samyama_optimization/algorithms/
tlbo.rs

1use crate::common::{Individual, OptimizationResult, Problem, SolverConfig};
2use ndarray::Array1;
3use rand::prelude::*;
4use rayon::prelude::*;
5
6pub struct TLBOSolver {
7    pub config: SolverConfig,
8}
9
10impl TLBOSolver {
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        // Initialize population
21        let mut population: Vec<Individual> = (0..self.config.population_size)
22            .map(|_| {
23                let mut vars = Array1::zeros(dim);
24                for i in 0..dim {
25                    vars[i] = rng.gen_range(lower[i]..upper[i]);
26                }
27                let fitness = problem.fitness(&vars);
28                Individual::new(vars, fitness)
29            })
30            .collect();
31
32        let mut history = Vec::with_capacity(self.config.max_iterations);
33
34        for iter in 0..self.config.max_iterations {
35            if iter % 10 == 0 {
36                println!("TLBO Solver: Iteration {}/{}", iter, self.config.max_iterations);
37            }
38            let best_idx = self.find_best(&population);
39            let teacher_vars = population[best_idx].variables.clone();
40            let best_fitness = population[best_idx].fitness;
41            let mean_vars = self.calculate_mean(&population, dim);
42
43            history.push(best_fitness);
44
45            // 1. Teacher Phase
46            population = population
47                .into_par_iter()
48                .map(|mut ind| {
49                    let mut local_rng = thread_rng();
50                    let tf: f64 = local_rng.gen_range(1..3) as f64; // Teaching Factor (1 or 2)
51                    let mut new_vars = Array1::zeros(dim);
52
53                    for j in 0..dim {
54                        let r: f64 = local_rng.gen();
55                        let delta = r * (teacher_vars[j] - tf * mean_vars[j]);
56                        new_vars[j] = (ind.variables[j] + delta).clamp(lower[j], upper[j]);
57                    }
58
59                    let new_fitness = problem.fitness(&new_vars);
60                    if new_fitness < ind.fitness {
61                        ind.variables = new_vars;
62                        ind.fitness = new_fitness;
63                    }
64                    ind
65                })
66                .collect();
67
68            // 2. Learner Phase
69            let pop_len = population.len();
70            for i in 0..pop_len {
71                let mut learner_j_idx;
72                loop {
73                    learner_j_idx = rng.gen_range(0..pop_len);
74                    if learner_j_idx != i { break; }
75                }
76
77                let ind_i = &population[i];
78                let ind_j = &population[learner_j_idx];
79                
80                let mut new_vars = Array1::zeros(dim);
81                for k in 0..dim {
82                    let r: f64 = rng.gen();
83                    let delta = if ind_i.fitness < ind_j.fitness {
84                        r * (&ind_i.variables[k] - &ind_j.variables[k])
85                    } else {
86                        r * (&ind_j.variables[k] - &ind_i.variables[k])
87                    };
88                    new_vars[k] = (ind_i.variables[k] + delta).clamp(lower[k], upper[k]);
89                }
90
91                let new_fitness = problem.fitness(&new_vars);
92                if new_fitness < population[i].fitness {
93                    population[i].variables = new_vars;
94                    population[i].fitness = new_fitness;
95                }
96            }
97        }
98
99        let final_best_idx = self.find_best(&population);
100        let final_best = &population[final_best_idx];
101
102        OptimizationResult {
103            best_variables: final_best.variables.clone(),
104            best_fitness: final_best.fitness,
105            history,
106        }
107    }
108
109    fn find_best(&self, population: &[Individual]) -> usize {
110        let mut best_idx = 0;
111        for (i, ind) in population.iter().enumerate() {
112            if ind.fitness < population[best_idx].fitness {
113                best_idx = i;
114            }
115        }
116        best_idx
117    }
118
119    fn calculate_mean(&self, population: &[Individual], dim: usize) -> Array1<f64> {
120        let mut mean = Array1::zeros(dim);
121        for ind in population {
122            mean += &ind.variables;
123        }
124        mean / (population.len() as f64)
125    }
126}
samyama_optimization/algorithms/tlbo.rs

samyama_optimization/algorithms/
tlbo.rs
