Optimization of the Carpool Service Problem via a Fuzzy-Controlled Genetic Algorithm

Huang, Shih-Chia; Jiau, Ming-Kai; Lin, Chien‐Chu

doi:10.1109/tfuzz.2014.2374194

Cited by 45 publications

(11 citation statements)

References 24 publications

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“…It takes advantage of the cloud as an agency node station, where data generated from moving vehicles are processed and exchanged among themselves. Examples include smartphone-based communication applications, such as instant messaging subscription services, video-capturing VoIP calls, online carpool matching [17]- [19], etc.…”

Section: A Vehicles To Clouds (Vtc) Infrastructurementioning

confidence: 99%

Multimedia Services in Cloud-Based Vehicular Networks

Jiau

Huang

Hwang

et al. 2015

IEEE Intell. Transport. Syst. Mag.

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Research into the requirements for mobile services has seen a growing interest in the fields of cloud technology and vehicular applications. Integrating cloud computing and storage with vehicles is a way to increase accessibility to multimedia services, and inspire myriad potential applications and research topics. This paper presents an overview of the characteristics of cloud computing, IEEE IntEllIgEnt transportatIon systEms magazInE • 63 • fall 2015 and introduces the basic concepts of vehicular networks. An architecture for multimedia cloud computing is proposed to suit subscription service mechanisms. The tendency to equip vehicles with advanced and embedded devices such as diverse sensors increases the capabilities of vehicles to provide computation and collection of multimedia content in the form of the vehicular network. Then, the taxonomy of cloud-based vehicular networks is addressed from the standpoint of the service relationship between the cloud computing and vehicular networks. In this paper, we identify the main considerations and challenges for cloud based vehicular networks regarding multimedia services, and propose potential research directions to make multimedia services achievable. More specifically, we quantitatively evaluate the performance metrics of these researches. For example, in the proposed broadcast storm mitigation scheme for vehicular networks, the packet delivery ratio and the normalized throughput can both achieve about 90%, making the proposed scheme a useful candidate for multimedia data exchange. Moreover, in the video uplinking scenarios, the proposed scheme is favorably compared with two well-known schedulers, M-LWDF and EXP, with the performance much closer to the optimum.

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Section: A Vehicles To Clouds (Vtc) Infrastructurementioning

confidence: 99%

Multimedia Services in Cloud-Based Vehicular Networks

Jiau

Huang

Hwang

et al. 2015

IEEE Intell. Transport. Syst. Mag.

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“…It is similar to the commonly used n-point crossover operator, but the locations of crossover points of two individuals do not have to be the same [17]. So far, this operator has been applied in wireless transmitter placement [18], long term evolution network planning [19], the carpool service problem [20], and pixel classification [21]. The spatial crossover operator is another representative, which works in the phenotypic space.…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution With a Variable Population Size for Deployment Optimization in a UAV-Assisted IoT Data Collection System

Huang

Wang

et al. 2020

IEEE Trans. Emerg. Top. Comput. Intell.

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This paper studies an unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT) data collection system, where a UAV is employed as a data collection platform for a group of ground IoT devices. Our objective is to minimize the energy consumption of this system by optimizing the UAV's deployment, including the number and locations of stop points of the UAV. When using evolutionary algorithms to solve this UAV's deployment problem, each individual usually represents an entire deployment. Since the number of stop points is unknown a priori, the length of each individual in the population should be varied during the optimization process. Under this condition, the UAV's deployment is a variable-length optimization problem and the traditional fixed-length mutation and crossover operators should be modified. In this paper, we propose a differential evolution algorithm with a variable population size, called DEVIPS, for optimizing the UAV's deployment. In DEVIPS, the location of each stop point is encoded into an individual, and thus the whole population represents an entire deployment. Over the course of evolution, differential evolution is employed to produce offspring. Afterward, we design a strategy to adjust the population size according to the performance improvement. By this strategy, the number of stop points can be increased, reduced, or kept unchanged adaptively. In DEVIPS, since each individual has a fixed length, the UAV's deployment becomes a fixed-length optimization problem and the traditional fixed-length mutation and crossover operators can be used directly. The performance of DEVIPS is compared with that of five algorithms on a set of instances. The experimental studies demonstrate its effectiveness.

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“…In 2015, Jiau [11] implemented the carpooling path matching in a short time through genetic algorithm and realized the carpooling path matching scheme of low complexity and low memory. In 2015, Huang [12] propose a fuzzy-controlled genetic-based carpool algorithm by using the combined approach of the genetic algorithm and the fuzzy control system, with which to optimize the route and match assignments of the providers and the requesters in the intelligent carpool system. In 2016, Chou [13] developed a particle swarm carpool algorithm based on stochastic setbased particle swarm optimization (PSO); the set-based PSO (S-PSO) can be realized by local exploration.…”

Section: Introductionmentioning

confidence: 99%

Clustering Algorithm for Urban Taxi Carpooling Vehicle Based on Data Field Energy

Xiao

Shuang-Shuang

2018

Journal of Advanced Transportation

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A clustering algorithm for urban taxi carpooling based on data field energy and point spacing is proposed to solve the clustering problem of taxi carpooling on urban roads. The data field energy function is used to calculate the field energy of each data point in the passenger taxi offpoint dataset. To realize the clustering of taxis, the central point, outlier, and data points of each cluster subset are discriminated according to the threshold value determined by the product of each data point field values and point spacing. The classical algorithm and proposed algorithm are compared and analyzed by using the compactness, separation, and Dunn validity index. The clustering results of the proposed algorithm are better than those of the classical clustering algorithm. In the case of cluster numbers 25, 249, 409, and 599, the algorithm has good clustering results for the taxi trajectory dataset with certain regularity in space distribution and irregular distribution in time distribution. This algorithm is suitable for the clustering of vehicles in urban traffic roads, which can provide new ideas and methods for the cluster study of urban traffic vehicles.

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Optimization of the Carpool Service Problem via a Fuzzy-Controlled Genetic Algorithm

Cited by 45 publications

References 24 publications

Multimedia Services in Cloud-Based Vehicular Networks

Multimedia Services in Cloud-Based Vehicular Networks

Differential Evolution With a Variable Population Size for Deployment Optimization in a UAV-Assisted IoT Data Collection System

Clustering Algorithm for Urban Taxi Carpooling Vehicle Based on Data Field Energy

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