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name: continuous-optimizers description: "Select and run DE, PSO, SA schedules, and AFSA for continuous optimization." disable-model-invocation: true metadata: disco-role: operating license: MIT

Continuous optimizers

Use this sub-skill for non-GA continuous minimization with DE, PSO, simulated annealing, and AFSA.

Read:

• references/api-reference.md when you need constructor signatures, supported constraints, aliases, or result fields.
• references/workflows.md when you are choosing an optimizer or preparing a tiny run.
• references/troubleshooting.md when runs fail, constraints are rejected, or the objective shape is wrong.
• scripts/smoke_continuous_optimizers.py to verify a local installation with a deterministic sphere-style smoke.

Use this sub-skill when:

• the objective is continuous and scalar-valued;
• the problem is handled by DE, PSO, SA/SAFast/SABoltzmann/SACauchy, or AFSA;
• you need best_x/best_y validation or PSO history capture.

Route elsewhere:

• GA-family or custom operators -> genetic-algorithms
• route/permutation/TSP problems -> routing-and-combinatorial
• objective-vectorization, caching, run modes, or benchmark helpers -> objective-functions-and-speedups

Important limits:

• PSO enforces constraint_ueq only; constraint_eq is not implemented.
• SA and AFSA do not accept constraint callbacks; use penalties or projection in the objective if you need feasibility control.