Refactor objectives to avoid NSGA-II

docs/src/internals/algorithms/heuristics.md

@@ -53,8 +53,7 @@ Currently available heuristics:
 Each heuristic accepts one of solutions from the population (not necessary the best known) and tries to improve it (or diversify).
 During one of refinement iterations, many solutions are picked at the same time and many heuristics are called then in parallel.
 Such incremental step is called a `generation`. Once it is completed, all found solutions are introduced to the population,
-which decides how to store them. With elitism/rosomaxa population types, to order solution from Pareto optimal front,
-`Non-Dominated Sorting Genetic Algorithm II` is used.
+which decides how to store them.
 
 [Related documentation](https://docs.rs/vrp-core/latest/vrp_core/solver/search/index.html)
 

docs/src/internals/algorithms/index.md

@@ -14,7 +14,6 @@ An incomplete list of important references:
 - Thibaut Vidal: `Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood`
 - Richard F. Hartl, Thibaut Vidal: `Workload Equity in Vehicle Routing Problems: A Survey and Analysis`
 
-- K. Deb; A. Pratap; S. Agarwal; T. Meyarivan: `A fast and elitist multiobjective genetic algorithm: NSGA-II`
 - Damminda Alahakoon, Saman K Halgamuge, Srinivasan Bala: `Dynamic self-organizing maps with controlled growth for knowledge discovery`
 - Daniel J. Russo, Benjamin Van Roy, Abbas Kazerouni, Ian Osband and Zheng Wen: `A Tutorial on Thompson Sampling` https://web.stanford.edu/~bvr/pubs/TS_Tutorial.pdf
 

docs/src/internals/overview.md

@@ -113,8 +113,7 @@ right side
 
    * objective
     * kind
-     *_ multi (NSGA-II)
-     *_ hierarchical
+     *_ lexicographical
     * types
      *_ minimize/maximize routes
      *_ minimize cost

experiments/heuristic-research/src/solver/mod.rs

@@ -23,7 +23,7 @@ fn get_population<O, S>(
 ) -> Box<dyn HeuristicPopulation<Objective = O, Individual = S> + Send + Sync>
 where
     O: HeuristicObjective<Solution = S> + Shuffled + 'static,
-    S: HeuristicSolution + RosomaxaWeighted + DominanceOrdered + 'static,
+    S: HeuristicSolution + RosomaxaWeighted + 'static,
 {
     match population_type {
         "greedy" => Box::new(ProxyPopulation::new(Greedy::new(objective, 1, None))),

experiments/heuristic-research/src/solver/proxies.rs

@@ -1,6 +1,6 @@
 use crate::*;
 use rosomaxa::example::VectorSolution;
-use rosomaxa::population::{DominanceOrdered, RosomaxaWeighted, Shuffled};
+use rosomaxa::population::{RosomaxaWeighted, Shuffled};
 use rosomaxa::prelude::*;
 use std::any::TypeId;
 use std::cmp::Ordering;
@@ -46,7 +46,7 @@ impl<'a> TryFrom<&'a str> for ExperimentData {
 
 impl<S> From<&S> for ObservationData
 where
-    S: HeuristicSolution + RosomaxaWeighted + DominanceOrdered + 'static,
+    S: HeuristicSolution + RosomaxaWeighted + 'static,
 {
     fn from(solution: &S) -> Self {
         if TypeId::of::<S>() == TypeId::of::<VectorSolution>() {
@@ -85,7 +85,7 @@ pub struct ProxyPopulation<P, O, S>
 where
     P: HeuristicPopulation<Objective = O, Individual = S> + 'static,
     O: HeuristicObjective<Solution = S> + Shuffled + 'static,
-    S: HeuristicSolution + RosomaxaWeighted + DominanceOrdered + 'static,
+    S: HeuristicSolution + RosomaxaWeighted + 'static,
 {
     generation: usize,
     inner: P,
@@ -95,7 +95,7 @@ impl<P, O, S> ProxyPopulation<P, O, S>
 where
     P: HeuristicPopulation<Objective = O, Individual = S> + 'static,
     O: HeuristicObjective<Solution = S> + Shuffled + 'static,
-    S: HeuristicSolution + RosomaxaWeighted + DominanceOrdered + 'static,
+    S: HeuristicSolution + RosomaxaWeighted + 'static,
 {
     /// Creates a new instance of `ProxyPopulation`.
     pub fn new(inner: P) -> Self {
@@ -112,7 +112,7 @@ impl<P, O, S> HeuristicPopulation for ProxyPopulation<P, O, S>
 where
     P: HeuristicPopulation<Objective = O, Individual = S> + 'static,
     O: HeuristicObjective<Solution = S> + Shuffled,
-    S: HeuristicSolution + RosomaxaWeighted + DominanceOrdered,
+    S: HeuristicSolution + RosomaxaWeighted,
 {
     type Objective = O;
     type Individual = S;
@@ -153,7 +153,7 @@ where
         }))
     }
 
-    fn ranked<'a>(&'a self) -> Box<dyn Iterator<Item = (&Self::Individual, usize)> + 'a> {
+    fn ranked<'a>(&'a self) -> Box<dyn Iterator<Item = &Self::Individual> + 'a> {
         self.inner.ranked()
     }
 
@@ -174,7 +174,7 @@ impl<P, O, S> Display for ProxyPopulation<P, O, S>
 where
     P: HeuristicPopulation<Objective = O, Individual = S> + 'static,
     O: HeuristicObjective<Solution = S> + Shuffled + 'static,
-    S: HeuristicSolution + RosomaxaWeighted + DominanceOrdered + 'static,
+    S: HeuristicSolution + RosomaxaWeighted + 'static,
 {
     fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
         self.inner.fmt(f)

experiments/heuristic-research/src/solver/state.rs

@@ -1,6 +1,6 @@
 use crate::{Coordinate, MatrixData};
 use rosomaxa::algorithms::gsom::NetworkState;
-use rosomaxa::population::{DominanceOrdered, Rosomaxa, RosomaxaWeighted, Shuffled};
+use rosomaxa::population::{Rosomaxa, RosomaxaWeighted, Shuffled};
 use rosomaxa::prelude::*;
 use serde::{Deserialize, Serialize};
 use std::any::TypeId;
@@ -44,10 +44,10 @@ pub fn get_population_state<P, O, S>(population: &P) -> PopulationState
 where
     P: HeuristicPopulation<Objective = O, Individual = S> + 'static,
     O: HeuristicObjective<Solution = S> + Shuffled + 'static,
-    S: HeuristicSolution + RosomaxaWeighted + DominanceOrdered + 'static,
+    S: HeuristicSolution + RosomaxaWeighted + 'static,
 {
     let fitness_values =
-        population.ranked().next().map(|(solution, _)| solution.fitness().collect::<Vec<_>>()).unwrap_or_default();
+        population.ranked().next().map(|solution| solution.fitness().collect::<Vec<_>>()).unwrap_or_default();
 
     if TypeId::of::<P>() == TypeId::of::<Rosomaxa<O, S>>() {
         let rosomaxa = unsafe { std::mem::transmute::<&P, &Rosomaxa<O, S>>(population) };

rosomaxa/src/algorithms/mod.rs

@@ -2,5 +2,4 @@
 
 pub mod gsom;
 pub mod math;
-pub mod nsga2;
 pub mod rl;