MinCost

A collection of modern heuristic optimization toolkit.

Overwiew

1. Genetic Algorithm

2. Simulated Annealing

3. Particle Swarm

4. Tabu Search(TODO)

Features

• Easy to embed

  This is the primary design goal of mincost. See how to embed genetic algorithm into your Rust project, please refer to examples

• Flexiable encoding style

  In mincost, you can encode your solution with various style. ie, in i32, i16, i8 and even boolean.

• Initialize solution by custom randness

  The solution can be initialized by your custom randomization strategy by closure. Refer to examples

License

Licensed under either of these:

• Apache License, Version 2.0, (LICENSE-APACHE or https://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or https://opensource.org/licenses/MIT)

The project is in the early stage of construction, especially docs && examples.

lib.rs:

A collection of modern heuristic optimization toolkit.

There are tree common optimization methods in the crate currently. 1.genetic algorithm 2.simluated annealing 3.particle swarm optimization

You can fit any of these methods into your project by enabling relavant features To use genetic algorithm

[dependencies]
mincost = { version = "0.1.1", features = ["ga"] }

To use simulated annealing algorithm

[dependencies]
mincost = { version = "0.1.1", features = ["sa"] }

To use particle swarm optimization

[dependencies]
mincost = { version = "0.1.1", features = ["pso"] }