Expand description
Domain-agnostic metaheuristic optimization framework.
Provides generic implementations of common metaheuristic algorithms:
- Genetic Algorithm (GA): Population-based evolutionary optimization with pluggable selection, crossover, and mutation operators.
- BRKGA: Biased Random-Key Genetic Algorithm — the user implements only a decoder; all evolutionary mechanics are handled generically.
- Simulated Annealing (SA): Single-solution trajectory optimization with pluggable cooling schedules.
- ALNS: Adaptive Large Neighborhood Search — destroy/repair operators with adaptive weight selection.
- Dispatching: Generic priority rule composition engine for multi-rule item ranking.
- CP (Constraint Programming): Domain-agnostic modeling layer for constrained optimization with interval, integer, and boolean variables.
- Tabu Search (TS): Single-solution trajectory optimization using short-term memory (tabu list) to escape local optima.
- Variable Neighborhood Search (VNS): Systematic neighborhood switching for escaping local optima via diversified perturbation.
§Architecture
This crate sits at Layer 2 (Algorithms) in the U-Engine ecosystem,
depending only on u-numflow (Layer 1: Foundation). It contains no
domain-specific concepts — scheduling, nesting, routing, etc. are
all defined by consumers at higher layers.
Modules§
- alns
- Adaptive Large Neighborhood Search (ALNS) framework.
- brkga
- Biased Random-Key Genetic Algorithm (BRKGA).
- cp
- Constraint Programming (CP) framework.
- dispatching
- Generic priority rule composition framework.
- ga
- Genetic Algorithm framework.
- sa
- Simulated Annealing (SA).
- tabu
- Tabu Search (TS).
- vns
- Variable Neighborhood Search (VNS).