graphmind_optimization/algorithms/
bwr.rs

1use crate::common::{Individual, OptimizationResult, Problem, SolverConfig};
2use ndarray::Array1;
3use rand::prelude::*;
4use rayon::prelude::*;
5
6pub struct BWRSolver {
7    pub config: SolverConfig,
8}
9
10impl BWRSolver {
11    pub fn new(config: SolverConfig) -> Self {
12        Self { config }
13    }
14
15    pub fn solve<P: Problem>(&self, problem: &P) -> OptimizationResult {
16        let mut rng = thread_rng();
17        let dim = problem.dim();
18        let (lower, upper) = problem.bounds();
19
20        let mut population: Vec<Individual> = (0..self.config.population_size)
21            .map(|_| {
22                let mut vars = Array1::zeros(dim);
23                for i in 0..dim {
24                    vars[i] = rng.gen_range(lower[i]..upper[i]);
25                }
26                let fitness = problem.fitness(&vars);
27                Individual::new(vars, fitness)
28            })
29            .collect();
30
31        let mut history = Vec::with_capacity(self.config.max_iterations);
32
33        for iter in 0..self.config.max_iterations {
34            if iter % 10 == 0 {
35                println!(
36                    "BWR Solver: Iteration {}/{}",
37                    iter, self.config.max_iterations
38                );
39            }
40            let (best_idx, worst_idx) = self.find_best_worst(&population);
41            let best_vars = population[best_idx].variables.clone();
42            let worst_vars = population[worst_idx].variables.clone();
43            let best_fitness = population[best_idx].fitness;
44
45            history.push(best_fitness);
46
47            population = population
48                .into_par_iter()
49                .map(|mut ind| {
50                    let mut local_rng = thread_rng();
51                    let mut new_vars = Array1::zeros(dim);
52
53                    let r1: f64 = local_rng.gen();
54                    let r2: f64 = local_rng.gen();
55                    let r3: f64 = local_rng.gen();
56                    let r4: f64 = local_rng.gen();
57                    let t: f64 = local_rng.gen_range(1..3) as f64;
58
59                    let mut rand_vars = Array1::zeros(dim);
60                    for j in 0..dim {
61                        rand_vars[j] = local_rng.gen_range(lower[j]..upper[j]);
62                    }
63
64                    if r4 > 0.5 {
65                        for j in 0..dim {
66                            let delta = r1 * (best_vars[j] - t * rand_vars[j])
67                                - r2 * (worst_vars[j] - rand_vars[j]);
68                            new_vars[j] = (ind.variables[j] + delta).clamp(lower[j], upper[j]);
69                        }
70                    } else {
71                        for j in 0..dim {
72                            new_vars[j] =
73                                (upper[j] - (upper[j] - lower[j]) * r3).clamp(lower[j], upper[j]);
74                        }
75                    }
76
77                    let new_fitness = problem.fitness(&new_vars);
78                    if new_fitness < ind.fitness {
79                        ind.variables = new_vars;
80                        ind.fitness = new_fitness;
81                    }
82                    ind
83                })
84                .collect();
85        }
86
87        let (final_best_idx, _) = self.find_best_worst(&population);
88        let final_best = &population[final_best_idx];
89
90        OptimizationResult {
91            best_variables: final_best.variables.clone(),
92            best_fitness: final_best.fitness,
93            history,
94        }
95    }
96
97    fn find_best_worst(&self, population: &[Individual]) -> (usize, usize) {
98        let mut best_idx = 0;
99        let mut worst_idx = 0;
100        for (i, ind) in population.iter().enumerate() {
101            if ind.fitness < population[best_idx].fitness {
102                best_idx = i;
103            }
104            if ind.fitness > population[worst_idx].fitness {
105                worst_idx = i;
106            }
107        }
108        (best_idx, worst_idx)
109    }
110}
graphmind_optimization/algorithms/bwr.rs

graphmind_optimization/algorithms/
bwr.rs
