Applying artificial bee colony algorithm to the multidepot vehicle routing problem

Gu, Zhaoquan; Zhu, Yan; Wang, Yuexuan; Du, Xiaojiang; Guizani, Mohsen; Tian, Zhihong

doi:10.1002/spe.2838

Cited by 22 publications

(12 citation statements)

References 42 publications

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“…The works of [102][103][104] focus on reducing the cost incurred in the routing for vehicles in logistics as a single objective formulation. On the other hand, the authors of [105][106][107] all work on the minimization of distance as their objective in determining the optimal route for delivery vehicles trying to serve multiple locations. Mounia and Bachir [106] address routing in logistics as a multi-objective problem where they not only aim to minimize the distance traveled by the vehicles but also aim to reduce CO 2 emissions and the number of vehicles used.…”

Section: Smart Industrymentioning

confidence: 99%

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Syed

Sierra-Sosa

Kumar

et al. 2022

Sensors

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One of the prime aims of smart cities has been to optimally manage the available resources and systems that are used in the city. With an increase in urban population that is set to grow even faster in the future, smart city development has been the main goal for governments worldwide. In this regard, while the useage of Artificial Intelligence (AI) techniques covering the areas of Machine and Deep Learning have garnered much attention for Smart Cities, less attention has focused towards the use of combinatorial optimization schemes. To help with this, the current review presents a coverage of optimization methods and applications from a smart city perspective enabled by the Internet of Things (IoT). A mapping is provided for the most encountered applications of computational optimization within IoT smart cities for five popular optimization methods, ant colony optimization, genetic algorithm, particle swarm optimization, artificial bee colony optimization and differential evolution. For each application identified, the algorithms used, objectives considered, the nature of the formulation and constraints taken in to account have been specified and discussed. Lastly, the data setup used by each covered work is also mentioned and directions for future work have been identified. This review will help researchers by providing them a consolidated starting point for research in the domain of smart city application optimization.

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Section: Smart Industrymentioning

confidence: 99%

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Syed

Sierra-Sosa

Kumar

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Equation ( 5) represents the constraint condition, the objective function must meet the requirement that any DCP is traversed and only traversed once, any possible traversal sequence must include all DCPs. As a TSP problem, which needs to be addressed by heuristic algorithms, such as particle swarm optimization algorithm [38], ant colony optimization [39], artificial bee colony algorithm [40], fruit fly optimization algorithm [41], tabu search algorithm [42], and simulated annealing algorithm [43], the genetic algorithm (GA) has shown good performance in solving problems such as task assignment and route optimization. The advantages of the GA in real applications have also been demonstrated [21,22].…”

Section: Path Optimization Modelmentioning

confidence: 99%

Optimization Schemes for UAV Data Collection with LoRa 2.4 GHz Technology in Remote Areas without Infrastructure

Zhang

Zhou

Sheng

et al. 2022

Drones

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Recently, the use of unmanned aerial vehicles (UAVs) and LPWANs (low-power wide-area networks) has been a good solution to the problem of data collection for environmental monitoring in remote areas without infrastructure, and there are many valuable research works in this field. UAV data collection for sensor nodes is becoming a challenge, that is, the amount of data will affect the UAV’s communication time and flight status, especially in LPWAN systems. In this paper, the optimization schemes are proposed to improve the efficiency of UAV for collecting data in LoRa network monitoring systems. Firstly, an improved clustering algorithm for the LoRa network is proposed, which considers the influence of distance between the cluster heads and the UAV take-off point. Secondly, we present an improved Genetic Algorithm for path planning to reduce the UAV flight distance, which introduces the Teaching–Learning-based Optimization (TLBO) and local search optimization algorithms to improve convergence speed and the path solution. Then, a LoRa 2.4 GHz adaptive data rate strategy with a dual channel is designed based on distance and link quality, to reduce the data transmitting time between the UAV and the cluster head nodes. Finally, we carry out the simulations and experiments. The results show the performance of the proposed schemes, which means that these can improve the efficiency of UAV data collection with low cost LoRa networks in remote areas without infrastructure.

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“…Artificial bee colony (ABC) is a swarm intelligence-based evolutionary algorithm that is inspired by exploring the food source behavior of honey bees. Similar to many other metaheuristic algorithms such as differential evolution (DE) algorithm, ant colony (ACO) algorithm, particle swarm optimization (PSO), and gravitational emulation that are adapted to solve many different problems such as routing [15,16], image segmentation [17][18][19][20][21][22], and many other clustering problems [23][24][25], ABC is also used in solving many different problems [26,27]. In the study by Hafez et al…”

Section: Related Workmentioning

confidence: 99%

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

Peng

Rastgari

Navaei

et al. 2021

Mathematical Problems in Engineering

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Social network aims to extend a widespread framework to communicate users and find alike people with common features, easier and faster. As people usually experience in everyday life, social communication can be formed from common groups with almost identical properties. Detecting such groups or communities is a challenging task in various fields of social network analysis. Many researchers intend to develop algorithms that work effectively and efficiently on social networks. It is believed that the most influential user in a community that had been followed by similar users could be a central point of a community or cluster, and the similar user would be members of the community. Research studies tend to increase intracommunity similarity and decrease intercommunity similarity to improve the performance of the community detection methods by finding such influential users accurately. In this paper, a hybrid metaheuristic method is proposed. In the proposed method called trust-based community detection using artificial bee colony by feature fusion (TCDABCF), we use a fusion approach combined with artificial bee colony (ABC) to improve the accuracy of the community detection task. In this approach, not only the social features of users are considered but also the relationship of trust between users in a community is also calculated. So, the proposed method can lead to finding more precise clusters of similar users with influential users in the center of each cluster. The proposed method uses the artificial bee colony (ABC) to find the influential users and the relation of their followers accurately. We compare this algorithm with nine state-of-the-art methods on the Facebook dataset. Experimental results show that the proposed method has obtained values of 0.9662 and 0.9533 for NMI and accuracy, respectively, which has improved in comparison with state-of-the-art community detection methods.

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Applying artificial bee colony algorithm to the multidepot vehicle routing problem

Cited by 22 publications

References 42 publications

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Making Cities Smarter—Optimization Problems for the IoT Enabled Smart City Development: A Mapping of Applications, Objectives, Constraints

Optimization Schemes for UAV Data Collection with LoRa 2.4 GHz Technology in Remote Areas without Infrastructure

TCDABCF: A Trust-Based Community Detection Using Artificial Bee Colony by Feature Fusion

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