Comparison of artificial bee colony and flower pollination algorithms in vehicle delay models at signalized intersections

Korkmaz, Ersin; Akgüngör, Ali Payıdar

doi:10.1007/s00521-018-3670-3

Cited by 11 publications

(6 citation statements)

References 38 publications

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“…The Bee Algorithm (BA) and its closely related variant, the Artificial Bee Colony (ABC), have demonstrated successful applications in various optimization problems. They have been particularly effective in domains such as vehicle routing and transportation, as exemplified by [21][22][23][24], as well as in timetabling and scheduling problems, as demonstrated in [25][26][27].…”

Section: Bees Algorithm (Ba)mentioning

confidence: 99%

Composing Multiple Online Exams: The Bees Algorithm Solution

Hosny,

Hayel,

Altwaijry

2023

Applied Sciences

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Online education has gained increasing importance in recent years due to its flexibility and ability to cater to a diverse range of learners. The COVID-19 pandemic has further emphasized the significance of online education as a means to ensure continuous learning during crisis situations. With the disruption of traditional in-person exams, online examinations have become the new norm for universities worldwide. Among the popular formats for online tests are multiple-choice questions, which are drawn from a large question bank. However, creating online tests often involves meeting specific requirements, such as minimizing the overlap between exams, grouping related questions, and determining the desired difficulty level. The manual selection of questions from a sizable question bank while adhering to numerous constraints can be a laborious task. Additionally, traditional search methods that evaluate all possible solutions are impractical and time-consuming for such a complex problem. Consequently, approximate methods like metaheuristics are commonly employed to achieve satisfactory solutions within a reasonable timeframe. This research proposes the application of the Bees Algorithm (BA), a popular metaheuristic algorithm, to address the problem of generating online exams. The proposed solution entails creating multiple exam forms that align with the desired difficulty level specified by the educator, while considering other identified constraints. Through extensive testing and comparison with four rival methods, the BA demonstrates superior performance in achieving the primary objective of matching the desired difficulty level in most test cases, as required by the educator. Furthermore, the algorithm exhibits robustness, indicated by minimal standard deviation across all experiments, which suggests its ability to generalize, adapt, and be practically applicable in real-world scenarios. However, the algorithm does have limitations related to the number of successful solutions and the achieved overlap percentage. These limitations have also been thoroughly discussed and highlighted in this research.

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Section: Bees Algorithm (Ba)mentioning

confidence: 99%

Composing Multiple Online Exams: The Bees Algorithm Solution

Hosny,

Hayel,

Altwaijry

2023

Applied Sciences

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“…The results showed that the proposed method outperforms the reference method and is, therefore, more reliable. Korkmaz and Akgungor ( 9 ) focused on accurate estimation of vehicle delay at signalized intersections. They aimed to estimate the delay simply and accurately and to evaluate the performance of two proposed approaches: the “artificial bee colony” and “flower pollination” algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The estimation models obtained through analytical approaches were able to estimate the delay successfully in undersaturated traffic flows, but they were unsuccessful in oversaturated traffic flows. It has been observed that the delay estimation models obtained by utilizing artificial intelligence approaches achieve successful results in undersaturated and oversaturated traffic flows (2)(3)(4)(5)(6)(7)(8)(9)(10). However, the number of artificial intelligence techniques utilized for vehicle delay estimation is limited in the literature.…”

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confidence: 99%

Vehicle Delay Estimation at Signalized Intersections Using Machine Learning Algorithms

Bağdatlı

Dokuz

2021

Transportation Research Record

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Accurate determination of average vehicle delays is significant for effective management of a signalized intersection. The vehicle delays can be determined by field studies, however, this approach is costly and time consuming. Analytical methods which are commonly utilized to estimate delay cannot generate accurate estimates, especially in oversaturated traffic flow conditions. Delay estimation models based on artificial intelligence have been presented in the literature in recent years to estimate the delay more accurately. However, the number of artificial/heuristic intelligence techniques utilized for vehicle delay estimation is limited in the literature. In this study, estimation models are developed using four different machine learning methods—support vector regression (SVR), random forest (RF), k nearest neighbor (kNN), and extreme gradient boosting (XGBoost)—that have not previously been applied in the literature for vehicle delay estimation at signalized intersections. The models were tested with data collected from 12 signalized intersections located in Ankara, the capital of Turkey, and the performance of the models was revealed. The models were furthermore compared with successful delay models from the literature. The developed models, in particular the RF and XGBoost models, showed high performance in estimating the delay at signalized intersections under different traffic conditions. The results indicate that the delay estimation models based on the RF and XGBoost techniques can significantly contribute to both the literature and practice.

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“…For this reason, studies continue to improve the accuracy and reliability of the analytical models. For this purpose, different delay models using artificial intelligence techniques have been developed and better performance values have been obtained compared to the analytical models in the literature 2–4 …”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

“…For this purpose, different delay models using artificial intelligence techniques have been developed and better performance values have been obtained compared to the analytical models in the literature. [2][3][4] The amount of delay and level of queuing at a signalized intersection varies depending on vehicle arrivals and the service characteristics of the intersection. In the delay models in the literature, these features are expressed with the symbol k, which is a delay parameter.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Bezier Search Differential Evolution algorithm based estimation models of delay parameter k for signalized intersections

Akgüngör

Korkmaz

2022

Concurrency and Computation

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This article presents a new methodology for estimating delay parameter k, and proposes analytical models which are used artificial intelligence technique for signalized intersections that considers the variation in traffic flow with under-saturated and over-saturated conditions. The delay parameter k has been expressed as a function of the degree of saturation in the proposed analytical models. Using the Bezier Search Differential Evolution algorithm (BeSD) algorithm, four different model forms were developed separately for under-saturated conditions (x <1) and over-saturated conditions (x > =1). Among the model forms developed as linear, quadratic, power, and logarithmic, the quadratic model presented the best results in both traffic conditions. In the validation of the models, a total of 140 different traffic conditions were determined, 56 of which cover the under-saturated and 84 the over-saturated traffic conditions. According to the statistical results, using k values depending on the proposed model instead of using a constant k value (0.5) provides 1.5 and 4.3 improvements for RMSE and MAPE values in under-saturated traffic conditions respectively, while these improvements in over-saturated traffic conditions have reached 9.5 and 6, respectively. As a result, using k values depending on the proposed model will be effective in obtaining a more accurate delay value. This effect is more evident, especially in over-saturated traffic conditions.

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Comparison of artificial bee colony and flower pollination algorithms in vehicle delay models at signalized intersections

Cited by 11 publications

References 38 publications

Composing Multiple Online Exams: The Bees Algorithm Solution

Composing Multiple Online Exams: The Bees Algorithm Solution

Vehicle Delay Estimation at Signalized Intersections Using Machine Learning Algorithms

Bezier Search Differential Evolution algorithm based estimation models of delay parameter k for signalized intersections

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