roma_lib 0.1.1

A Rust metaheuristics framework inspired by jMetal for optimization and experimentation.
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
132
133
134
135
136

use roma_lib::{
    Algorithm,
    BinaryTournamentSelection,
    GeneticAlgorithm,
    GeneticAlgorithmParameters,
    HillClimbing,
    HillClimbingParameters,
    OrderCrossover,
    SimulatedAnnealing,
    SimulatedAnnealingParameters,
    SolutionSet,
    SwapMutation,
    TerminationCriteria,
    TerminationCriterion,
    TspProblem,
};

#[test]
fn hill_climbing_runs_on_tsp_and_returns_valid_distance() {
    let problem = TspProblem::with_distance_matrix(vec![
        vec![0.0, 8.0, 6.0, 4.0],
        vec![8.0, 0.0, 7.0, 5.0],
        vec![6.0, 7.0, 0.0, 3.0],
        vec![4.0, 5.0, 3.0, 0.0],
    ]);

    let parameters = HillClimbingParameters::new(
        SwapMutation::new(),
        0.25,
        TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(50)]),
    )
    .with_seed(42);

    let mut algorithm = HillClimbing::new(parameters);
    let result = algorithm.run(&problem).expect("Hill Climbing on TSP should succeed");

    assert_eq!(result.size(), 1);
    let solution = result.get(0).expect("Expected one solution");
    assert_eq!(solution.num_variables(), 4);
    assert!(solution.quality_value().is_finite());
}

#[test]
fn simulated_annealing_runs_on_tsp_open_route() {
    let problem = TspProblem::with_distance_matrix(vec![
        vec![0.0, 9.0, 4.0, 7.0, 8.0],
        vec![9.0, 0.0, 5.0, 6.0, 3.0],
        vec![4.0, 5.0, 0.0, 2.0, 6.0],
        vec![7.0, 6.0, 2.0, 0.0, 1.0],
        vec![8.0, 3.0, 6.0, 1.0, 0.0],
    ])
    .with_open_route();

    let parameters = SimulatedAnnealingParameters::new(
        SwapMutation::new(),
        0.3,
        60.0,
        0.99,
        TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(60)]),
    )
    .with_seed(7);

    let mut algorithm = SimulatedAnnealing::new(parameters);
    let result = algorithm
        .run(&problem)
        .expect("Simulated Annealing on TSP should succeed");

    assert_eq!(result.size(), 1);
    let solution = result.get(0).expect("Expected one solution");
    assert_eq!(solution.num_variables(), 5);
    assert!(solution.quality_value().is_finite());
}

#[test]
fn hill_climbing_respects_fixed_start_city_constraint() {
    let fixed_start_city = 3usize;
    let problem = TspProblem::with_distance_matrix(vec![
        vec![0.0, 8.0, 6.0, 4.0, 7.0],
        vec![8.0, 0.0, 7.0, 5.0, 9.0],
        vec![6.0, 7.0, 0.0, 3.0, 2.0],
        vec![4.0, 5.0, 3.0, 0.0, 1.0],
        vec![7.0, 9.0, 2.0, 1.0, 0.0],
    ])
    .with_fixed_start_city(fixed_start_city);

    let parameters = HillClimbingParameters::new(
        SwapMutation::new(),
        0.0,
        TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(20)]),
    )
    .with_seed(99);

    let mut algorithm = HillClimbing::new(parameters);
    let result = algorithm
        .run(&problem)
        .expect("Hill Climbing with fixed-start TSP should succeed");

    let solution = result.get(0).expect("Expected one solution");
    assert_eq!(solution.get_variable(0).copied(), Some(fixed_start_city));
    assert!(solution.quality_value().is_finite());
}

#[test]
fn genetic_algorithm_runs_on_tsp_and_preserves_route_validity() {
    let problem = TspProblem::with_distance_matrix(vec![
        vec![0.0, 8.0, 6.0, 4.0, 7.0],
        vec![8.0, 0.0, 7.0, 5.0, 9.0],
        vec![6.0, 7.0, 0.0, 3.0, 2.0],
        vec![4.0, 5.0, 3.0, 0.0, 1.0],
        vec![7.0, 9.0, 2.0, 1.0, 0.0],
    ]);

    let parameters = GeneticAlgorithmParameters::new(
        24,
        0.9,
        0.15,
        OrderCrossover::new(),
        SwapMutation::new(),
        BinaryTournamentSelection::new(),
        TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(25)]),
    )
    .with_elite_size(1)
    .with_seed(11)
    .sequential();

    let mut algorithm = GeneticAlgorithm::new(parameters);
    let result = algorithm.run(&problem).expect("GA on TSP should succeed");

    let best = result.best_solution(&problem).expect("Expected one best solution");
    let mut route = best.variables().to_vec();
    route.sort_unstable();

    assert_eq!(best.num_variables(), 5);
    assert_eq!(route, vec![0, 1, 2, 3, 4]);
    assert!(best.quality_value().is_finite());
}