Zaragoza; Pérez, Sergio Luis Pérez

doi:10.1109/iceee.2017.8108889

Cited by 3 publications

(1 citation statement)

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“…The size was calculated as a function of the runtime of the whole algorithm and the average runtime of the local search for the given instance. According to Valencia, Martinez, and Perez [22] this is the best known technique to solve the general case of k-AP. These authors performed an experimental evaluation of a basic genetic algorithm combined with a dimensionwise variation heuristic and showed its effectiveness in comparison to a more complex state-of-the-art memetic algorithm for k-AP.…”

Section: Literature Reviewmentioning

confidence: 99%

Multiple Hungarian Method for k-Assignment Problem

et al. 2020

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The k-assignment problem (or, the k-matching problem) on k-partite graphs is an NP-hard problem for k≥3. In this paper we introduce five new heuristics. Two algorithms, Bm and Cm, arise as natural improvements of Algorithm Am from (He et al., in: Graph Algorithms And Applications 2, World Scientific, 2004). The other three algorithms, Dm, Em, and Fm, incorporate randomization. Algorithm Dm can be considered as a greedy version of Bm, whereas Em and Fm 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 Am in terms of solution quality. On the first dataset with known optimal values the average relative error ranges from 1.47% over optimum (algorithm Am) to 0.08% over optimum (algorithm Em). On the second dataset with known optimal values the average relative error ranges from 4.41% over optimum (algorithm Am) to 0.45% over optimum (algorithm Fm). 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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Section: Literature Reviewmentioning

confidence: 99%