In this paper an ant colony optimization (ACO) algorithm for the minimum
connected dominating set problem (MCDSP) is presented. The MCDSP become
increasingly important in recent years due to its applicability to the mobile
ad hoc networks (MANETs) and sensor grids. We have implemented a one-step ACO
algorithm based on a known simple greedy algorithm that has a significant
drawback of being easily trapped in local optima. We have shown that by
adding a pheromone correction strategy and dedicating special attention to
the initial condition of the ACO algorithm this negative effect can be
avoided. Using this approach it is possible to achieve good results without
using the complex two-step ACO algorithm previously developed. We have tested
our method on standard benchmark data and shown that it is competitive to the
existing algorithms. [Projekat Ministarstva nauke Republike Srbije, br.
III-44006]
Abstract:Charging station location decisions are a critical element in mainstream adoption of electric vehicles (EVs). The consumer confidence in EVs can be boosted with the deployment of carefully-planned charging infrastructure that can fuel a fair number of trips. The charging station (CS) location problem is complex and differs considerably from the classical facility location literature, as the decision parameters are additionally linked to a relatively longer charging period, battery parameters, and available grid resources. In this study, we propose a three-layered system model of fast charging stations (FCSs). In the first layer, we solve the flow capturing location problem to identify the locations of the charging stations. In the second layer, we use a queuing model and introduce a resource allocation framework to optimally provision the limited grid resources. In the third layer, we consider the battery charging dynamics and develop a station policy to maximize the profit by setting maximum charging levels. The model is evaluated on the Arizona state highway system and North Dakota state network with a gravity data model, and on the City of Raleigh, North Carolina, using real traffic data. The results show that the proposed hierarchical model improves the system performance, as well as the quality of service (QoS), provided to the customers. The proposed model can efficiently assist city planners for CS location selection and system design.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.