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//
// Implementation of Modified Equilibrium Optimizer (m-EO)
//
extern crate rand;
use rand::distributions::{Distribution, Uniform};
//use rand::prelude::ThreadRng;
use std::time::Instant;
use crate::algos::eo::EOparams;
use crate::common::*;
use crate::core::eoa::{InitializationMode, EOA};
use crate::core::genome::Genome;
use crate::core::optimization_result::OptimizationResult;
use crate::core::parameters::Parameters;
use crate::core::problem::Problem;
///
/// Sequential Modified Equilibrium Optimizer (MEO)
/// Reference:
/// "Gupta, S., Deep, K., & Mirjalili, S. (2020).
/// An efficient equilibrium optimizer with mutation strategy for numerical optimization.
/// Applied Soft Computing, 96, 106542."
///
#[derive(Debug)]
pub struct MEO<'a, T: Problem> {
pub problem: &'a mut T,
pub params: &'a EOparams<'a>,
pub optimization_result: OptimizationResult,
}
impl<'a, T: Problem> MEO<'a, T> {
pub fn new(settings: &'a EOparams, problem: &'a mut T) -> Self {
let result = OptimizationResult {
best_genome: None,
best_fitness: None,
convergence_trend: None,
computation_time: None,
err_report: None,
};
MEO {
problem,
params: settings,
optimization_result: result,
}
}
}
impl<'a, T: Problem> EOA for MEO<'a, T> {
fn run(&mut self) -> OptimizationResult {
let chronos = Instant::now();
//check paramaters
//let params = self.params.clone();
match self.params.check() {
Err(error) => OptimizationResult::get_none(error),
Ok(()) => {
let dim = self.params.get_problem_dimension(); //self.params.get_dimensions();
let particles_no = self.params.get_population_size(); //self.params.get_population_size();
let lb = self.params.get_lower_bounds(); //self.params.get_lower_bounds();
let ub = self.params.get_upper_bounds();
let max_iter = self.params.get_max_iterations();
//
// a1=2;
// a2=1;
// GP=0.5;
let a1: f64 = self.params.a1;
let a2: f64 = self.params.a2;
let gp: f64 = self.params.gp;
// Initialize variables
//Ceq1=zeros(1,dim); Ceq1_fit=inf;
//Ceq2=zeros(1,dim); Ceq2_fit=inf;
//Ceq3=zeros(1,dim); Ceq3_fit=inf;
//Ceq4=zeros(1,dim); Ceq4_fit=inf;
let mut ceq1 = vec![0.0f64; dim];
let mut ceq2 = vec![0.0f64; dim];
let mut ceq3 = vec![0.0f64; dim];
let mut ceq4 = vec![0.0f64; dim];
let mut ceq_ave = vec![0.0f64; dim];
let mut ceq1_fit = f64::MAX;
let mut ceq2_fit = f64::MAX;
let mut ceq3_fit = f64::MAX;
let mut ceq4_fit = f64::MAX;
let mut ceq1_index: usize = 0;
// Iter=0; V=1;
let mut iter = 0;
let v: f64 = 1.0;
// to store agents fitness values
let mut fitness = vec![0.0f64; particles_no];
let mut fit_old = vec![0.0f64; particles_no];
let mut c_old = vec![vec![0.0f64; dim]; particles_no];
let mut c_pool = vec![vec![0.0f64; dim]; 5];
let mut lambda = vec![0.0f64; dim];
let mut r = vec![0.0f64; dim];
let mut r1 = vec![0.0f64; dim];
let mut r2 = vec![0.0f64; dim];
let mut ceq = vec![0.0f64; dim];
let mut f = vec![0.0f64; dim];
let mut _gcp: f64 = 0.0;
//------------------------------------------
let interval = Uniform::from(0..c_pool.len());
//let between01 = Uniform::from(0.0..=1.0);
let mut rng = rand::thread_rng();
//------------------------------------------
let mut convergence_curve = vec![0.0f64; max_iter];
let mut _index: usize = 0;
let mut _g0: f64 = 0.0;
let mut _g: f64 = 0.0;
//let chronos = Instant::now();
// Step 1: initialize the population randomly within the solution space
let mut c = self.initialize(self.params, InitializationMode::RealUniform);
// Step 2 : Evaluate the fitness value of each candidate soluion
for genom in c.iter_mut() {
genom.fitness = Some(self.problem.objectivefunction(&mut genom.genes));
}
// Step 3 : Select the 4 best solutions
// 3.1 Sorting :
c.sort_by(Genome::cmp_genome);
// 3.2 update indexes
for i in 0..c.len() {
c[i].id = i;
}
// check
for g in c.iter() {
println!("id: {}, fit: {:?}", g.id, g.fitness);
}
// the main loop of EO
while iter < max_iter {
for i in 0..c.len() {
// space bound
for j in 0..dim {
if c[i].genes[j] < lb[j] {
c[i].genes[j] = lb[j];
}
if c[i].genes[j] > ub[j] {
c[i].genes[j] = ub[j];
}
}
// compute fitness for agents
fitness[i] = self.problem.objectivefunction(&mut c[i].genes); //fobj(&c[i]);
// check fitness with best
if fitness[i] < ceq1_fit {
ceq1_index = i;
ceq1_fit = fitness[i];
//copy_vector(&c[i].genes, &mut ceq1);
ceq1[..dim].clone_from_slice(&c[i].genes[..dim]);
} else if (fitness[i] < ceq2_fit) & (fitness[i] > ceq1_fit) {
//ceq2_index = i;
ceq2_fit = fitness[i];
//copy_vector(&c[i].genes, &mut ceq2);
ceq2[..dim].clone_from_slice(&c[i].genes[..dim]);
} else if (fitness[i] < ceq3_fit)
& (fitness[i] > ceq2_fit)
& (fitness[i] > ceq1_fit)
{
//ceq3_index = i;
ceq3_fit = fitness[i];
//copy_vector(&c[i].genes, &mut ceq3);
ceq3[..dim].clone_from_slice(&c[i].genes[..dim]);
} else if (fitness[i] < ceq4_fit)
& (fitness[i] > ceq3_fit)
& (fitness[i] > ceq2_fit)
& (fitness[i] > ceq1_fit)
{
//ceq4_index = i;
ceq4_fit = fitness[i];
//copy_vector(&c[i].genes, &mut ceq4);
ceq4[..dim].clone_from_slice(&c[i].genes[..dim]);
}
}
// copy the best 4 genomes
//copy_vector(&c[ceq1_index].genes, &mut ceq1);
//copy_vector(&c[ceq2_index].genes, &mut ceq2);
//copy_vector(&c[ceq3_index].genes, &mut ceq3);
//copy_vector(&c[ceq4_index].genes, &mut ceq4);
//ceq1_fit = fitness[ceq1_index];
//ceq2_fit = fitness[ceq2_index];
//ceq3_fit = fitness[ceq3_index];
//ceq4_fit = fitness[ceq4_index];
//-- Memory saving---
if iter == 0 {
//copy_vector(&fitness, &mut fit_old);
fit_old[..particles_no].clone_from_slice(&fitness[..particles_no]);
copy_matrix(&c, &mut c_old);
}
for i in 0..particles_no {
if fit_old[i] < fitness[i] {
fitness[i] = fit_old[i];
//copy_vector2genome(&c_old[i], &mut c[i]);
c[i].genes[..dim].clone_from_slice(&c_old[i][..dim]);
}
}
copy_matrix(&c, &mut c_old);
//copy_vector(&fitness, &mut fit_old);
fit_old[..particles_no].clone_from_slice(&fitness[..particles_no]);
// compute averaged candidate Ceq_ave
for i in 0..dim {
ceq_ave[i] = (ceq1[i] + ceq2[i] + ceq3[i] + ceq4[i]) / 4.0;
}
//Equilibrium pool
c_pool[0][..dim].clone_from_slice(&ceq1[..dim]);
c_pool[1][..dim].clone_from_slice(&ceq2[..dim]);
c_pool[2][..dim].clone_from_slice(&ceq3[..dim]);
c_pool[3][..dim].clone_from_slice(&ceq4[..dim]);
c_pool[4][..dim].clone_from_slice(&ceq_ave[..dim]);
// comput t using Eq 09
let tmpt = (iter / max_iter) as f64;
let t: f64 = (1.0 - tmpt).powf(a2 * tmpt);
// let chronos = Instant::now();
for i in 0..particles_no {
MEO::<'a, T>::randomize(&mut lambda); // lambda=rand(1,dim); lambda in Eq(11)
MEO::<'a, T>::randomize(&mut r); // r=rand(1,dim); r in Eq(11
//-------------------------------------------------------
// Ceq=C_pool(randi(size(C_pool,1)),:);
// random selection of one candidate from the pool
_index = interval.sample(&mut rng);
//copy_vector(&c_pool[_index], &mut ceq);
ceq[..dim].clone_from_slice(&c_pool[_index][..dim]);
//--------------------------------------------------------
// compute F using Eq(11)
for j in 0..dim {
f[j] = a1
* f64::signum(r[j] - 0.5)
* (f64::exp(-1.0 * lambda[j] * t) - 1.0);
}
// r1 and r2 to use them in Eq(15)
MEO::<'a, T>::randomize(&mut r1);
MEO::<'a, T>::randomize(&mut r2);
for j in 0..dim {
// Eq. 15
if r2[j] > gp {
_gcp = 0.5 * r1[j];
} else {
_gcp = 0.0f64;
}
// Eq. 14
_g0 = _gcp * (ceq[j] - lambda[j] * c[i].genes[j]);
// Eq 13
_g = _g0 * f[j];
// Eq. 16
c[i].genes[j] = ceq[j]
+ (c[i].genes[j] - ceq[j]) * f[j]
+ (_g / (lambda[j] * v)) * (1.0 - f[j]);
}
}
// let duration = chronos.elapsed();
// println!("seq--> End computation in : {:?}", duration);
convergence_curve[iter] = ceq1_fit;
iter += 1;
}
//return results
let duration = chronos.elapsed();
let result = OptimizationResult {
best_genome: Some(Genome::from(ceq1_index, &ceq1, ceq1_fit)),
best_fitness: Some(ceq1_fit),
convergence_trend: Some(convergence_curve),
computation_time: Some(duration),
err_report: None,
};
// copy result to EO struct
self.optimization_result = result.clone();
result
}
}
}
}