BCI 2026: Performance Metrics for a Bi-Objective Maximal Covering Location Problem

An annex to a 2026 research paper on multi-objective evolutionary algorithms, this dataset contains a full list of performance metrics for solving a variant of the bi-objective maximal covering location problem. The 169.6 KB PDF file, authored by Arkadii Semenov and published on figshare, lists results across all synthetic instances generated for the study. It was last updated on April 20, 2026.

Use Cases

• Benchmarking algorithm performance based on the listed metrics across synthetic instances.
• Analyzing trade-offs between objectives in a maximal covering location problem based on the reported results.
• Reproducing or validating the findings of the associated research paper based on the full results annex.

Strengths

• Dataset is directly linked to a specific, peer-reviewed research paper, providing clear context.
• Published under a permissive CC-BY-4.0 license, allowing for reuse with attribution.
• Last update timestamp of 2026-04-20 indicates recent publication.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 169.6 KB size suggests a limited scope, likely containing summary results rather than raw computational data.

Provenance

Source

figshare, as an annex to the paper 'multi-objective evolutionary algorithms for solving a variant of bi-objective maximal covering location problem'.

Collection Method

Likely generated computationally as part of the research study's experimental results.

Time Range

Associated with research published in 2026.

Freshness

Last updated 2026-04-20 07:53:29; freshness should be verified.

Geography

Synthetic instances likely have no specific geographic coverage.