Abstract
In this paper, a 2-level iterated tabu search (ITS) algorithm for the solution of the quadratic assignment problem (QAP) is considered. The novelty of the proposed ITS algorithm is that the solution mutation procedures are incorporated within the algorithm, which enable to diversify the search process and eliminate the search stagnation, thus increasing the algorithm’s efficiency. In the computational experiments, the algorithm is examined with various implemented variants of the mutation procedures using the QAP test (sample) instances from the library of the QAP instances – QAPLIB. The results of these experiments demonstrate how the different mutation procedures affect and possibly improve the overall performance of the ITS algorithm.
Highlights
The novelty of the proposed iterated tabu search (ITS) algorithm is that the solution mutation procedures are incorporated within the algorithm
//įsimenamas geriausias rastas sprendinys if q2
International Journal of Innovative Computing, Information and Control, vol 2, p
Summary
Šiame straipsnyje nagrinėjamas vadinamasis dviejų lygių iteracinis tabu paieškos (ITP) algoritmas kvadratinio paskirstymo (KP) uždaviniui. Pagrindiniai žodžiai: skaitmeninis intelektas, kombinatorinis optimizavimas, euristiniai optimizavimo algoritmai, tabu paieška, mutavimo procedūros, kvadratinio paskirstymo uždavinys. Tokiu būdu z (tiksliau, z/2) gali būti suprantamas kaip bendras sujungimų tarp komponentų ilgis, kuomet visi n komponentai yra išdėstyti atitinkamose n pozicijų. Tabu search) (Taillard, 1991; Misevicius, 2005; Shylo, 2017), godžiosios randomizuotos adaptyvios paieškos procedūros Algoritmo veikimo principo naujoviškumas yra tas, kad tabu paieškos procedūra yra vykdoma iteraciniu būdu, pradedant vis nuo naujo sprendinio (pereinant tarp skirtingų lokaliai optimalių sprendinių). Po to aprašomas dviejų lygių iteracinis tabu paieškos algoritmas ir į jį integruotos sprendinių mutavimo procedūros.
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