pub struct SimulatedAnnealingParameters<T, N>where
T: Clone,
N: NeighborhoodOperator<T>,{
pub neighborhood: N,
pub initial_temperature: f64,
pub minimum_temperature: f64,
pub cooling_rate: f64,
pub termination_criteria: TerminationCriteria,
pub random_seed: Option<u64>,
/* private fields */
}Fields§
§neighborhood: N§initial_temperature: f64§minimum_temperature: f64§cooling_rate: f64§termination_criteria: TerminationCriteria§random_seed: Option<u64>Implementations§
Source§impl<T, N> SimulatedAnnealingParameters<T, N>where
T: Clone,
N: NeighborhoodOperator<T>,
impl<T, N> SimulatedAnnealingParameters<T, N>where
T: Clone,
N: NeighborhoodOperator<T>,
Sourcepub fn new(
neighborhood: N,
initial_temperature: f64,
cooling_rate: f64,
termination_criteria: TerminationCriteria,
) -> Self
pub fn new( neighborhood: N, initial_temperature: f64, cooling_rate: f64, termination_criteria: TerminationCriteria, ) -> Self
Examples found in repository?
examples/experiment_parallel_vs_sequential.rs (lines 58-63)
35fn run_experiment(parallel: bool, runs: usize) -> Result<(Duration, ExperimentReport), String> {
36 let problem = build_problem();
37
38 let hill_climbing_case = HillClimbingParameters::new(
39 BitFlipNeighborhood::new(),
40 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(180)]),
41 )
42 .with_seed(111);
43
44 // Keep GA internally sequential to focus the comparison on experiment-level parallelism.
45 let genetic_algorithm_case = GeneticAlgorithmParameters::new(
46 80,
47 0.90,
48 0.06,
49 SinglePointCrossover::new(),
50 BitFlipMutation::new(),
51 BinaryTournamentSelection::new(),
52 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(60)]),
53 )
54 .with_elite_size(1)
55 .with_seed(222)
56 .sequential();
57
58 let simulated_annealing_case = SimulatedAnnealingParameters::new(
59 BitFlipNeighborhood::new(),
60 45.0,
61 0.985,
62 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(220)]),
63 )
64 .with_seed(333);
65
66 let pso_case = PSOParameters::new(
67 50,
68 0.72,
69 1.49,
70 1.49,
71 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(120)]),
72 )
73 .with_velocity_clamp(4.0)
74 .with_seed(444);
75
76 let experiment = Experiment::new(problem)
77 .with_runs(runs)
78 .add_case(hill_climbing_case)
79 .add_case(genetic_algorithm_case)
80 .add_case(simulated_annealing_case)
81 .add_case(pso_case);
82
83 measure_result(|| {
84 if parallel {
85 experiment.with_parallel().execute()
86 } else {
87 experiment.sequential().execute()
88 }
89 })
90}More examples
examples/mono_objective_experiment.rs (lines 51-56)
13fn main() {
14 let problem = KnapsackBuilder::new()
15 .with_capacity(150.0)
16 .add_item(1.0, 2.0)
17 .add_item(1.0, 2.0)
18 .add_item(2.0, 6.0)
19 .add_item(2.0, 6.5)
20 .add_item(3.0, 7.0)
21 .add_item(10.0, 20.0)
22 .add_item(20.0, 30.0)
23 .add_item(30.0, 60.0)
24 .add_item(35.0, 65.0)
25 .add_item(45.0, 100.0)
26 .add_item(55.0, 120.0)
27 .add_item(75.0, 211.0)
28 .add_item(75.0, 211.0)
29 .add_item(80.0, 160.0)
30 .add_item(90.0, 301.0)
31 .add_item(150.0, 301.0)
32 .build();
33
34 let hill_climbing_case = HillClimbingParameters::new(
35 BitFlipNeighborhood::new(),
36 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(180)]),
37 );
38
39 let genetic_algorithm_case = GeneticAlgorithmParameters::new(
40 80,
41 0.90,
42 0.06,
43 SinglePointCrossover::new(),
44 BitFlipMutation::new(),
45 BinaryTournamentSelection::new(),
46 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(60)]),
47 )
48 .with_elite_size(1)
49 .with_threads(4);
50
51 let simulated_annealing_case = SimulatedAnnealingParameters::new(
52 BitFlipNeighborhood::new(),
53 45.0,
54 0.985,
55 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(220)]),
56 )
57 .with_seed(777);
58
59 let pso_case = PSOParameters::new(
60 50,
61 0.72,
62 1.49,
63 1.49,
64 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(120)]),
65 )
66 .with_velocity_clamp(4.0)
67 .with_seed(999);
68
69 let report = Experiment::new(problem)
70 .with_runs(24)
71 .add_case(hill_climbing_case)
72 .add_case(genetic_algorithm_case)
73 .add_case(simulated_annealing_case)
74 .add_case(pso_case)
75 .execute();
76
77 match report {
78 Ok(report) => println!("{}", report.to_text_table()),
79 Err(error) => eprintln!("Experiment execution failed: {}", error),
80 }
81}pub fn with_minimum_temperature(self, minimum_temperature: f64) -> Self
Sourcepub fn with_seed(self, seed: u64) -> Self
pub fn with_seed(self, seed: u64) -> Self
Examples found in repository?
examples/experiment_parallel_vs_sequential.rs (line 64)
35fn run_experiment(parallel: bool, runs: usize) -> Result<(Duration, ExperimentReport), String> {
36 let problem = build_problem();
37
38 let hill_climbing_case = HillClimbingParameters::new(
39 BitFlipNeighborhood::new(),
40 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(180)]),
41 )
42 .with_seed(111);
43
44 // Keep GA internally sequential to focus the comparison on experiment-level parallelism.
45 let genetic_algorithm_case = GeneticAlgorithmParameters::new(
46 80,
47 0.90,
48 0.06,
49 SinglePointCrossover::new(),
50 BitFlipMutation::new(),
51 BinaryTournamentSelection::new(),
52 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(60)]),
53 )
54 .with_elite_size(1)
55 .with_seed(222)
56 .sequential();
57
58 let simulated_annealing_case = SimulatedAnnealingParameters::new(
59 BitFlipNeighborhood::new(),
60 45.0,
61 0.985,
62 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(220)]),
63 )
64 .with_seed(333);
65
66 let pso_case = PSOParameters::new(
67 50,
68 0.72,
69 1.49,
70 1.49,
71 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(120)]),
72 )
73 .with_velocity_clamp(4.0)
74 .with_seed(444);
75
76 let experiment = Experiment::new(problem)
77 .with_runs(runs)
78 .add_case(hill_climbing_case)
79 .add_case(genetic_algorithm_case)
80 .add_case(simulated_annealing_case)
81 .add_case(pso_case);
82
83 measure_result(|| {
84 if parallel {
85 experiment.with_parallel().execute()
86 } else {
87 experiment.sequential().execute()
88 }
89 })
90}More examples
examples/mono_objective_experiment.rs (line 57)
13fn main() {
14 let problem = KnapsackBuilder::new()
15 .with_capacity(150.0)
16 .add_item(1.0, 2.0)
17 .add_item(1.0, 2.0)
18 .add_item(2.0, 6.0)
19 .add_item(2.0, 6.5)
20 .add_item(3.0, 7.0)
21 .add_item(10.0, 20.0)
22 .add_item(20.0, 30.0)
23 .add_item(30.0, 60.0)
24 .add_item(35.0, 65.0)
25 .add_item(45.0, 100.0)
26 .add_item(55.0, 120.0)
27 .add_item(75.0, 211.0)
28 .add_item(75.0, 211.0)
29 .add_item(80.0, 160.0)
30 .add_item(90.0, 301.0)
31 .add_item(150.0, 301.0)
32 .build();
33
34 let hill_climbing_case = HillClimbingParameters::new(
35 BitFlipNeighborhood::new(),
36 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(180)]),
37 );
38
39 let genetic_algorithm_case = GeneticAlgorithmParameters::new(
40 80,
41 0.90,
42 0.06,
43 SinglePointCrossover::new(),
44 BitFlipMutation::new(),
45 BinaryTournamentSelection::new(),
46 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(60)]),
47 )
48 .with_elite_size(1)
49 .with_threads(4);
50
51 let simulated_annealing_case = SimulatedAnnealingParameters::new(
52 BitFlipNeighborhood::new(),
53 45.0,
54 0.985,
55 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(220)]),
56 )
57 .with_seed(777);
58
59 let pso_case = PSOParameters::new(
60 50,
61 0.72,
62 1.49,
63 1.49,
64 TerminationCriteria::new(vec![TerminationCriterion::MaxIterations(120)]),
65 )
66 .with_velocity_clamp(4.0)
67 .with_seed(999);
68
69 let report = Experiment::new(problem)
70 .with_runs(24)
71 .add_case(hill_climbing_case)
72 .add_case(genetic_algorithm_case)
73 .add_case(simulated_annealing_case)
74 .add_case(pso_case)
75 .execute();
76
77 match report {
78 Ok(report) => println!("{}", report.to_text_table()),
79 Err(error) => eprintln!("Experiment execution failed: {}", error),
80 }
81}Trait Implementations§
Source§impl<T, N> Clone for SimulatedAnnealingParameters<T, N>
impl<T, N> Clone for SimulatedAnnealingParameters<T, N>
Source§fn clone(&self) -> SimulatedAnnealingParameters<T, N>
fn clone(&self) -> SimulatedAnnealingParameters<T, N>
Returns a duplicate of the value. Read more
1.0.0 (const: unstable) · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from
source. Read moreSource§impl<T, N, P> ExperimentalCase<T, f64, P> for SimulatedAnnealingParameters<T, N>
impl<T, N, P> ExperimentalCase<T, f64, P> for SimulatedAnnealingParameters<T, N>
fn algorithm_name(&self) -> &str
Source§fn parameters(&self) -> Vec<CaseParameter>
fn parameters(&self) -> Vec<CaseParameter>
Returns generic parameter key/value pairs for reporting.
Source§fn run(&self, problem: &P) -> Result<Box<dyn SolutionSet<T, f64>>, String>
fn run(&self, problem: &P) -> Result<Box<dyn SolutionSet<T, f64>>, String>
Creates and executes the algorithm with its configured parameters.
Source§fn parameters_as_text(&self) -> String
fn parameters_as_text(&self) -> String
Helper to print all parameters in a single textual line.
Auto Trait Implementations§
impl<T, N> Freeze for SimulatedAnnealingParameters<T, N>where
N: Freeze,
impl<T, N> RefUnwindSafe for SimulatedAnnealingParameters<T, N>where
N: RefUnwindSafe,
T: RefUnwindSafe,
impl<T, N> Send for SimulatedAnnealingParameters<T, N>
impl<T, N> Sync for SimulatedAnnealingParameters<T, N>
impl<T, N> Unpin for SimulatedAnnealingParameters<T, N>
impl<T, N> UnsafeUnpin for SimulatedAnnealingParameters<T, N>where
N: UnsafeUnpin,
impl<T, N> UnwindSafe for SimulatedAnnealingParameters<T, N>where
N: UnwindSafe,
T: UnwindSafe,
Blanket Implementations§
Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
Source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more