IGD Values of Six Algorithms on Multi-Objective Benchmark Problems

13.5 KB of Inverted Generational Distance (IGD) values comparing six multi-objective evolutionary algorithms. The dataset, authored by Wei Jiang and last updated in April 2026, contains results from evaluating an improved NSGAII algorithm (IM-NSGAII) against five other algorithms on ZDT, DTLZ, MaF, and WFG benchmark problems.

Use Cases

• Benchmarking algorithm convergence speed based on IGD values across multiple test suites.
• Comparing population diversity performance of algorithms on complex Pareto fronts.
• Validating improvements to the NSGAII algorithm using standardized benchmark results.

Strengths

• Data is focused on a specific, well-defined metric (IGD) for algorithm comparison.
• Compares six algorithms across four established benchmark problem families (ZDT, DTLZ, MaF, WFG).
• Released under a permissive CC-BY-4.0 license for reuse.

Limitations

• Row count and column-level documentation are unknown, limiting suitability assessment.
• The 13.5 KB file size suggests a very limited scope, likely containing summary results rather than raw experimental data.

Provenance

Source

Wei Jiang via figshare.

Collection Method

Likely contains experimental results from algorithm evaluations on benchmark problems.

Time Range

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Freshness

Last updated 2026-04-17 17:36:09.

Geography

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