A cooperative GPU-based Parallel Multistart Simulated Annealing algorithm for Quadratic Assignment Problem

Sonuç, Emrullah; Şen, Baha; Bayir, Safak

doi:10.1016/j.jestch.2018.08.002

Cited by 11 publications

(8 citation statements)

References 26 publications

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“…We compare the performance of our systems against eight state-of-the-art solvers, described in Table 1. Solvers [4,5,22] use a preset iteration/time limit as their termination criterion while [15,[18][19][20]24] terminate as soon as the BKS is reached. All metrics are taken directly from the respective papers.…”

Section: Experiments and Resultsmentioning

confidence: 99%

A Permutational Boltzmann Machine with Parallel Tempering for Solving Combinatorial Optimization Problems

Bagherbeik

Ashtari

Mousavi

et al. 2020

Lecture Notes in Computer Science

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Boltzmann Machines are recurrent neural networks that have been used extensively in combinatorial optimization due to their simplicity and ease of parallelization. This paper introduces the Permutational Boltzmann Machine, a neural network capable of solving permutation optimization problems. We implement this network in combination with a Parallel Tempering algorithm with varying degrees of parallelism ranging from a single-thread variant to a multi-threaded system using a 64core CPU with SIMD instructions. We benchmark the performance of this new system on Quadratic Assignment Problems, using some of the most difficult known instances, and show that our parallel system performs in excess of 100× faster than any known dedicated solver, including those implemented on CPU clusters, GPUs, and FPGAs.

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Section: Experiments and Resultsmentioning

confidence: 99%

A Permutational Boltzmann Machine with Parallel Tempering for Solving Combinatorial Optimization Problems

Bagherbeik

Ashtari

Mousavi

et al. 2020

Lecture Notes in Computer Science

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show abstract

“…The best solutions produced in the hives are then enhanced by a long-run TS. Sonuc et al (Sonuc et al, 2018) developed the Parallel Multistart Simulated Annealing (PMSA) approach using the CUDA architecture to solve QAP. The use of the multistart approach and interthread collaboration enhances the quality of the solution.…”

Section: Parallel Algorithms For Qapmentioning

confidence: 99%

A Discrete Hybrid Grey Wolf Optimizer Algorithm to solve Quadratic Assignment Problem

Panwar,

Deep

2023

Preprint

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Grey Wolf Optimizer (GWO) is one of the popular swarm intelligence-based algorithms that derived its inspiration from the hunting behaviour of wolves. GWO has been found quite an effective algorithm for solving various optimization problems. It has demonstrated impressive results, which makes it a strong candidate for finding optimal solutions to NP-hard problems. This study proposes a discrete Hybrid Grey Wolf Optimizer (HGWO) that combines GWO with Tabu Search (TS) to address the well-known combinatorial optimization problem, the Quadratic Assignment Problem (QAP). The development of efficient techniques for the QAP is an active area of research, which motivates our attempts to propose HGWO. As QAP is a combinatorial optimization problem, the continuous values obtained from the classical GWO are converted to discrete values using the largest real value mapping. In HGWO, the position of each individual is first updated by GWO and further improved by TS. The performance of HGWO is evaluated through computational experiments over 100 QAP benchmark instances. The numerical results are compared with other well-known algorithms from the literature. Also, for an unbiased and rigorous comparison, statistical tests such as Friedman non-parametric test and Wilcoxon signed rank test are conducted. The analysis of obtained numerical results demonstrated that HGWO has merit in solving QAP.

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“…Several of these works take advantage of the capabilities of the Graphic Processing Units (GPUs) to use them for general-purpose programming. Concretely (Fabris & Krohling, 2012;Ferreiro et al, 2013;Sonuç et al, 2017Sonuç et al, , 2018Wei et al, 2015) have been devoted to the parallelization of the SA for Nvidia GPUs in CUDA and to solve several optimization problems.…”

Section: 𝑇 𝑗mentioning

confidence: 99%

Coverage area maximization with parallel simulated annealing

Coll

Fort

Saus

2022

Expert Systems with Applications

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A cooperative GPU-based Parallel Multistart Simulated Annealing algorithm for Quadratic Assignment Problem

Cited by 11 publications

References 26 publications

A Permutational Boltzmann Machine with Parallel Tempering for Solving Combinatorial Optimization Problems

A Permutational Boltzmann Machine with Parallel Tempering for Solving Combinatorial Optimization Problems

A Discrete Hybrid Grey Wolf Optimizer Algorithm to solve Quadratic Assignment Problem

Coverage area maximization with parallel simulated annealing

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