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A Multi-Strategy Improved Ant Colony Optimization Algorithm for the Electric Vehicle Routing Problem with Time Windows and En-Route Recharging.

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Title: A Multi-Strategy Improved Ant Colony Optimization Algorithm for the Electric Vehicle Routing Problem with Time Windows and En-Route Recharging.
Authors: Gao, Liping1 (AUTHOR), He, Zhaolei1,2 (AUTHOR), Lin, Cong1 (AUTHOR), Zhao, Jing1,2 (AUTHOR), He, Ao1 (AUTHOR), Jia, Xianguang2 (AUTHOR), Li, Wei2 (AUTHOR) liwei6@kust.edu.cn
Source: Energies (19961073). Sep2026, Vol. 19 Issue 17, p4146. 28p.
Subject Terms: *Ant algorithms, *Vehicle routing problem, *Time measurements, *Optimization algorithms, *Energy storage, *Freight & freightage
Abstract: With the continued electrification and digitalization of urban logistics, electric freight routing increasingly requires the coordinated consideration of customer time windows, vehicle capacity, limited battery range, and en-route charging. This study formulates an electric vehicle routing problem with time windows (EVRPTW) for smart-city electric freight and develops a multi-strategy improved ant colony optimization algorithm (IACO). The proposed model integrates customer service, route continuity, time windows, vehicle capacity, battery-energy propagation, and en-route charging. IACO combines a route–charging-state representation with feasibility-guided sweep-insertion initialization, max–min pheromone control, multi-representative guidance, reachable charging-station insertion, greedy feasibility repair, and 2-opt local search, forming a multi-stage search process that integrates global exploration, feasibility restoration, and local intensification. Computational experiments on an R-C benchmark scenario with 51 customers and 9 charging stations compare IACO with ACO, GA, TS, LNS, SA, PSO, and WOA over 100 independent runs under a common 300-iteration limit. Under the current experimental protocol, IACO records a representative generalized cost of 570.88, with reductions of 7.75–44.65% relative to the seven comparison methods, while its median CPU time is 28.42 s. These results demonstrate a clear solution-quality–computation trade-off and indicate the potential of IACO for plan-level electric freight routing and en-route charging coordination. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 197040627
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Label: Title
  Group: Ti
  Data: A Multi-Strategy Improved Ant Colony Optimization Algorithm for the Electric Vehicle Routing Problem with Time Windows and En-Route Recharging.
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  Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Sep2026, Vol. 19 Issue 17, p4146. 28p.
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  Data: *<searchLink fieldCode="DE" term="%22Ant+algorithms%22">Ant algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Vehicle+routing+problem%22">Vehicle routing problem</searchLink><br />*<searchLink fieldCode="DE" term="%22Time+measurements%22">Time measurements</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+storage%22">Energy storage</searchLink><br />*<searchLink fieldCode="DE" term="%22Freight+%26+freightage%22">Freight & freightage</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: With the continued electrification and digitalization of urban logistics, electric freight routing increasingly requires the coordinated consideration of customer time windows, vehicle capacity, limited battery range, and en-route charging. This study formulates an electric vehicle routing problem with time windows (EVRPTW) for smart-city electric freight and develops a multi-strategy improved ant colony optimization algorithm (IACO). The proposed model integrates customer service, route continuity, time windows, vehicle capacity, battery-energy propagation, and en-route charging. IACO combines a route–charging-state representation with feasibility-guided sweep-insertion initialization, max–min pheromone control, multi-representative guidance, reachable charging-station insertion, greedy feasibility repair, and 2-opt local search, forming a multi-stage search process that integrates global exploration, feasibility restoration, and local intensification. Computational experiments on an R-C benchmark scenario with 51 customers and 9 charging stations compare IACO with ACO, GA, TS, LNS, SA, PSO, and WOA over 100 independent runs under a common 300-iteration limit. Under the current experimental protocol, IACO records a representative generalized cost of 570.88, with reductions of 7.75–44.65% relative to the seven comparison methods, while its median CPU time is 28.42 s. These results demonstrate a clear solution-quality–computation trade-off and indicate the potential of IACO for plan-level electric freight routing and en-route charging coordination. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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        Value: 10.3390/en19174146
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        Text: English
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        StartPage: 4146
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      – SubjectFull: Ant algorithms
        Type: general
      – SubjectFull: Vehicle routing problem
        Type: general
      – SubjectFull: Time measurements
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      – SubjectFull: Optimization algorithms
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      – SubjectFull: Energy storage
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      – SubjectFull: Freight & freightage
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      – TitleFull: A Multi-Strategy Improved Ant Colony Optimization Algorithm for the Electric Vehicle Routing Problem with Time Windows and En-Route Recharging.
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            NameFull: Gao, Liping
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            – D: 01
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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