Bees Algorithm for the Quadratic Assignment Problem on CUDA Platform

Chmiel, Wojciech; Szwed, Piotr

doi:10.1007/978-3-319-23437-3_53

Cited by 4 publications

(5 citation statements)

References 17 publications

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“…Er = (δ avg − BKS) BKS × 100 (15) where BKS is the best known solution, S best is the best found value and δ avg the average of the best found solution. Figure 4a and Figure 4b show the results obtained while applying PHCSO algorithm and the comparison with other algorithms from the literature such as DBA [54] and BeA [55]. The experiments show that PHCSO algorithm is able to provide better results which prove that combining constructive procedure of GRASP and Tabu search neighborhood mechanism in the equation of rooster is able to enhance the performance of our algorithm.…”

Section: Computational Resultsmentioning

confidence: 78%

“…As result, we found that when we applied nonparametric Wilcoxon signed-rank tests, at the = 0.05, the null hypotheses are accepted in the case since the QAP is a minimization problem, which means that samples in all groups of the population are obtained from groups with the same mean value. We observe from Table 4 that PHCSO returns better solutions than BeA [55], HPSO [56] and DBA [54] and the p-values are smaller than the corresponding Holm step-down threshold, which signify that the use of constructive procedure of GRASP and Tabu Search greatly improve the performance of PHCSO.…”

Section: Computational Resultsmentioning

confidence: 99%

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Parallel hybrid chicken swarm optimization for solving the quadratic assignment problem

Semlali

Riffi

Chebihi

2019

IJECE

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<p>In this research, we intend to suggest a new method based on a parallel hybrid chicken swarm optimization (PHCSO) by integrating the constructive procedure of GRASP and an effective modified version of Tabu search. In this vein, the goal of this adaptation is straightforward about the fact of preventing the stagnation of the research. Furthermore, the proposed contribution looks at providing an optimal trade-off between the two key components of bio-inspired metaheuristics: local intensification and global diversification, which affect the efficiency of our proposed algorithm and the choice of the dependent parameters. Moreover, the pragmatic results of exhaustive experiments were promising while applying our algorithm on diverse QAPLIB instances . Finally, we briefly highlight perspectives for further research.</p>

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Section: Computational Resultsmentioning

confidence: 78%

Section: Computational Resultsmentioning

confidence: 99%

Parallel hybrid chicken swarm optimization for solving the quadratic assignment problem

Semlali

Riffi

Chebihi

2019

IJECE

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“…In this example the flows and distances are A comparison of nature inspired algorithms for the quadratic assignment problem asymmetric. For example, notation (3,2) for facilities means that the flow from facility 1 to facility 2 is 3 and inversely 2.…”

Section: Koopmans-beckmann Quadratic Assignment Problemmentioning

confidence: 99%

“…QAP belongs to the class of NP-hard problems. This is the reason why the approximation algorithms are used for the instances bigger than 30 [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

A comparison of nature inspired algorithms for the quadratic assignment problem

Chmiel

Kadłuczka

Kwiecień

et al. 2017

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract. This paper presents an application of the ant algorithm and bees algorithm in optimization of QAP problem as an example of NPhard optimization problem. The experiments with two types of algorithms: the bees algorithm and the ant algorithm were performed for the test instances of the quadratic assignment problem from QAPLIB, designed by Burkard, Karisch and Rendl. On the basis of the experiments results, an influence of particular elements of algorithms, including neighbourhood size and neighbourhood search method, will be determined. The QAP is an NP-hard problem and this difficulty is not restricted only to finding the optimal solution. Sahni and Gonzalez [4] proved that even finding an ɛ-approximation solution for QAP is a hard problem in this sense that the existence of a ɛ-approximation algorithm implies P = NP.Finding an optimal solution to QAP is a difficult task not only in case of looking for the best solution among all the feasible ones. It might appear that finding an optimal solution in the subset of the feasible solutions can be easier. For QAP it was proven that finding an optimal solution in case of the local search is a difficult problem too. Johnson and Papadimitriou in [6] created the base for the complexity theory in the local search case, where a special structure of neighbourhood is introduced. They define the PLS-problems (polynomial-time local search problem) as a set for which a locally optimal solution can be found in polynomial time. Next, they introduce a PLS-complete decision problem as an analogy of NP-complete one, which are the most difficult problems in PLS.Murthy, Pardalos and Li [7] proposed a neighbourhood structure for QAP problem and proved that the corresponding local search problem is PLS-complete. The proposed structure is similar to that proposed by Kernighan and Lin [8] for the graph partitioning problem called K-L type neighbourhood structure N K-L . As the problem of finding QAP optimal solution in N K-L (called (QAP, N K-L )) is PLS-complete, then in the

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“…Due to the computational complexity of the QAPs, exact methods can solve relatively small-sized instances from the QAP benchmark library (QAPLIB) with up to 30 locations. Therefore, to obtain near-optimal solutions, various heuristic and metaheuristic approaches have been developed, such as tabu search [ 5 , 6 , 7 ], simulated annealing [ 8 , 9 ], scatter search or swarm algorithms including ant colony optimization [ 10 ], particle swarm optimization [ 11 , 12 ] and bees algorithm [ 13 , 14 ]. One of the initiatives followed by many researchers is using evolutionary algorithms for solving quadratic assignment problems [ 3 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Quantum-Inspired Evolutionary Approach for the Quadratic Assignment Problem

Chmiel

Kwiecień

2018

Entropy

Self Cite

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The paper focuses on the opportunity of the application of the quantum-inspired evolutionary algorithm for determining minimal costs of the assignment in the quadratic assignment problem. The idea behind the paper is to present how the algorithm has to be adapted to this problem, including crossover and mutation operators and introducing quantum principles in particular procedures. The results have shown that the performance of our approach in terms of converging to the best solutions is satisfactory. Moreover, we have presented the results of the selected parameters of the approach on the quality of the obtained solutions.

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Bees Algorithm for the Quadratic Assignment Problem on CUDA Platform

Cited by 4 publications

References 17 publications

Parallel hybrid chicken swarm optimization for solving the quadratic assignment problem

Parallel hybrid chicken swarm optimization for solving the quadratic assignment problem

A comparison of nature inspired algorithms for the quadratic assignment problem

Quantum-Inspired Evolutionary Approach for the Quadratic Assignment Problem

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