Post-hoc Wilcoxon Test Results for Felis Catus Optimization Algorithm

Felis Catus Optimization (FCO) is a novel metaheuristic algorithm modeled on the behavior of urban domestic cats. The dataset contains post-hoc Wilcoxon test results comparing FCO's performance against seven other algorithms on CEC 2005 and CEC 2017 benchmark functions. Authored by Mohammad Salehi and last updated in April 2026, the 5.5 KB Excel file validates FCO's competitive ranking and robust convergence pattern.

Use Cases

• Benchmarking new optimization algorithms based on the provided CEC 2005 and CEC 2017 test results.
• Conducting statistical analysis of algorithm performance using the post-hoc Wilcoxon test results.
• Applying the FCO algorithm to real-world engineering design problems, as referenced in the description.
• Studying population dynamics and convergence patterns in nature-inspired metaheuristics.

Strengths

• Results are validated against two established benchmark suites: CEC 2005 and CEC 2017.
• Statistical significance of results is confirmed using Holm's post-hoc procedure (p < 0.05).
• Algorithm performance is also demonstrated on three real-world engineering design problems.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Likely contains statistical test results from computational experiments.

Freshness

Last updated 2026-04-15 18:08:27