An improved hybrid optimization algorithm for the quadratic assignment problem

Abstract

In this paper, we present an improved hybrid optimization algorithm, which was applied to the hard combinatorial optimization problem, the quadratic assignment problem (QAP). This is an extended version of the earlier hybrid heuristic approach proposed by the author. The new algorithm is distinguished for the further exploitation of the idea of hybridization of the well‐known efficient heuristic algorithms, namely, simulated annealing (SA) and tabu search (TS). The important feature of our algorithm is the so‐called “cold restart mechanism”, which is used in order to avoid a possible “stagnation” of the search. This strategy resulted in very good solutions obtained during simulations with a number of the QAP instances (test data). These solutions show that the proposed algorithm outperforms both the “pure” SA/TS algorithms and the earlier author's combined SA and TS algorithm. Key words: hybrid optimization, simulated annealing, tabu search, quadratic assignment problem, simulation

Šiame straipsnyje pasi ulytas patobulintas hibridinis euristinis optimizavimo algoritmas gerai žinomam, sudetingam kombinatorinio optimizavimo uždaviniui, b utent, kvadratinio paskirstymo (KP) uždaviniui. Tai‐pagerinta autoriaus ankstesnio hibridinio algoritmo versija. Naujasis algoritmas pasižymi tuo, jog čia išpletota efektyviu euristiku (atkaitinimo modeliavimo (AM) (angi. simulated annealing) ir tabu paieškos (TP) (angi. tabu search) “hibridizacijos” ideja. “Hibridizacija” remiasi vadinamaja iteracine schema: TP algoritmas panaudojamas kaip post‐analizes proced ura AM algoritmo gautajam sprendiniui, savo ruožtu, AM algoritmas taikomas sprendiniu sekai, gautai sprendiniu diversifikavimo/generavimo keliu. Svarbi pasi ulyto algoritmo savybe yra ir ta, kad jame realizuotas vadinamasis “šaltojo pakartotinio starto” principas, kurio paskirtis padeti išvengti galimu paieškos “stagnacijos” situaciju. Naujasis algoritmas išbandytas su KP uždavinio duomenimis iš testiniu pavyzdžiu bibliotekos QAPLIB. Gauti eksperimentu rezultatai liudija, jog nagrinetiems KP uždavinio pavyzdžiams si ulomas algoritmas yra pranašesnis už ankstesnius atkaitinimo modeliavimo ir tabu paieškos algoritmus, taip pat už ankstesni autoriaus hibridini algoritma.