Distribution Planning of UAV Automatic Charging Station Based on Genetic Algorithm

Hu, Ying; Gao, Jixiang; Chen, Xiao; Meng, F.; Wang, Yu Heng

doi:10.1109/icemme49371.2019.00095

Cited by 6 publications

(1 citation statement)

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“…The proposed method has been tested using practical operational data for Tokyo. Another problem that has been faced with EAs is the deployment of CS for UAV: Hu et al 23 faced this problem with GAs and Mirzaeinia et al 24 with Particle Swarm Optimization.…”

Section: Introductionmentioning

confidence: 99%

Optimization of electric vehicles charging station deployment by means of evolutionary algorithms

Niccolai

Bettini

Zich

2021

Int J of Intelligent Sys

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Due to the growing importance of electric vehicles, charging stations (CS) deployment is becoming an important issue in many cities. The aim of this paper is to introduce a novel evolutionary‐based approach for solving the CS deployment problem. This study investigates many aspects of the formulation of this approach, such as the design variables selection and the definition of a feasibility function, to improve both effectiveness and flexibility. In particular, the latter is a key factor compared to many other state‐of‐the‐art approaches: in fact, it can be used with most of the available Evolutionary Algorithms (EAs) and can manage different quality‐of‐service performance parameters. The proposed approach is successfully compared with a greedy optimization on the case study of the City of Milan (Italy) using four different EAs. Two different performance parameters have been defined and used to prove the flexibility of the proposed approach. The results show its very good convergence rate and the quality of the obtained solutions.

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Section: Introductionmentioning

confidence: 99%