An iterated greedy algorithm for the obnoxious p-median problem

Gökalp, Osman

doi:10.1016/j.engappai.2020.103674

Cited by 21 publications

(23 citation statements)

References 17 publications

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“…In this section, the results of various hybrid deep architectures for intrusion detection in IoT systems are evaluated. The performance of the proposed NSBPSO algorithm is also evaluated in comparison with some widely-used and competitive metaheuristic algorithms, including the particle swarm optimization (PSO) algorithm, the artificial bee colony (ABC) algorithm, the iterated greedy algorithm (IG) [66], the improved crow search algorithm (I-CSA) [67], and the black widow optimization (BWO) algorithm [68]. All algorithms have been coded in MATLAB, and the calibration parameters of the algorithms have been shown in Table 2.…”

Section: Simulation Results On the Nid Datasetsmentioning

confidence: 99%

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems

Baniasadi

Rostami

Martín

et al. 2022

Sensors

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The Internet of Things (IoT) has become one of the most important concepts in various aspects of our modern life in recent years. However, the most critical challenge for the world-wide use of the IoT is to address its security issues. One of the most important tasks to address the security challenges in the IoT is to detect intrusion in the network. Although the machine/deep learning-based solutions have been repeatedly used to detect network intrusion through recent years, there is still considerable potential to improve the accuracy and performance of the classifier (intrusion detector). In this paper, we develop a novel training algorithm to better tune the parameters of the used deep architecture. To specifically do so, we first introduce a novel neighborhood search-based particle swarm optimization (NSBPSO) algorithm to improve the exploitation/exploration of the PSO algorithm. Next, we use the advantage of NSBPSO to optimally train the deep architecture as our network intrusion detector in order to obtain better accuracy and performance. For evaluating the performance of the proposed classifier, we use two network intrusion detection datasets named UNSW-NB15 and Bot-IoT to rate the accuracy and performance of the proposed classifier.

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Section: Simulation Results On the Nid Datasetsmentioning

confidence: 99%

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems

Baniasadi

Rostami

Martín

et al. 2022

Sensors

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“…The cutting-stock problem is an NP problem. It has been shown [31] that no algorithm guarantees the solution of problems of different scales [32]. Approximation algorithms are more suitable for determining acceptable solutions within a reasonable time.…”

Section: Algorithm Improvementmentioning

confidence: 99%

An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

Ma¹,

Zhao²,

Cang³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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To meet the requirements of specifications, intelligent optimization of steel bar blanking can improve resource utilization and promote the intelligent development of sustainable construction. As one of the most important building materials in construction engineering, reinforcing bars (rebar) account for more than 30% of the cost in civil engineering. A significant amount of cutting waste is generated during the construction phase. Excessive cutting waste increases construction costs and generates a considerable amount of CO 2 emission. This study aimed to develop an optimization algorithm for steel bar blanking that can be used in the intelligent optimization of steel bar engineering to realize sustainable construction. In the proposed algorithm, the integer linear programming algorithm was applied to solve the problem. It was combined with the statistical method, a greedy strategy was introduced, and a method for determining the dynamic critical threshold was developed to ensure the accuracy of large-scale data calculation. The proposed algorithm was verified through a case study; the results confirmed that the rebar loss rate of the proposed method was reduced by 9.124% compared with that of traditional distributed processing of steel bars, reducing CO 2 emissions and saving construction costs. As the scale of a project increases, the calculation quality of the optimization algorithm for steel bar blanking proposed also increases, while maintaining high calculation efficiency. When the results of this study are applied in practice, they can be used as a sustainable foundation for building informatization and intelligent development.

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“…Він базується на припущеннях з теорії ігор, де стратегіями гравця є різні варіанти розстановки постів мережі моніторингу, які характеризуються як конфігурацією, так і кількістю постів. У грі приймає участь «природа», яка визначає синоптичні і метеорологічні параметри навколишнього середовища [9].…”

Section: огляд останніх дослідженьunclassified

“…Критерії оптимальності ММСАП можуть бути найрізноманітнішими, проте найчастіше використовуються: статистичні (вимога мінімуму середньої квадратичної помилки відновлення значення концентрації ЗР у певній точці простору і в певний момент часу); економічні (мінімальна сума збитків від неточності отриманої інформації, мінімальна вартість мережі тощо); комплексні (інформаційна корисність, інформативність тощо) [9].…”

Section: мunclassified

“…Функція мети, що описує відповідний критерій оптимальності (або їх набір), та обмеження в задачі можуть бути лінійними чи нелінійними, опуклими чи неопуклими тощо. Таким чином, задачі оптимізації ММСАП поділяються на лінійні та нелінійні тощо [9].…”

Section: мunclassified

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Optimizational Task Solution of Stationary Points Placement for Observation of Atmospheric Pollution at Technogenically Loaded Territories of Ukraine

Popov¹,

Artemchuk²,

Іatsyshyn³

et al. 2020

Geohìm. tehnog.

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territories is an urgent scientific problem. Comparative analysis of different approaches to determining spatial configuration of the air monitoring network was identified. Their main shortcomings are identified. It makes almost impossible to use them in today's Ukraine. Mathematical formalization of optimization problem solution of stationary points placement for observation of atmospheric pollution at technogenically loaded territories is carried out. From the point of view of optimization theory, the obtained problem is dynamic, nonlinear, deterministic and discrete on a nonconvex domain. Due to considerable complexity of the problem, its solution (finding the optimal solution) is possible only by the method of complete search. However, application of this method is complicated due to the very large number of computational operations for large zones and agglomerations. So, there is a need to use new optimization algorithms. Two algorithms for optimization problem solving were developed. They are based on combination of greedy algorithm and complete search method. Testing of these algorithms (on the example of data from Kyiv) showed that they allow to obtain problem solution (close to optimal) much faster than the method of complete search.

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An iterated greedy algorithm for the obnoxious p-median problem

Cited by 21 publications

References 17 publications

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems

A Novel Deep Supervised Learning-Based Approach for Intrusion Detection in IoT Systems

An Intelligent Optimization Method of Reinforcing Bar Cutting for Construction Site

Optimizational Task Solution of Stationary Points Placement for Observation of Atmospheric Pollution at Technogenically Loaded Territories of Ukraine

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