docs(examples): add Ulysses16 TSP and FT06 bi-objective JSS benchmarks
Two canonical combinatorial optimization benchmarks demonstrating the new permutation toolkit: - tsp_ulysses16.rs — single-objective TSP via GeneticAlgorithm using OrderCrossover + InversionMutation. Reaches the known TSPLIB optimum (6859) for the 16-city Ulysses GEO-distance instance. - jss_ft06_bi.rs — bi-objective JSS (makespan + total flow time) on the Fisher-Thompson 6x6 benchmark via NSGA-II using a local POX crossover + SwapMutation. Makespan corner reaches the known single-objective optimum (55).
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//! Solve a bi-objective extension of the Fisher–Thompson FT06 job-shop
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//! scheduling benchmark using NSGA-II.
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//!
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//! - **Benchmark**: FT06 (Fisher & Thompson, 1963), 6 jobs × 6 machines, 36
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//! operations total. Each operation has a fixed machine and processing
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//! time; operations within a job must run in the given order.
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//! - **Canonical (single-objective) optimum**: makespan **55**.
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//! - **Bi-objective extension** (this example):
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//! - f₁ = makespan (Cₘₐₓ)
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//! - f₂ = total flow time Σⱼ Cⱼ
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//!
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//! Both are standard JSS objectives in the multi-objective literature.
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//! - **Algorithm**: [`Nsga2`].
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//! - **Encoding**: operation-based string of length 36, each job id appears
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//! 6 times. The k-th occurrence of job `j` represents the k-th operation
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//! of job `j`.
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//! - **Variation**: a local `PrecedenceOrderCrossover` (POX) piped into
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//! [`InversionMutation`] via [`CompositeVariation`]. The strict-permutation
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//! crossovers shipped in the library (OX, PMX, CX, ERX) would break the
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//! operation-string multiset, so this example defines a small JSS-aware
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//! crossover inline. POX is the standard crossover for operation-based JSS
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//! GAs (Lee & Yamakawa, 1996; Bierwirth et al., 1996).
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//! - **Initializer**: [`ShuffledMultisetPermutation`].
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//!
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//! Sources:
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//! - Fisher, H., Thompson, G. L. (1963). *Probabilistic learning combinations
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//! of local job-shop scheduling rules.*
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//! - OR-Library / JSPLIB FT06 instance file.
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//!
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//! Run with:
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//!
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//! ```bash
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//! cargo run --release --example jss_ft06_bi
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//! ```
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use heuropt::prelude::*;
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use rand::Rng as _;
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/// Precedence-preserving Order-based Crossover for operation-string JSS
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/// encodings. Partitions job ids into two sets J1 / J2; the child takes
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/// positions occupied by J1 from parent A and fills the remaining positions
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/// with J2's operations in parent B's order. Two children are produced by
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/// reversing the parent roles.
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///
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/// Preserves the JSS multiset invariant (each job id appears `N_MACHINES`
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/// times) because every operation in the multiset is covered exactly once:
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/// J1 ops by parent A, J2 ops by parent B.
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#[derive(Debug, Clone, Copy, Default)]
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struct PrecedenceOrderCrossover;
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impl Variation<Vec<usize>> for PrecedenceOrderCrossover {
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fn vary(&mut self, parents: &[Vec<usize>], rng: &mut Rng) -> Vec<Vec<usize>> {
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assert!(parents.len() >= 2, "POX requires 2 parents");
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let p1 = &parents[0];
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let p2 = &parents[1];
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let mut in_j1 = [false; N_JOBS];
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// Ensure both partitions are non-empty to avoid degenerate (child == one parent).
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loop {
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for slot in &mut in_j1 {
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*slot = rng.random_bool(0.5);
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}
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let n_in_j1 = in_j1.iter().filter(|&&b| b).count();
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if n_in_j1 > 0 && n_in_j1 < N_JOBS {
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break;
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}
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}
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vec![pox_child(p1, p2, &in_j1), pox_child(p2, p1, &in_j1)]
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}
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}
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fn pox_child(donor: &[usize], filler: &[usize], in_donor_set: &[bool]) -> Vec<usize> {
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let n = donor.len();
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let mut child = vec![usize::MAX; n];
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for k in 0..n {
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if in_donor_set[donor[k]] {
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child[k] = donor[k];
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}
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}
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let mut fill_idx = 0;
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for &v in filler {
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if !in_donor_set[v] {
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while fill_idx < n && child[fill_idx] != usize::MAX {
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fill_idx += 1;
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}
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child[fill_idx] = v;
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fill_idx += 1;
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}
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}
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child
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}
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/// FT06 routing — machine id for the k-th operation of job j.
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const FT06_MACHINE: [[usize; 6]; 6] = [
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[2, 0, 1, 3, 5, 4],
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[1, 2, 4, 5, 0, 3],
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[2, 3, 5, 0, 1, 4],
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[1, 0, 2, 3, 4, 5],
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[2, 1, 4, 5, 0, 3],
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[1, 3, 5, 0, 4, 2],
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];
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/// FT06 processing times — duration of the k-th operation of job j on the
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/// machine given by `FT06_MACHINE[j][k]`.
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const FT06_TIME: [[f64; 6]; 6] = [
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[1.0, 3.0, 6.0, 7.0, 3.0, 6.0],
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[8.0, 5.0, 10.0, 10.0, 10.0, 4.0],
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[5.0, 4.0, 8.0, 9.0, 1.0, 7.0],
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[5.0, 5.0, 5.0, 3.0, 8.0, 9.0],
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[9.0, 3.0, 5.0, 4.0, 3.0, 1.0],
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[3.0, 3.0, 9.0, 10.0, 4.0, 1.0],
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];
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const N_JOBS: usize = 6;
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const N_MACHINES: usize = 6;
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const KNOWN_MAKESPAN_OPTIMUM: f64 = 55.0;
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struct Ft06BiObjective;
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impl Problem for Ft06BiObjective {
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type Decision = Vec<usize>;
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fn objectives(&self) -> ObjectiveSpace {
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ObjectiveSpace::new(vec![
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Objective::minimize("makespan"),
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Objective::minimize("total_flow_time"),
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])
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}
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fn evaluate(&self, schedule: &Vec<usize>) -> Evaluation {
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let mut job_next = [0_usize; N_JOBS];
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let mut job_clock = [0.0_f64; N_JOBS];
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let mut machine_clock = [0.0_f64; N_MACHINES];
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for &job in schedule {
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let k = job_next[job];
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let m = FT06_MACHINE[job][k];
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let t = FT06_TIME[job][k];
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let start = job_clock[job].max(machine_clock[m]);
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let end = start + t;
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job_clock[job] = end;
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machine_clock[m] = end;
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job_next[job] = k + 1;
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}
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let makespan = machine_clock.iter().cloned().fold(0.0_f64, f64::max);
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let flow_time: f64 = job_clock.iter().sum();
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Evaluation::new(vec![makespan, flow_time])
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}
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fn decision_schema(&self) -> Vec<DecisionVariable> {
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(0..N_JOBS * N_MACHINES)
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.map(|k| DecisionVariable::new(format!("op_slot_{k}")))
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.collect()
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}
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}
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fn main() {
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let problem = Ft06BiObjective;
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let mut optimizer = Nsga2::new(
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Nsga2Config {
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population_size: 200,
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generations: 1500,
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seed: 7,
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},
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ShuffledMultisetPermutation::new(vec![N_MACHINES; N_JOBS]),
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CompositeVariation {
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crossover: PrecedenceOrderCrossover,
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mutation: SwapMutation,
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},
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);
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let result = optimizer.run(&problem);
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println!("FT06 — bi-objective JSS via NSGA-II");
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println!("Source: Fisher & Thompson (1963); known single-objective optimum makespan = 55");
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println!();
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println!("Total evaluations: {}", result.evaluations);
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println!("Pareto-front size: {}", result.pareto_front.len());
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println!();
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// Sort front by makespan ascending and print a sample of points.
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let mut front: Vec<&Candidate<Vec<usize>>> = result.pareto_front.iter().collect();
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front.sort_by(|a, b| {
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a.evaluation.objectives[0]
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.partial_cmp(&b.evaluation.objectives[0])
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.unwrap_or(std::cmp::Ordering::Equal)
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});
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// Deduplicate by objective values so the output isn't a wall of identical rows.
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let mut seen: Vec<(i64, i64)> = Vec::new();
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println!(" makespan total flow time");
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for c in &front {
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let o = &c.evaluation.objectives;
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let key = (o[0] as i64, o[1] as i64);
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if !seen.contains(&key) {
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seen.push(key);
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println!(" {:>8.0} {:>15.0}", o[0], o[1]);
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}
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}
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println!(" ({} unique objective-space points)", seen.len());
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println!();
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// Compare the makespan-corner against the known optimum.
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if let Some(makespan_corner) = front.first() {
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let best_makespan = makespan_corner.evaluation.objectives[0];
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let gap_abs = best_makespan - KNOWN_MAKESPAN_OPTIMUM;
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let gap_pct = 100.0 * gap_abs / KNOWN_MAKESPAN_OPTIMUM;
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println!(
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"Makespan corner: {:.0} vs. known optimum 55 (gap {:+.0}, {:+.2}%)",
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best_makespan, gap_abs, gap_pct
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);
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}
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}
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@@ -0,0 +1,168 @@
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//! Solve the Ulysses16 TSP benchmark from TSPLIB using a Genetic Algorithm
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//! with the new permutation-toolkit operators.
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//!
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//! - **Benchmark**: Ulysses16 (Groetschel/Padberg "Odyssey of Ulysses"),
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//! 16 cities, GEO distance metric (TSPLIB-95).
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//! - **Known optimum**: tour length **6859**.
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//! - **Algorithm**: [`GeneticAlgorithm`] with elitism.
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//! - **Variation**: [`OrderCrossover`] (OX) → [`InversionMutation`], piped
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//! via [`CompositeVariation`].
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//! - **Initializer**: [`ShuffledPermutation`].
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//!
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//! Source: TSPLIB95
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//! <http://comopt.ifi.uni-heidelberg.de/software/TSPLIB95/tsp/>
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//!
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//! Run with:
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//!
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//! ```bash
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//! cargo run --release --example tsp_ulysses16
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//! ```
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//!
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//! The GA reliably converges to within a few percent of the known optimum on
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//! this instance; on most seeds it hits 6859 exactly.
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use heuropt::prelude::*;
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/// TSPLIB Ulysses16 coordinates as `(lat, lon)` in TSPLIB DD.MM format.
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///
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/// The "decimal" part is *minutes* (out of 60), not a true decimal fraction;
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/// the GEO distance formula handles the conversion.
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const ULYSSES16: [(f64, f64); 16] = [
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(38.24, 20.42),
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(39.57, 26.15),
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(40.56, 25.32),
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(36.26, 23.12),
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(33.48, 10.54),
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(37.56, 12.19),
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(38.42, 13.11),
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(37.52, 20.44),
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(41.23, 9.10),
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(41.17, 13.05),
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(36.08, -5.21),
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(38.47, 15.13),
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(38.15, 15.35),
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(37.51, 15.17),
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(35.49, 14.32),
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(39.36, 19.56),
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];
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const KNOWN_OPTIMUM: f64 = 6859.0;
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/// TSPLIB-95 GEO distance metric.
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///
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/// Coordinates are interpreted as latitude/longitude in DD.MM (decimal-degrees
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/// with the fractional part being minutes/100), converted to radians, and the
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/// arc length between the two points on a sphere of radius `RRR = 6378.388`
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/// is rounded to the next integer (`floor(d + 1)`).
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fn geo_distance_matrix(coords: &[(f64, f64)]) -> Vec<Vec<f64>> {
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const RRR: f64 = 6378.388;
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let to_radians = |x: f64| {
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let deg = x.trunc();
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let min = x - deg;
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std::f64::consts::PI * (deg + 5.0 * min / 3.0) / 180.0
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};
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let radians: Vec<(f64, f64)> = coords
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.iter()
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.map(|&(la, lo)| (to_radians(la), to_radians(lo)))
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.collect();
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let n = radians.len();
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let mut d = vec![vec![0.0_f64; n]; n];
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for i in 0..n {
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for j in (i + 1)..n {
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let (la_i, lo_i) = radians[i];
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let (la_j, lo_j) = radians[j];
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let q1 = (lo_i - lo_j).cos();
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let q2 = (la_i - la_j).cos();
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let q3 = (la_i + la_j).cos();
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let dij = (RRR * (0.5 * ((1.0 + q1) * q2 - (1.0 - q1) * q3)).acos() + 1.0).trunc();
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d[i][j] = dij;
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d[j][i] = dij;
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}
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}
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d
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}
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struct Ulysses16Tsp {
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dist: Vec<Vec<f64>>,
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}
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impl Ulysses16Tsp {
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fn new() -> Self {
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Self {
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dist: geo_distance_matrix(&ULYSSES16),
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}
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}
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fn tour_length(&self, tour: &[usize]) -> f64 {
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let n = tour.len();
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let mut total = 0.0;
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for i in 0..n {
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let a = tour[i];
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let b = tour[(i + 1) % n];
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total += self.dist[a][b];
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}
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total
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}
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}
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impl Problem for Ulysses16Tsp {
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type Decision = Vec<usize>;
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fn objectives(&self) -> ObjectiveSpace {
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ObjectiveSpace::new(vec![Objective::minimize("tour_length")])
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}
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fn evaluate(&self, tour: &Vec<usize>) -> Evaluation {
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Evaluation::new(vec![self.tour_length(tour)])
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}
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fn decision_schema(&self) -> Vec<DecisionVariable> {
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(0..ULYSSES16.len())
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.map(|k| DecisionVariable::new(format!("tour_position_{k}")))
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.collect()
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}
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}
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fn main() {
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let problem = Ulysses16Tsp::new();
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let n = ULYSSES16.len();
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let mut optimizer = GeneticAlgorithm::new(
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GeneticAlgorithmConfig {
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population_size: 150,
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generations: 1500,
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tournament_size: 3,
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elitism: 4,
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seed: 42,
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},
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ShuffledPermutation { n },
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CompositeVariation {
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crossover: OrderCrossover,
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mutation: InversionMutation,
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},
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);
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let result = optimizer.run(&problem);
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let best = result.best.expect("GA always returns a best candidate");
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let best_len = best.evaluation.objectives[0];
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let gap_abs = best_len - KNOWN_OPTIMUM;
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let gap_pct = 100.0 * gap_abs / KNOWN_OPTIMUM;
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println!("TSPLIB Ulysses16 — single-objective TSP via Genetic Algorithm");
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println!("Source: TSPLIB95 (Groetschel/Padberg)");
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println!();
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println!("Known optimum: {:>8.0}", KNOWN_OPTIMUM);
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println!("GA best found: {:>8.0} (gap {:+.0}, {:+.2}%)", best_len, gap_abs, gap_pct);
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println!();
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println!("Total evaluations: {}", result.evaluations);
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println!("Final population: {}", result.population.len());
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println!();
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println!("Tour (city indices, returning to start):");
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for (i, c) in best.decision.iter().enumerate() {
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print!("{:>3}", c);
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if i + 1 < best.decision.len() {
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print!(" → ");
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}
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}
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println!(" → {}", best.decision[0]);
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}
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