Distance related: a procedure for applying directly Artificial Bee Colony algorithm in routing problems

Trachanatzi, Dimitra; Rigakis, Manousos; Marinaki, Magdalene; Marinakis, Yannis; Matsatsinis, Nikolaos F.

doi:10.1007/s00500-019-04438-w

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…After several times of selection, if the location of some honey source is still not improved, the reconnaissance bee operation will be carried out, the honey source will be abandoned, a group of substitutes will be found, and the evaluation will be carried out as before. e whole process is executed repeatedly, and the optimal solution of each step is recorded until it stops [17].…”

Section: Improved Artificial Bee Colony Algorithmmentioning

confidence: 99%

Electricity Network Security Monitoring Based on Bee Colony Algorithm

Su¹,

Zhang²

2022

International Transactions on Electrical Energy Systems

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In order to effectively detect and discover network threats in the initial stage, this study proposes an electricity network security intrusion detection method based on feature selection. A heuristic feature selection algorithm based on the bee colony algorithm is proposed to overcome the shortcomings of existing feature evaluation methods. The algorithm uses average mutual information to measure the importance of features and more truly reflects the relationship between the selected features, the selected features, and the classification labels. Aiming at the problem that the algorithm is easy to fall into local optimization, a heuristic random search algorithm is proposed, which iteratively optimizes to generate smaller feature subsets, and improves the speed and accuracy of intrusion detection. The experimental results show that compared with the traditional algorithm, the proposed method can effectively evaluate the risk of attack path on the selected experimental data set, and the gap between the generation strategy and the optimal strategy is reduced by 71.3%, which enhances the practicability of the attack graph analysis method in a large-scale network environment. Conclusion. This method has good scalability and can be applied to large-scale network environments. It can effectively obtain attack paths that are more in line with the real threat situation in an acceptable time, so as to effectively find the network threats.

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Section: Improved Artificial Bee Colony Algorithmmentioning

confidence: 99%

Electricity Network Security Monitoring Based on Bee Colony Algorithm

Su¹,

Zhang²

2022

International Transactions on Electrical Energy Systems

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“…For the reverse logistics location and routing problem, Guo and Zhang [67] proposed a discrete ABC with greedy adjustment strategy. Lei et al [68] designed an effective evaluation mechanism to dynamically determine employed bee and onlooker swarm, and then variable neighborhood descent was introduced in employed bee and onlooker phases. To solve the team orienteering problem, Trachanatzi et al [69] presented a distance related ABC based on a novel encoding/decoding method.…”

Section: Related Workmentioning

confidence: 99%

Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

Guo

Sun

et al. 2023

Complex Syst. Model. Simul.

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Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines, and the rationality of their routes plays the direct impact on operation safety and energy consumption. Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil, however, less works are for electric trackless rubber-tyred vehicles. Furthermore, energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving, especially the limited cruising ability of electric trackless rubber-tyred vehichles (TRVs). To address this issue, an energy consumption model of an electric trackless rubber-tyred vehicle is formulated, in which the effects from total mass, speed profiles, slope of roadways, and energy management mode are all considered. Following that, a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance, allowable load, and endurance power. As a problem-solver, an improved artificial bee colony algorithm is put forward. More especially, an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space. In order to assign onlookers to some promising food sources reasonably, their selection probability is adaptively adjusted. For a stagnant food source, a knowledge-driven initialization is developed to generate a feasible substitute. The experimental results on four real-world instances indicate that improved artificial bee colony algorithm (IABC) outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.

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“…Tabu search was applied by Arockia et al, 2021;Kurniawati et al, 2022;Caballero-Morales et al, 2018;Dam et al, 2017;Obaid, 2018. Furthermore, some scholars use artificial bee colony for solving CVRP (Ding et al, 2018;Karaoglan et al, 2020;Wahyuningsih et al, 2020;Trachanatzi et al 2020;Katiyar et al;.…”

Section: Introductionmentioning

confidence: 99%

Solving Capacitated Vehicle Routing Problem Using Football Game Algorithm

Alif¹,

Garside

Amallynda³

et al. 2022

JITI

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The Capacitated Vehicle Routing Problem (CVRP) plays an important role in the logistics transportation sector. Determining the proper route will reduce the company's operational costs. In CVRP, a number of vehicles have a capacity limit that can serve all customers. This research completes a real case study on a bottled drinking water company where the company still uses the subjective method of the driver to determine the transportation route. Based on the conditions in the company, the selection of the best route will consider vehicle capacity and demand to determine the shortest route. The execution of this case study uses the Football Game Algorithm (FGA) which was first initiated by Fadakar Ebrahimi which proved promising and had the strongest performance in all cases. FGA is expected to be able to determine the shortest distribution route from the existing cases to reduce the distribution costs incurred. This study takes data from 4 days of delivery that served 78 customers. The average daily transportation cost savings result is 42%. This amount indicates that the FGA algorithm is effective for completing a real case study in CVRP.

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Distance related: a procedure for applying directly Artificial Bee Colony algorithm in routing problems

Cited by 4 publications

References 37 publications

Electricity Network Security Monitoring Based on Bee Colony Algorithm

Electricity Network Security Monitoring Based on Bee Colony Algorithm

Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

Solving Capacitated Vehicle Routing Problem Using Football Game Algorithm

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