pyMetaheuristic Lab

Optimization Laboratory

Choose a workflow to begin. Each mode exposes different configuration options
and result visualizations tailored to the analysis you need.

🔬

Single Algorithm

Standard · Constrained · Binary

Run one metaheuristic on a continuous, constrained, or binary objective. Three sub-modes: standard optimization, constraint-handling (Deb/penalty/repair), and binary via BinaryAdapter with chaotic transfer functions.

Convergence chartsDiversityExploitation ratioPopulation snapshotFeasibility bannerBinary solution

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📊

Benchmark Runner

Many algorithms · Many functions

Compare multiple algorithms across benchmark functions with repeated trials. Statistical summaries, compact mean±std tables, global rankings, timing analysis.

Mean±std tableRankingsGlobal rankingTimingCSV export

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🏝️

Collaborative Islands

Islands · Migration · Cooperation

Multiple algorithm instances exchange best solutions via migration. Ring, star, full, and other topologies. Migration log, hall of fame, per-island convergence.

Global convergencePer-island chartsMigration logHall of fame

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🎯

Orchestrated Islands

Rules/Fixed · Checkpoints · Decisions

Full cooperative engine with adaptive checkpointing and a heuristic or fixed controller. Inspect decisions at every checkpoint, action outcomes, island health and diversity telemetry.

Controller decisionsAction outcomesTelemetryCheckpoints

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Loading algorithms…

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Select an algorithm from the list
to explore its capabilities and defaults.

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Capabilities

Default Parameters

Continue to configuration →

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Click Run Optimization to start.
Results appear here live as the job runs.