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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Bibliographic Details
Published in:	Engineering applications of artificial intelligence Vol. 162; p. 112586
Main Authors:	Yang, Zhe, Deng, Libao, Di, Yuanzhu, Li, Chunlei, Qin, Yifan, Zhang, Lili
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 26.12.2025
Subjects:	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
Online Access:	Get full text
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Summary:	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