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name: routing-and-combinatorial description: "Use scikit-opt route and permutation optimizers for TSP-style problems." disable-model-invocation: true metadata: disco-role: operating license: MIT

routing-and-combinatorial

Use this sub-skill when a task is about permutation search, TSP-style routing, fixed-endpoint route construction, or other combinatorial route objectives in scikit-opt / sko.

Read these bundled files

• references/api-reference.md — exact signatures and route-representation notes for GA_TSP, SA_TSP, ACA_TSP, and IA_TSP, plus the function_for_TSP helper. Read this when you need parameter names or route-state details.
• references/workflows.md — how to build a distance matrix, define a route-cost function, handle fixed endpoints, and choose a route optimizer. Read this before solving a routing task.
• references/troubleshooting.md — fixes for non-permutation outputs, invalid distance matrices, endpoint handling, plotting extras, and the PSO_TSP version-specific failure. Read this when route behavior is confusing.
• scripts/smoke_routing_algorithms.py — tiny deterministic CLI smoke for route optimizers using an in-script coordinate fixture. Run this after changes or before handoff.

Route elsewhere when needed

• Route continuous vectors and constrained real-valued optimization to continuous-optimizers.
• Route GA integer/mixed-precision search and custom operators to genetic-algorithms.
• Route objective run modes, benchmark functions, and optional GPU acceleration to objective-functions-and-speedups.

Core guidance

• Use a permutation objective that returns the total route cost.
• Build a square distance matrix from coordinates before optimizing.
• Validate that every best route contains each city exactly once.
• Treat PSO_TSP as a known version-specific caveat in this release, not as a reliable default route solver.

This sub-skill stays focused on route representation and permutation workflows; the bundled references carry the exact recipes and caveats.