mahf 0.1.0

A framework for modular construction and evaluation of metaheuristics.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131

//! Invasive Weed Optimization (IWO).
//!
//! # References
//!
//! \[1\] Ali Reza Mehrabian and Caro Lucas. 2006.
//! A novel numerical optimization algorithm inspired from weed colonization.
//! Ecological Informatics 1, 4 (December 2006), 355–366.
//! DOI:<https://doi.org/10/dc4njs>

use eyre::ensure;

use crate::{
    component::ExecResult,
    components::{boundary, initialization, mapping, mutation, replacement, selection, Block},
    conditions::Condition,
    configuration::Configuration,
    identifier::{Global, Identifier},
    lens::ValueOf,
    logging::Logger,
    problems::{LimitedVectorProblem, SingleObjectiveProblem},
    state::common,
    Component,
};

#[derive(Clone, Debug)]
pub struct RealProblemParameters {
    pub initial_population_size: u32,
    pub max_population_size: u32,
    pub min_number_of_seeds: u32,
    pub max_number_of_seeds: u32,
    pub initial_deviation: f64,
    pub final_deviation: f64,
    pub modulation_index: u32,
}

/// An example single-objective Invasive Weed Optimization operating on a real search space.
///
/// Uses the [iwo] component internally.
///
/// # Requirements
/// - initial_population_size <= max_population_size
/// - min_number_of_seeds <= max_number_of_seeds
/// - final_deviation <= initial_deviation
pub fn real_iwo<P>(
    params: RealProblemParameters,
    condition: Box<dyn Condition<P>>,
) -> ExecResult<Configuration<P>>
where
    P: SingleObjectiveProblem + LimitedVectorProblem<Element = f64>,
{
    let RealProblemParameters {
        initial_population_size,
        max_population_size,
        min_number_of_seeds,
        max_number_of_seeds,
        initial_deviation,
        final_deviation,
        modulation_index,
    } = params;

    ensure!(initial_population_size <= max_population_size, "it is not possible to select more individuals with MuPlusLambda selection than are present");
    ensure!(
        !(initial_deviation..final_deviation).is_empty(),
        "the std_dev range must not be empty for this operator"
    );

    Ok(Configuration::builder()
        .do_(initialization::RandomSpread::new(initial_population_size))
        .evaluate()
        .update_best_individual()
        .do_(iwo::<P, Global>(
            Parameters {
                max_population_size,
                min_number_of_seeds,
                max_number_of_seeds,
                mutation: Block::new([
                    mutation::NormalMutation::new(initial_deviation, 1.),
                    mapping::Polynomial::new(
                        initial_deviation,
                        final_deviation,
                        modulation_index as f64,
                        ValueOf::<common::Progress<ValueOf<common::Iterations>>>::new(),
                        ValueOf::<mutation::MutationStrength<mutation::NormalMutation>>::new(),
                    ),
                ]),
                constraints: boundary::Saturation::new(),
            },
            condition,
        ))
        .build())
}

/// Basic building blocks of [`iwo`].
pub struct Parameters<P> {
    pub max_population_size: u32,
    pub min_number_of_seeds: u32,
    pub max_number_of_seeds: u32,
    pub mutation: Box<dyn Component<P>>,
    pub constraints: Box<dyn Component<P>>,
}

/// A generic single-objective Invasive Weed Optimization (IWO) template.
pub fn iwo<P, I>(params: Parameters<P>, condition: Box<dyn Condition<P>>) -> Box<dyn Component<P>>
where
    P: SingleObjectiveProblem,
    I: Identifier,
{
    let Parameters {
        max_population_size,
        min_number_of_seeds,
        max_number_of_seeds,
        mutation,
        constraints,
    } = params;

    Configuration::builder()
        .while_(condition, |builder| {
            builder
                .do_(selection::iwo::DeterministicFitnessProportional::new(
                    min_number_of_seeds,
                    max_number_of_seeds,
                ))
                .do_(mutation)
                .do_(constraints)
                .evaluate_with::<I>()
                .update_best_individual()
                .do_(replacement::MuPlusLambda::new(max_population_size))
                .do_(Logger::new())
        })
        .build_component()
}