Crate autoeq_de

Expand description

§AutoEQ Differential Evolution

This crate provides a pure Rust implementation of Differential Evolution (DE) global optimization algorithm with advanced features.

§Features

Pure Rust Implementation: No external dependencies for core optimization
Multiple DE Strategies: Various mutation and crossover strategies
Constraint Handling: Linear and nonlinear constraint support
Adaptive Parameters: Self-adjusting F and CR parameters
Evaluation Recording: Track optimization progress and convergence
Visualization Tools: Plot test functions and optimization traces

§Optimization Strategies

§Mutation Strategies

DE/rand/1: x_trial = x_r1 + F * (x_r2 - x_r3)
DE/best/1: x_trial = x_best + F * (x_r1 - x_r2)
DE/current-to-best/1: Combines current and best vectors
DE/rand/2: Uses five random vectors for mutation

§Crossover Strategies

Binomial: Random parameter-wise crossover
Exponential: Sequential parameter crossover

§Usage

use autoeq_de::{differential_evolution, DEConfig, Strategy, Mutation};
use ndarray::Array1;

// Example objective function (Rosenbrock)
let objective = |x: &Array1<f64>| {
    let a = 1.0;
    let b = 100.0;
    (a - x[0]).powi(2) + b * (x[1] - x[0].powi(2)).powi(2)
};

// Define bounds for 2D problem
let bounds = vec![(-5.0, 5.0), (-5.0, 5.0)];

let config = DEConfig {
    strategy: Strategy::Rand1Bin,
    maxiter: 1000,
    popsize: 50,
    mutation: Mutation::Factor(0.8),
    recombination: 0.9,
    seed: Some(42),
    ..Default::default()
};

let result = differential_evolution(&objective, &bounds, config);
println!("Best solution: {:?}", result.x);
println!("Best fitness: {}", result.fun);

§Constraint Support

§Linear Constraints

use autoeq_de::{LinearConstraintHelper, DEConfig};
use ndarray::{Array1, Array2};

// Linear constraint: x1 + x2 <= 1.0
let constraint = LinearConstraintHelper {
    a: Array2::from_shape_vec((1, 2), vec![1.0, 1.0]).unwrap(),
    lb: Array1::from_vec(vec![f64::NEG_INFINITY]),
    ub: Array1::from_vec(vec![1.0]),
};

// Apply to configuration with penalty weight
let mut config = DEConfig::default();
constraint.apply_to(&mut config, 1000.0); // penalty weight

§Nonlinear Constraints

let nonlinear_constraint = |x: &[f64]| -> f64 {
    x[0].powi(2) + x[1].powi(2) - 1.0 // circle constraint
};

§Visualization

The crate includes a plot_functions binary for visualizing test functions and optimization traces:

# Plot test functions as contour plots
cargo run --bin plot_functions -- --functions rosenbrock,sphere

# Show optimization traces from CSV files
cargo run --bin plot_functions -- --csv-dir traces/ --show-traces

§Integration

This crate is part of the AutoEQ ecosystem:

Used by autoeq for filter parameter optimization
Integrates with autoeq-testfunctions for validation
Works with autoeq-iir for audio filter optimization

§Examples

The crate includes several example programs demonstrating different DE capabilities:

basic_de: Simple unconstrained optimization
linear_constraints: Linear constraint handling
nonlinear_constraints: Complex constraint optimization

§References

§License

GPL-3.0-or-later

§References

[https://en.wikipedia.org/wiki/Differential_evolution]
[https://www.sfu.ca/~ssurjano/optimization.html]
[https://ch.mathworks.com/matlabcentral/fileexchange/23147-test-functions-for-global-optimization-algorithms]

§Scipy

@ARTICLE{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlays}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}

§A large review in 2020 of the DE space

@article{BILAL2020103479,
    title = {Differential Evolution: A review of more than two decades of research},
    journal = {Engineering Applications of Artificial Intelligence},
    volume = {90},
    pages = {103479},
    year = {2020},
    issn = {0952-1976},
    doi = {https://doi.org/10.1016/j.engappai.2020.103479},
    url = {https://www.sciencedirect.com/science/article/pii/S095219762030004X},
    author = { Bilal and Millie Pant and Hira Zaheer and Laura Garcia-Hernandez and Ajith Abraham},
    keywords = {Meta-heuristics, Differential evolution, Mutation, Crossover, Selection},
    abstract = {Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most frequently used algorithms for solving complex optimization problems. Its flexibility and versatility have prompted several customized variants of DE for solving a variety of real life and test problems. The present study, surveys the near 25 years of existence of DE. In this extensive survey, 283 research articles have been covered and the journey of DE is shown through its basic aspects like population generation, mutation schemes, crossover schemes, variation in parameters and hybridized variants along with various successful applications of DE. This study also provides some key bibliometric indicators like highly cited papers having citations more than 500, publication trend since 1996, journal citations etc. The main aim of the present document is to serve as an extended summary of 25 years of existence of DE, intended for dissemination to interested parties. It is expected that the present survey would generate interest among the new users towards the philosophy of DE and would also guide the experience researchers.}
}

§JADE

@ARTICLE{5208221,
  author={Zhang, Jingqiao and Sanderson, Arthur C.},
  journal={IEEE Transactions on Evolutionary Computation},
  title={JADE: Adaptive Differential Evolution With Optional External Archive},
  year={2009},
  volume={13},
  number={5},
  pages={945-958},
  keywords={Genetic mutations;Programmable control;Adaptive control;Convergence;Automatic control;Evolutionary computation;Feedback;Robustness;Particle swarm optimization;Performance analysis;Adaptive parameter control;differential evolution;evolutionary optimization;external archive},
  doi={10.1109/TEVC.2009.2014613}}

Re-exports§

pub use differential_evolution::differential_evolution;
pub use parallel_eval::ParallelConfig;
pub use recorder::OptimizationRecord;
pub use recorder::OptimizationRecorder;
pub use run_recorded::run_recorded_differential_evolution;

Modules§

apply_integrality
apply_wls
crossover_binomial
crossover_exponential
differential_evolution
distinct_indices
function_registry
impl_helpers
init_latin_hypercube
init_random
metadata: Tests for metadata-driven optimization examples
mutant_adaptive
mutant_best1
mutant_best2
mutant_current_to_best1
mutant_rand1
mutant_rand2
mutant_rand_to_best1
parallel_eval
recorder
run_recorded: Recording wrapper for differential evolution for testing purposes
stack_linear_penalty

Structs§

AdaptiveConfig: Adaptive differential evolution configuration
DEConfig: Configuration for the Differential Evolution optimizer
DEConfigBuilder: Fluent builder for DEConfig for ergonomic configuration.
DEIntermediate: Information passed to callback after each generation
DEReport: Result/Report of a DE optimization run
DifferentialEvolution: Differential Evolution optimizer
LinearConstraintHelper: SciPy-like linear constraint helper: lb <= A x <= ub
LinearPenalty: Linear penalty specification: lb <= A x <= ub (component-wise)
NonlinearConstraintHelper: SciPy-like nonlinear constraint helper: vector-valued fun(x) with lb <= fun(x) <= ub
PolishConfig: Polishing configuration using NLopt local optimizer within bounds

Enums§

CallbackAction: Action returned by callback
Crossover: Crossover type
Init: Initialization scheme for the population
Mutation: Mutation setting: either a fixed factor, a uniform range (dithering), or adaptive
Strategy: Differential Evolution strategy
Updating: Whether best updates during a generation (we use Deferred only)

Type Aliases§

CallbackFn: Callback function type
PenaltyTuple: Penalty tuple type (function, weight)
ScalarConstraintFn: Scalar constraint function type
VectorConstraintFn: Vector constraint function type

Crate autoeq_de

Crate autoeq_de Copy item path

§AutoEQ Differential Evolution

§Features

§Optimization Strategies

§Mutation Strategies

§Crossover Strategies

§Usage

§Constraint Support

§Linear Constraints

§Nonlinear Constraints

§Visualization

§Integration

§Examples

§References

§License

§References

§Scipy

§A large review in 2020 of the DE space

§JADE

Re-exports§

Modules§

Structs§

Enums§

Type Aliases§

Crate autoeq_de