graphmind_optimization/
common.rs

1use ndarray::Array1;
2use serde::{Deserialize, Serialize};
3
4/// Represents a candidate solution in the optimization space.
5#[derive(Clone, Debug, Serialize, Deserialize)]
6pub struct Individual {
7    pub variables: Array1<f64>,
8    pub fitness: f64,
9}
10
11impl Individual {
12    pub fn new(variables: Array1<f64>, fitness: f64) -> Self {
13        Self { variables, fitness }
14    }
15}
16
17/// Defines the optimization problem.
18pub trait Problem: Send + Sync {
19    /// The objective function to minimize.
20    fn objective(&self, variables: &Array1<f64>) -> f64;
21
22    /// Optional constraints. Returns a penalty score (0 if all satisfied).
23    fn penalty(&self, _variables: &Array1<f64>) -> f64 {
24        0.0
25    }
26
27    /// Combined fitness (objective + penalty).
28    fn fitness(&self, variables: &Array1<f64>) -> f64 {
29        self.objective(variables) + self.penalty(variables)
30    }
31
32    /// Number of variables.
33    fn dim(&self) -> usize;
34
35    /// Lower and upper bounds for each variable.
36    fn bounds(&self) -> (Array1<f64>, Array1<f64>);
37}
38
39/// Represents a candidate solution in a multi-objective space.
40#[derive(Clone, Debug, Serialize, Deserialize)]
41pub struct MultiObjectiveIndividual {
42    pub variables: Array1<f64>,
43    pub fitness: Vec<f64>,
44    pub rank: usize,
45    pub crowding_distance: f64,
46}
47
48impl MultiObjectiveIndividual {
49    pub fn new(variables: Array1<f64>, fitness: Vec<f64>) -> Self {
50        Self {
51            variables,
52            fitness,
53            rank: 0,
54            crowding_distance: 0.0,
55        }
56    }
57}
58
59/// Defines a multi-objective optimization problem.
60pub trait MultiObjectiveProblem: Send + Sync {
61    /// Multiple objective functions to minimize.
62    fn objectives(&self, variables: &Array1<f64>) -> Vec<f64>;
63
64    /// Optional constraints. Returns a vector of penalties.
65    fn penalties(&self, _variables: &Array1<f64>) -> Vec<f64> {
66        vec![]
67    }
68
69    /// Number of variables.
70    fn dim(&self) -> usize;
71
72    /// Lower and upper bounds for each variable.
73    fn bounds(&self) -> (Array1<f64>, Array1<f64>);
74
75    /// Number of objectives.
76    fn num_objectives(&self) -> usize;
77}
78
79/// The result of a multi-objective optimization run (Pareto Front).
80#[derive(Debug, Serialize, Deserialize)]
81pub struct MultiObjectiveResult {
82    pub pareto_front: Vec<MultiObjectiveIndividual>,
83    pub history: Vec<f64>, // e.g., hypervolume or min of first objective
84}
85
86/// Configuration for the solver.
87#[derive(Clone, Debug, Serialize, Deserialize)]
88pub struct SolverConfig {
89    pub population_size: usize,
90    pub max_iterations: usize,
91}
92
93impl Default for SolverConfig {
94    fn default() -> Self {
95        Self {
96            population_size: 50,
97            max_iterations: 100,
98        }
99    }
100}
101
102/// The result of an optimization run.
103#[derive(Debug, Serialize, Deserialize)]
104pub struct OptimizationResult {
105    pub best_variables: Array1<f64>,
106    pub best_fitness: f64,
107    pub history: Vec<f64>,
108}
109
110/// A simple problem defined by a closure.
111pub struct SimpleProblem<F>
112where
113    F: Fn(&Array1<f64>) -> f64 + Send + Sync,
114{
115    pub objective_func: F,
116    pub dim: usize,
117    pub lower: Array1<f64>,
118    pub upper: Array1<f64>,
119}
120
121impl<F> Problem for SimpleProblem<F>
122where
123    F: Fn(&Array1<f64>) -> f64 + Send + Sync,
124{
125    fn objective(&self, variables: &Array1<f64>) -> f64 {
126        (self.objective_func)(variables)
127    }
128
129    fn dim(&self) -> usize {
130        self.dim
131    }
132
133    fn bounds(&self) -> (Array1<f64>, Array1<f64>) {
134        (self.lower.clone(), self.upper.clone())
135    }
136}
graphmind_optimization/common.rs

graphmind_optimization/
common.rs
