An evolutionary algorithm driving by dimensionality reduction operator and knowledge model for the electric vehicle routing problem with flexible charging strategy

IF 8.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Bihao Yang, Teng Ren, Huijuan Yu, Jie Chen, Yaya Wang
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引用次数: 0

Abstract

The digital economy and digital technology are promoting the integrated development of industry and digital, forming a new path for industrial upgrading and building a new development pattern.In today's context of digital economy and green transformation, it is a challenging optimization problem to scientifically plan the logistics routes of electric vehicles (EVs) when taking charging strategies into consideration. Aiming at the drawback of supposing a fixed charging rate in the traditional EV routing problems (EVRPs), the charging data of a type of mainstream EVs were collected and the instantaneous charging power was simulated in the real scenario. To solve problems of the fixed charge timing and charged energy in traditional EVRP models and partial charging strategies, a new EVRP model considering the flexible charging strategy (EVRP-FCS) by taking the charged energy as one of the decision variables. To effectively solve the model and fully search in the solution space, an improved evolutionary algorithm was proposed. The performance advantages of the algorithm are determined by comparison of 22 groups of large-scale experimental examples. The experimental results have demonstrated the performance advantages of the algorithm.
在数字经济和绿色转型的今天,如何在考虑充电策略的前提下科学规划电动汽车(EV)的物流路线是一个极具挑战性的优化问题。针对传统电动汽车路由问题(EVRPs)中假设固定充电率的弊端,收集了一种主流电动汽车的充电数据,并模拟了真实场景中的瞬时充电功率。为了解决传统 EVRP 模型中的固定充电时间和充电能量问题以及部分充电策略问题,将充电能量作为决策变量之一,建立了考虑灵活充电策略的新型 EVRP 模型(EVRP-FCS)。为了有效求解该模型并充分搜索解空间,提出了一种改进的进化算法。通过对 22 组大规模实验实例的比较,确定了该算法的性能优势。实验结果证明了该算法的性能优势。
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来源期刊
Swarm and Evolutionary Computation
Swarm and Evolutionary Computation COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
16.00
自引率
12.00%
发文量
169
期刊介绍: Swarm and Evolutionary Computation is a pioneering peer-reviewed journal focused on the latest research and advancements in nature-inspired intelligent computation using swarm and evolutionary algorithms. It covers theoretical, experimental, and practical aspects of these paradigms and their hybrids, promoting interdisciplinary research. The journal prioritizes the publication of high-quality, original articles that push the boundaries of evolutionary computation and swarm intelligence. Additionally, it welcomes survey papers on current topics and novel applications. Topics of interest include but are not limited to: Genetic Algorithms, and Genetic Programming, Evolution Strategies, and Evolutionary Programming, Differential Evolution, Artificial Immune Systems, Particle Swarms, Ant Colony, Bacterial Foraging, Artificial Bees, Fireflies Algorithm, Harmony Search, Artificial Life, Digital Organisms, Estimation of Distribution Algorithms, Stochastic Diffusion Search, Quantum Computing, Nano Computing, Membrane Computing, Human-centric Computing, Hybridization of Algorithms, Memetic Computing, Autonomic Computing, Self-organizing systems, Combinatorial, Discrete, Binary, Constrained, Multi-objective, Multi-modal, Dynamic, and Large-scale Optimization.
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