Improved Hunting Search Algorithm for the Quadratic Assignment Problem

Agharghor, Amine; Riffi, Mohammed Essaid; Chebihi, Fayçal

doi:10.11591/ijeecs.v14.i1.pp143-154

Cited by 8 publications

(4 citation statements)

References 12 publications

(12 reference statements)

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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%

“…In order to make an update to the system, we must conduct a trial. First, a neuron x i is randomly chosen and the change in energy as a result of flipping its state is calculated via (4). Next, the probability of flipping the neuron's state, P move , is calculated via (5) and is compared against a uniformly distributed random number in [0,1] to determine the change in the neuron's state using (6).…”

Section: Boltzmann Machinesmentioning

confidence: 99%

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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%

Section: Boltzmann Machinesmentioning

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

“…In this paper, we proposed to search the optimal matching using the ACO, which is a meta-heuristic approach that has been used to solve many combinatorial optimization problems such as: Vehicle Routing [20,21], Traveling Salesman [22,23], Network Model [24], and Quadratic Assignment (QAP) [16,25,26]. We applied the correspondence points problem theory which integrates proximity information [27][28][29] and explains how the ACO algorithm is used to determine the landmarks matching.…”

Section: Aco Based Landmark Matchingmentioning

confidence: 99%

Robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification

Ismail¹,

Elmannai²,

Meshoul³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Imagery assessment is an efficient method for detecting craniofacial anomalies. A cephalometric landmark matching approach may help in orthodontic diagnosis, craniofacial growth assessment and treatment planning. Automatic landmark matching and anomalies detection helps face the manual labelling limitations and optimize preoperative planning of maxillofacial surgery. The aim of this study was to develop an accurate Cephalometric Landmark Matching method as well as an automatic system for anatomical anomalies classification. First, the Active Appearance Model (AAM) was used for the matching process. This process was achieved by the Ant Colony Optimization (ACO) algorithm enriched with proximity information. Then, the maxillofacial anomalies were classified using the Support Vector Machine (SVM). The experiments were conducted on X-ray cephalograms of 400 patients where the ground truth was produced by two experts. The frameworks achieved a landmark matching error (LE) of 0.50 ± 1.04 and a successful landmark matching of 89.47% in the 2 mm and 3 mm range and of 100% in the 4 mm range. The classification of anomalies achieved an accuracy of 98.75%. Compared to previous work, the proposed approach is simpler and has a comparable range of acceptable matching cost and anomaly classification. Results have also shown that it outperformed the K-nearest neighbors (KNN) classifier.

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“…On the one hand, quadratic assignment problems are widely used in practice, and many real-world problems can be formalized as quadratic assignment problems, such as integrated circuit wiring [2][3], factory location layout [4], typewriter keyboard design, task scheduling [5][6], etc. On the other hand, some classical NP-hard combinatorial optimization problems, such as the traveling salesman problem, the triangulation problem, and the Max Clique problem, can also be transformed into quadratic assignment problems [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

A Novel Discrete Rat Swarm Optimization Algorithm for the Quadratic Assignment Problem

Mzili,

Riffi

et al. 2023

FU Mech Eng

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The quadratic assignment problem (QAP) is an NP-hard problem with a wide range of applications in many real-world applications. This study introduces a discrete rat swarm optimizer (DRSO)algorithm for the first time as a solution to the QAP and demonstrates its effectiveness in terms of solution quality and computational efficiency. To address the combinatorial nature of the QAP, a mapping strategy is introduced to convert real values into discrete values, and mathematical operators are redefined to make then suitable for combinatorial problems. Additionally, a solution quality improvement strategy based on local search heuristics such as 2-opt and 3-opt is proposed. Simulations with test instances from the QAPLIB test library validate the effectiveness of the DRSO algorithm, and statistical analysis using the Wilcoxon parametric test confirms its performance. Comparative analysis with other algorithms demonstrates the superior performance of DRSO in terms of solution quality, convergence speed, and deviation from the best-known values, making it a promising approach for solving the QAP.

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Improved Hunting Search Algorithm for the Quadratic Assignment Problem

Cited by 8 publications

References 12 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

Robust ACO-Based Landmark Matching and Maxillofacial Anomalies Classification

A Novel Discrete Rat Swarm Optimization Algorithm for the Quadratic Assignment Problem

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