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use super::super::{
utilities::randomness::{random_gaussian_vector, random_uniform_vector},
utilities::Solution,
};
use std::{cmp::Ordering, ops::Sub};
const NUMBER_OF_DIMENSIONS: usize = 5;
#[derive(Clone, Debug)]
pub struct Ackley {
objective_function_value: Vec<f64>,
quality_scalar: f64,
x: Vec<f64>,
}
impl Solution<Ackley> for Ackley {
const NUMBER_OF_MOVE_OPERATORS: usize = 1;
const NUMBER_OF_OBJECTIVES: usize = 1;
fn generate_initial_solution() -> Ackley {
let mut solution = Ackley {
objective_function_value: vec![0.0; Ackley::NUMBER_OF_OBJECTIVES],
x: random_uniform_vector(NUMBER_OF_DIMENSIONS, -5.0, 5.0),
quality_scalar: 0.0,
};
solution.evaluate();
solution
}
fn apply_move_operator(&mut self, _move_index: usize, temperature: f64) {
let perturbation = random_gaussian_vector(self.x.len(), 0.0, temperature);
for i in 0..self.x.len() {
self.x[i] += perturbation[i];
}
self.evaluate();
}
fn get_quality_scalar(&self) -> f64 {
self.quality_scalar
}
}
impl Ackley {
fn evaluate(&mut self) {
let n = self.x.len();
let mut fx = 0.0;
let mut square_sum = 0.0;
let mut cosine_sum = 0.0;
for xi in self.x.to_vec() {
square_sum += xi.powi(2);
cosine_sum += (2.0 * std::f64::consts::PI * xi).cos();
}
fx += -20.0 * (-0.2 * (0.5 * square_sum).sqrt()).exp();
fx -= (cosine_sum / (n as f64)).exp();
fx += std::f64::consts::E + 20.0;
self.objective_function_value = vec![fx; 1];
self.quality_scalar = -fx;
}
}
impl PartialEq for Ackley {
fn eq(&self, other: &Self) -> bool {
self.quality_scalar == other.quality_scalar
}
}
impl Eq for Ackley {}
impl PartialOrd for Ackley {
fn partial_cmp(&self, other: &Self) -> Option<Ordering> {
self.quality_scalar.partial_cmp(&other.quality_scalar)
}
}
impl Ord for Ackley {
fn cmp(&self, other: &Self) -> Ordering {
self.partial_cmp(other).unwrap()
}
}
impl Sub for Ackley {
type Output = f64;
fn sub(self, rhs: Self) -> Self::Output {
self.quality_scalar - rhs.quality_scalar
}
}