Publication Type

Journal Article

Version

acceptedVersion

Publication Date

3-2026

Abstract

The sorted collection of municipal solid waste has emerged as an effective waste management strategy due to varying timeliness requirements across different waste types, giving rise to the critical research challenge of timeliness-based waste collection. While existing algorithms primarily focus on small-scale versions of this problem, solving large-scale timeliness-based waste collection problems remains particularly challenging. To tackle this issue, this paper proposes a knowledge transfer-based membrane evolutionary algorithm. Specifically, the original problem and simplified problem are constructed in different membranes respectively, and the knowledge transfer learning mechanism is incorporated into the membrane evolutionary algorithm, enabling effective information exchange between the original problem membrane and the simplified problem membrane to efficiently obtain high-quality solutions. Extensive experiments on large-scale benchmark instances demonstrate the superiority and efficiency of the proposed algorithm compared with other state-ofthe-art approaches. Furthermore, ablation experiments further confirm the effectiveness of both the membrane operation rules and the knowledge transfer mechanism.

Keywords

Sorted waste collection, Membrane evolutionary algorithm, Collection timeliness, Knowledge transfer, Large-scale problem

Discipline

Environmental Sciences | Operations Research, Systems Engineering and Industrial Engineering | Theory and Algorithms

Research Areas

Intelligent Systems and Optimization

Publication

Swarm and Evolutionary Computation

Volume

102

First Page

1

Last Page

14

ISSN

2210-6502

Identifier

10.1016/j.swevo.2026.102345

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.swevo.2026.102345

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