A Dual-Population Constrained Multi-Objective Evolutionary Algorithm with Success Incentive Mechanism and its application to uncertain multimodal transportation problems

The evolution of the transportation industry has heightened the focus on environmentally sustainable multimodal transport, particularly in addressing carbon emissions. In modern logistics, path planning under uncertainty has become a pivotal research area. This paper proposes a multi-objective, mult...

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Vydané v:Engineering applications of artificial intelligence Ročník 162; s. 112586
Hlavní autori: Yang, Zhe, Deng, Libao, Di, Yuanzhu, Li, Chunlei, Qin, Yifan, Zhang, Lili
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 26.12.2025
Predmet:
Constrained Multi-Objective Evolutionary Algorithms
Constrained Multi-Objective Optimization Problems
Constraint handling techniques
Multimodal transportation
Multimodal transportation
Constrained Multi-Objective Evolutionary Algorithms
Constrained Multi-Objective Optimization Problems
Constraint handling techniques
ISSN:0952-1976
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Shrnutí:The evolution of the transportation industry has heightened the focus on environmentally sustainable multimodal transport, particularly in addressing carbon emissions. In modern logistics, path planning under uncertainty has become a pivotal research area. This paper proposes a multi-objective, multi-constraint optimization model for multimodal transport that aims to concurrently minimize cost, carbon emissions, and time. The model accounts for numerous operational constraints, including timetables, as well as dual sources of uncertainty from demand and the transport environment. To solve this complex problem, this paper introduces a new algorithmic framework. The proposed algorithm, a Dual-Population Constrained Multi-Objective Evolutionary Algorithm with a Success Incentive Mechanism (DSCMOEA), integrates three key innovations: a universal priority-based encoding/decoding adapter, a specialized constraint-handling architecture, and an adaptive operator selection mechanism. The adapter is central to the framework, enabling continuous-domain evolutionary algorithms to solve the discrete transport problem without internal modification. This approach also provides the versatility to handle various uncertainty paradigms through a multi-scenario simulation context. Experimental analysis validates the superiority of the proposed algorithm against eight established competitors, demonstrating its effectiveness in solving complex multimodal transport problems under uncertainty.
ISSN:0952-1976
DOI:10.1016/j.engappai.2025.112586