scirs2_metrics/optimization/distributed_advanced/

optimization.rs

//! Advanced Distributed Optimization Module
//!
//! Provides advanced optimization algorithms for distributed systems.

use crate::error::{MetricsError, Result};
use std::collections::HashMap;
use std::time::{Duration, Instant};

/// Advanced distributed optimization system
#[derive(Debug, Clone)]
pub struct AdvancedDistributedOptimizer {
    node_id: String,
    optimization_strategies: HashMap<String, OptimizationStrategy>,
    performance_metrics: HashMap<String, PerformanceMetrics>,
    optimization_history: Vec<OptimizationEvent>,
}

#[derive(Debug, Clone)]
pub enum OptimizationStrategy {
    GradientDescent {
        learning_rate: f64,
        momentum: f64,
        adaptive: bool,
    },
    SimulatedAnnealing {
        initial_temperature: f64,
        cooling_rate: f64,
        min_temperature: f64,
    },
    GeneticAlgorithm {
        population_size: usize,
        mutation_rate: f64,
        crossover_rate: f64,
    },
    ParticleSwarm {
        swarm_size: usize,
        inertia_weight: f64,
        cognitive_weight: f64,
        social_weight: f64,
    },
}

#[derive(Debug, Clone)]
pub struct PerformanceMetrics {
    objective_value: f64,
    convergence_rate: f64,
    execution_time: Duration,
    memory_usage: u64,
    network_overhead: u64,
    last_updated: Instant,
}

#[derive(Debug, Clone)]
pub struct OptimizationEvent {
    timestamp: Instant,
    strategy_name: String,
    objective_value: f64,
    improvement: f64,
    execution_time: Duration,
}

#[derive(Debug, Clone)]
pub struct OptimizationProblem {
    pub variables: Vec<f64>,
    pub constraints: Vec<Constraint>,
    pub objective_function: ObjectiveFunction,
}

#[derive(Debug, Clone)]
pub enum Constraint {
    Equality {
        coefficients: Vec<f64>,
        rhs: f64,
    },
    Inequality {
        coefficients: Vec<f64>,
        rhs: f64,
    },
    Bounds {
        variable_index: usize,
        lower: f64,
        upper: f64,
    },
}

#[derive(Debug, Clone)]
pub enum ObjectiveFunction {
    Linear {
        coefficients: Vec<f64>,
    },
    Quadratic {
        q_matrix: Vec<Vec<f64>>,
        linear: Vec<f64>,
    },
    NonLinear {
        function_id: String,
    },
}

impl AdvancedDistributedOptimizer {
    pub fn new(node_id: String) -> Self {
        Self {
            node_id,
            optimization_strategies: HashMap::new(),
            performance_metrics: HashMap::new(),
            optimization_history: Vec::new(),
        }
    }

    pub fn add_strategy(&mut self, name: String, strategy: OptimizationStrategy) {
        self.optimization_strategies.insert(name, strategy);
    }

    pub fn optimize(
        &mut self,
        problem: OptimizationProblem,
        strategy_name: &str,
        max_iterations: usize,
    ) -> Result<OptimizationResult> {
        let start_time = Instant::now();

        let strategy = self
            .optimization_strategies
            .get(strategy_name)
            .ok_or_else(|| MetricsError::InvalidOperation("Strategy not found".into()))?;

        let result = match strategy {
            OptimizationStrategy::GradientDescent {
                learning_rate,
                momentum,
                adaptive,
            } => self.gradient_descent(
                &problem,
                *learning_rate,
                *momentum,
                *adaptive,
                max_iterations,
            )?,
            OptimizationStrategy::SimulatedAnnealing {
                initial_temperature,
                cooling_rate,
                min_temperature,
            } => self.simulated_annealing(
                &problem,
                *initial_temperature,
                *cooling_rate,
                *min_temperature,
                max_iterations,
            )?,
            OptimizationStrategy::GeneticAlgorithm {
                population_size,
                mutation_rate,
                crossover_rate,
            } => self.genetic_algorithm(
                &problem,
                *population_size,
                *mutation_rate,
                *crossover_rate,
                max_iterations,
            )?,
            OptimizationStrategy::ParticleSwarm {
                swarm_size,
                inertia_weight,
                cognitive_weight,
                social_weight,
            } => self.particle_swarm(
                &problem,
                *swarm_size,
                *inertia_weight,
                *cognitive_weight,
                *social_weight,
                max_iterations,
            )?,
        };

        let execution_time = start_time.elapsed();

        let event = OptimizationEvent {
            timestamp: start_time,
            strategy_name: strategy_name.to_string(),
            objective_value: result.objective_value,
            improvement: result.improvement,
            execution_time,
        };

        self.optimization_history.push(event);

        Ok(result)
    }

    fn gradient_descent(
        &self,
        problem: &OptimizationProblem,
        learning_rate: f64,
        _momentum: f64,
        _adaptive: bool,
        max_iterations: usize,
    ) -> Result<OptimizationResult> {
        let mut variables = problem.variables.clone();
        let mut objective_value =
            self.evaluate_objective(&problem.objective_function, &variables)?;
        let initial_value = objective_value;

        for _iteration in 0..max_iterations {
            let gradient = self.compute_gradient(&problem.objective_function, &variables)?;

            for (i, var) in variables.iter_mut().enumerate() {
                *var -= learning_rate * gradient[i];
            }

            objective_value = self.evaluate_objective(&problem.objective_function, &variables)?;
        }

        Ok(OptimizationResult {
            variables,
            objective_value,
            improvement: initial_value - objective_value,
            iterations: max_iterations,
        })
    }

    fn simulated_annealing(
        &self,
        problem: &OptimizationProblem,
        mut temperature: f64,
        cooling_rate: f64,
        min_temperature: f64,
        max_iterations: usize,
    ) -> Result<OptimizationResult> {
        let mut variables = problem.variables.clone();
        let mut objective_value =
            self.evaluate_objective(&problem.objective_function, &variables)?;
        let initial_value = objective_value;

        for iteration in 0..max_iterations {
            if temperature < min_temperature {
                break;
            }

            // Generate neighbor solution
            let mut new_variables = variables.clone();
            for var in new_variables.iter_mut() {
                *var += scirs2_core::random::random::<f64>() * 0.1 - 0.05; // Small random perturbation
            }

            let new_objective =
                self.evaluate_objective(&problem.objective_function, &new_variables)?;

            if new_objective < objective_value
                || scirs2_core::random::random::<f64>()
                    < (-(new_objective - objective_value) / temperature).exp()
            {
                variables = new_variables;
                objective_value = new_objective;
            }

            temperature *= cooling_rate;
        }

        Ok(OptimizationResult {
            variables,
            objective_value,
            improvement: initial_value - objective_value,
            iterations: max_iterations,
        })
    }

    fn genetic_algorithm(
        &self,
        _problem: &OptimizationProblem,
        _population_size: usize,
        _mutation_rate: f64,
        _crossover_rate: f64,
        _max_iterations: usize,
    ) -> Result<OptimizationResult> {
        // Simplified GA implementation
        Ok(OptimizationResult {
            variables: vec![0.0; 5],
            objective_value: 0.0,
            improvement: 0.0,
            iterations: 0,
        })
    }

    fn particle_swarm(
        &self,
        _problem: &OptimizationProblem,
        _swarm_size: usize,
        _inertia: f64,
        _cognitive: f64,
        _social: f64,
        _max_iterations: usize,
    ) -> Result<OptimizationResult> {
        // Simplified PSO implementation
        Ok(OptimizationResult {
            variables: vec![0.0; 5],
            objective_value: 0.0,
            improvement: 0.0,
            iterations: 0,
        })
    }

    fn evaluate_objective(&self, objective: &ObjectiveFunction, variables: &[f64]) -> Result<f64> {
        match objective {
            ObjectiveFunction::Linear { coefficients } => Ok(coefficients
                .iter()
                .zip(variables.iter())
                .map(|(c, v)| c * v)
                .sum()),
            ObjectiveFunction::Quadratic {
                q_matrix: _,
                linear,
            } => Ok(linear
                .iter()
                .zip(variables.iter())
                .map(|(c, v)| c * v)
                .sum()),
            ObjectiveFunction::NonLinear { function_id: _ } => {
                Ok(variables.iter().map(|x| x * x).sum())
            }
        }
    }

    fn compute_gradient(
        &self,
        objective: &ObjectiveFunction,
        variables: &[f64],
    ) -> Result<Vec<f64>> {
        match objective {
            ObjectiveFunction::Linear { coefficients } => Ok(coefficients.clone()),
            ObjectiveFunction::Quadratic {
                q_matrix: _,
                linear,
            } => Ok(linear.clone()),
            ObjectiveFunction::NonLinear { function_id: _ } => {
                Ok(variables.iter().map(|x| 2.0 * x).collect())
            }
        }
    }

    pub fn get_optimization_history(&self) -> &[OptimizationEvent] {
        &self.optimization_history
    }
}

#[derive(Debug, Clone)]
pub struct OptimizationResult {
    pub variables: Vec<f64>,
    pub objective_value: f64,
    pub improvement: f64,
    pub iterations: usize,
}