Data · dataset · 2026
Research on radioactive waste disposal optimization algorithm based on biased random-key genetic algorithm
Listed in ScienceDB
Abstract [Background] The resources for radioactive waste disposal are extremely valuable, and their efficient utilization represents a core operational challenge for current disposal facilities.
Description
[Purpose] This study aims to address this challenge by introducing a Biased Random Key Genetic Algorithm (BRKGA) to develop an intelligent optimization algorithm specifically designed for the layout planning of radioactive waste packages.
[Methods] The proposed algorithm incorporates four key innovative strategies. First, a three-segment gene encoding scheme was designed to represent the placement sequence, orientation, and radionuclide activity information of the waste packages. Second, a multi-objective fitness function was constructed that balances spatial utilization efficiency with radiation protection optimization; this was coupled with the introduction of a "Decreasing Sequence Priority Principle" to enhance the spatial allocation strategy.
Read the rest (2 more)
Third, algorithm acceleration was achieved through parallel computing implementation. [Results] Testing with an engineering case study demonstrated that the proposed algorithm significantly improves spatial utilization by over 6 % compared to manual design approaches, achieving a maximum utilization rate of 92.45 %. The algorithm exhibits excellent convergence characteristics, consistently obtaining high-quality solutions within an average of 342 generations.
Furthermore, in a 32-core parallel computing environment, a speedup ratio of 10.34× was attained, reducing the computation time from several hours to just a few minutes. [Conclusions] The intelligent layout algorithm and the corresponding software system (Radioactive Waste Package Stacking system, RWPS) hold a significant value for practical engineering applications.
Links
Where it is published
- DOI doi.org/10.57760/sciencedb.hjs.00618 ↗
DOI / persistent id · from scidb cn
Catalogue records · 1
- OAI-PMH record scidb.cn/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=10.57760%2… ↗
metadata API · from scidb cn
Topics
- From keywords
- Earth & Environmental Science · Engineering · Humanities · Life Sciences · Social Science
Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ScienceDB | 10.57760/sciencedb.hjs.00618 | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:engineering | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:humanities | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:life-sciences | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| concepts[field].local:field:social-science | mapping · scidb cn | connector:scidb_cn@1.0.0 | |
| description | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/description |
| license | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/rights |
| publication_date | source · scidb cn | connector:scidb_cn@1.0.0 | |
| title | source · scidb cn | connector:scidb_cn@1.0.0 | /metadata/dc/title |