### 66 releases (40 stable)

new 9.2.1 | Jun 9, 2023 |
---|---|

9.0.1 | Apr 26, 2023 |

8.0.4 | Nov 21, 2022 |

7.7.0 | Nov 19, 2022 |

0.8.0 | Jul 27, 2021 |

#**194** in Algorithms

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Used in **2** crates
(via four-bar)

**MIT**license

70KB

1.5K
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# metaheuristics-nature

A collection of nature-inspired metaheuristic algorithms. This crate provides objective function trait, well-known methods, and tool functions let you implement your own searching method.

This crate implemented following algorithms:

- Real-coded Genetic Algorithm (RGA)
- Differential Evolution (DE)
- Particle Swarm Optimization (PSO)
- Firefly Algorithm (FA)
- Teaching-Learning Based Optimization (TLBO)

Each algorithm gives same API and default parameters to help you test different implementation. For example, you can
test another algorithm by simply replacing

to `Rga`

.`De`

`use` `metaheuristics_nature``::``{`Rga`,` Solver`}``;`
`let` `mut` report `=` `Vec``::`with_capacity`(``20``)``;`
`//` Build and run the solver
`let` s `=` `Solver``::`build`(``Rga``::`default`(``)``,` `MyFunc``::`new`(``)``)`
`.``task``(``|``ctx``|` `ctx``.`gen `==` `20``)`
`.``callback``(``|``ctx``|` `report``.``push``(`ctx`.`best_f`)``)`
`.``solve``(``)`
`.``unwrap``(``)``;`
`//` Get the optimized XY value of your function
`let` xs `=` s`.``best_parameters``(``)``;`
`let` y `=` s`.``best_fitness``(``)``;`
`//` Get the history reports
`let` y2 `=` report`[``2``]``;`

### What kinds of problems can be solved?

If your problem can be simulated and evaluated, the optimization method is the efficient way to find the best design! 🚀

Assuming that your simulation can be done with a function

, by inputting the parameters `f`

and the evaluation value `X`

, then the optimization method will try to adjust `y`

to obtain the smallest `X ={x0, x1, ...}`

`y`

. Their relationship can be written as `f``(`X`)` `=` y

.The number of the parameters

is called "dimension", imagine `X`

is the coordinate in the multi-dimension, and `X`

is the weight of the "point". If the dimension becomes higher, the problem will be more difficult to search.`y`

The "metaheuristic" algorithms use multiple points to search for the minimum value, which detects the local gradient, across the most feasible solutions and keeps away from the local optimum. Even with an unknown gradient or feasible region.

Please see the API documentation for more information.

### Gradient-based Methods

For more simple functions, for example, if the 1st derivative function is known, the gradient-based methods are recommended to use for the fastest speed. Such as OSQP.

#### Dependencies

~2.3–3.5MB

~63K SLoC