scirs2_optimize/multi_objective/algorithms/
moead.rs

1//! MOEA/D (Multi-objective Evolutionary Algorithm based on Decomposition)
2//!
3//! Decomposes multi-objective optimization into scalar subproblems.
4
5use super::{MultiObjectiveOptimizer, MultiObjectiveResult};
6use crate::error::OptimizeError;
7use crate::multi_objective::solutions::{Population, Solution};
8use ndarray::{Array1, ArrayView1};
9
10/// MOEA/D optimizer
11#[derive(Debug, Clone)]
12pub struct MOEAD {
13    population_size: usize,
14    n_objectives: usize,
15    n_variables: usize,
16    weight_vectors: Vec<Vec<f64>>,
17    neighborhood_size: usize,
18    population: Population,
19    generation: usize,
20    evaluations: usize,
21}
22
23impl MOEAD {
24    /// Create new MOEA/D optimizer
25    pub fn new(population_size: usize, n_objectives: usize, n_variables: usize) -> Self {
26        Self {
27            population_size,
28            n_objectives,
29            n_variables,
30            weight_vectors: Vec::new(),
31            neighborhood_size: 20,
32            population: Population::with_capacity(population_size, n_objectives, n_variables),
33            generation: 0,
34            evaluations: 0,
35        }
36    }
37
38    /// Generate uniform weight vectors
39    fn generate_weight_vectors(&mut self) {
40        // TODO: Implement weight vector generation
41    }
42
43    /// Tchebycheff approach for scalar optimization
44    fn tchebycheff(&self, objectives: &[f64], weight: &[f64], ideal_point: &[f64]) -> f64 {
45        objectives
46            .iter()
47            .zip(weight.iter())
48            .zip(ideal_point.iter())
49            .map(|((obj, w), ideal)| w * (obj - ideal).abs())
50            .fold(0.0_f64, |a, b| a.max(b))
51    }
52}
53
54impl MultiObjectiveOptimizer for MOEAD {
55    fn optimize<F>(&mut self, objective_function: F) -> Result<MultiObjectiveResult, OptimizeError>
56    where
57        F: Fn(&ArrayView1<f64>) -> Array1<f64> + Send + Sync,
58    {
59        // TODO: Implement MOEA/D algorithm
60        Ok(MultiObjectiveResult {
61            pareto_front: Vec::new(),
62            population: Vec::new(),
63            n_evaluations: 0,
64            n_generations: 0,
65            success: true,
66            message: "MOEA/D not yet implemented".to_string(),
67            hypervolume: Some(0.0),
68            metrics: Default::default(),
69        })
70    }
71
72    fn evolve_generation<F>(&mut self, _objective_function: &F) -> Result<(), OptimizeError>
73    where
74        F: Fn(&ArrayView1<f64>) -> Array1<f64> + Send + Sync,
75    {
76        self.generation += 1;
77        Ok(())
78    }
79
80    fn initialize_population(&mut self) -> Result<(), OptimizeError> {
81        // TODO: Implement population initialization
82        Ok(())
83    }
84
85    fn check_convergence(&self) -> bool {
86        // TODO: Implement convergence criteria
87        false
88    }
89
90    fn get_population(&self) -> &Population {
91        &self.population
92    }
93
94    fn get_generation(&self) -> usize {
95        self.generation
96    }
97
98    fn get_evaluations(&self) -> usize {
99        self.evaluations
100    }
101
102    fn name(&self) -> &str {
103        "MOEA/D"
104    }
105}
scirs2_optimize/multi_objective/algorithms/moead.rs

scirs2_optimize/multi_objective/algorithms/
moead.rs
