A multi-parent genetic algorithm for the quadratic assignment problem

Solving quadratic assignment problem by symbiotic organisms search algorithm

“…Four sets of test problems from the literature were used. One set was provided by Kennedy and Eberhart (1942), Riffi and Bouzidi (2014), Miranda et al (2005), one set was formed by Chmiel et al (2017), one set was tested by Burkard et al (1991), Riffi et al (2017), Ahmed (2015aAhmed ( , 2014 and the other set was tested by Mamaghani and Meybodi (2012). The table shows for each problem the best known solution obtained from QAPLIB, average error from the best knew solution, the number of times the best known solution was found by our proposed algorithm out of the 20 runs (no.…”

“…But large size instances cannot be solved by these methods because it takes a long time to achieve the optimal solutions, hence metaheuristic and heuristic approaches have been utilised to solve large size instances of QAP, because these approaches achieve high quality solution in short computation time. Harmony search (Amudha, 2012), simulated annealing (Wilhelm and Ward, 1987), Tabu search (Skorin-Kapov, 1990;Taillard, 1991), ant colony optimisation (Montero and Lopez, 2015), genetic algorithms (Azarbonyad and Babazadeh, 2014;Ahuja et al, 1995;Ahmed, 2015a), particle swarm optimisation (Mamaghani and Meybodi, 2012), discrete firefly (DFA) (Durkota, 2009), the discrete cat swarm optimisation algorithms (DCA) (Riffi and Bouzidi, 2014) and teaching-learning-based optimisation algorithms (Dokeroglu, 2015) consider robust tools to solve QAP efficiently. The metaheuristic approaches with their well-known ability can increase the solution quality for intractable problems.…”

A Hybrid Genetic-Hierarchical Algorithm for the Quadratic Assignment Problem

“…The advantage of this class of algorithms is that these algorithms operate with the sets of solutions instead of single solutions and this property is of prime importance when it comes to the solution of the QAP and related problems. In particular, it is found that, namely, the genetic algorithms (GA) seem to be very likely among the most powerful heuristic algorithms for solving the QAP, among them: greedy genetic algorithm [ 71 ], genetic-local search algorithm [ 72 , 73 , 74 ], genetic algorithm using cohesive crossover [ 75 ], improved genetic algorithm [ 76 ], parallel genetic algorithm [ 77 ], memetic algorithm [ 78 ], genetic algorithm on graphics processing units [ 79 ], quantum genetic algorithm [ 80 ], and other GA modifications [ 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ]. Note that the population-based algorithms are usually hybridized with the single-solution-based algorithms (local search, tabu search, iterated local/tabu search, GRASP).…”

A variable neighbourhood search enhanced estimation of distribution algorithm for quadratic assignment problems

“…The set of benchmark instances provided by various researchers are used for assessing the performance of these algorithms. Some of the familiar heuristic and metaheuristics algorithms reported in the literature include Simulated Annealing [72], Genetic Algorithms [5], Tabu Search [31], Ant Colony Optimization [57], Neural Networks [99], Memetic Algorithms [19] and Iterated Local Search [86] and these have been successful in solving QAP, at least to a near optimal solution. In Zaied and Shawky [107], Burkard et al [26] and Loiola et al [67] present detailed reviews on QAP with formulations, application areas and solution methodologies.…”