//! Advanced Mode Coordinator
//!
//! This module implements the cutting-edge Advanced Mode for optimization,
//! which intelligently coordinates between multiple advanced optimization strategies:
//! - Neural Architecture Search (NAS) systems
//! - Quantum-inspired optimization
//! - Neuromorphic computing approaches
//! - Meta-learning optimizers
//! - Real-time adaptive strategy switching
//! - Cross-modal optimization fusion
//!
//! The Advanced Coordinator represents the pinnacle of optimization technology,
//! combining insights from quantum mechanics, neuroscience, artificial intelligence,
//! and adaptive systems theory.

use crate::error::OptimizeError;
use crate::error::OptimizeResult as Result;
use crate::learned_optimizers::{
    LearnedOptimizationConfig,
    LearnedOptimizer,
    MetaLearningOptimizer,
    OptimizationProblem,
    // Unused import: TrainingTask,
};
use crate::neuromorphic::{BasicNeuromorphicOptimizer, NeuromorphicConfig, NeuromorphicOptimizer};
use crate::quantum_inspired::{QuantumInspiredOptimizer, QuantumOptimizationStats};
use crate::result::OptimizeResults;
use scirs2_core::ndarray::{Array1, Array2, ArrayView1};
use scirs2_core::random::prelude::*;
use std::collections::{HashMap, VecDeque};
use std::time::{Duration, Instant};

/// Advanced coordination strategy for Advanced mode
#[derive(Debug, Clone, Copy, PartialEq)]
pub enum AdvancedStrategy {
    /// Quantum-Neural Fusion: Combines quantum superposition with neural adaptation
    QuantumNeuralFusion,
    /// Neuromorphic-Quantum Hybrid: Spiking networks with quantum tunneling
    NeuromorphicQuantumHybrid,
    /// Meta-Learning Quantum: Quantum-enhanced meta-learning optimization
    MetaLearningQuantum,
    /// Adaptive Strategy Selection: Dynamic strategy switching based on performance
    AdaptiveSelection,
    /// Full Advanced: All strategies working in parallel with intelligent coordination
    FullAdvanced,
}

/// Configuration for Advanced Mode
#[derive(Debug, Clone)]
pub struct AdvancedConfig {
    /// Primary coordination strategy
    pub strategy: AdvancedStrategy,
    /// Maximum optimization iterations
    pub max_nit: usize,
    /// Function evaluation budget
    pub max_evaluations: usize,
    /// Convergence tolerance
    pub tolerance: f64,
    /// Strategy switching threshold (performance improvement required)
    pub switching_threshold: f64,
    /// Time budget for optimization (seconds)
    pub time_budget: Option<Duration>,
    /// Enable quantum components
    pub enable_quantum: bool,
    /// Enable neuromorphic components
    pub enable_neuromorphic: bool,
    /// Enable meta-learning components
    pub enable_meta_learning: bool,
    /// Number of parallel optimization threads
    pub parallel_threads: usize,
    /// Cross-modal fusion strength (0.0 to 1.0)
    pub fusion_strength: f64,
    /// Adaptive learning rate for strategy coordination
    pub coordination_learning_rate: f64,
    /// Memory size for strategy performance tracking
    pub performance_memory_size: usize,
}

impl Default for AdvancedConfig {
    fn default() -> Self {
        Self {
            strategy: AdvancedStrategy::FullAdvanced,
            max_nit: 10000,
            max_evaluations: 100000,
            tolerance: 1e-12,
            switching_threshold: 0.01,
            time_budget: Some(Duration::from_secs(300)), // 5 minutes
            enable_quantum: true,
            enable_neuromorphic: true,
            enable_meta_learning: true,
            parallel_threads: 4,
            fusion_strength: 0.7,
            coordination_learning_rate: 0.01,
            performance_memory_size: 1000,
        }
    }
}

/// Real-time performance statistics for strategy coordination
#[derive(Debug, Clone)]
pub struct StrategyPerformance {
    /// Strategy identifier
    pub strategy_id: String,
    /// Convergence rate (improvement per iteration)
    pub convergence_rate: f64,
    /// Function evaluations used
    pub evaluations_used: usize,
    /// Success rate (fraction of problems solved)
    pub success_rate: f64,
    /// Average time per iteration
    pub avg_iteration_time: Duration,
    /// Best objective value achieved
    pub best_objective: f64,
    /// Exploration efficiency
    pub exploration_efficiency: f64,
    /// Exploitation efficiency  
    pub exploitation_efficiency: f64,
    /// Adaptation speed
    pub adaptation_speed: f64,
}

impl Default for StrategyPerformance {
    fn default() -> Self {
        Self {
            strategy_id: String::new(),
            convergence_rate: 0.0,
            evaluations_used: 0,
            success_rate: 0.0,
            avg_iteration_time: Duration::from_millis(1),
            best_objective: f64::INFINITY,
            exploration_efficiency: 0.5,
            exploitation_efficiency: 0.5,
            adaptation_speed: 0.1,
        }
    }
}

/// Advanced optimization state
#[derive(Debug, Clone)]
pub struct AdvancedState {
    /// Current best solution across all strategies
    pub global_best_solution: Array1<f64>,
    /// Current best objective value
    pub global_best_objective: f64,
    /// Total function evaluations used
    pub total_evaluations: usize,
    /// Current iteration
    pub current_iteration: usize,
    /// Active strategy performances
    pub strategy_performances: HashMap<String, StrategyPerformance>,
    /// Cross-modal knowledge transfer matrix
    pub knowledge_transfer_matrix: Array2<f64>,
    /// Strategy confidence scores
    pub strategy_confidences: HashMap<String, f64>,
    /// Fusion weights for multi-strategy coordination
    pub fusion_weights: Array1<f64>,
    /// Problem characteristics learned so far
    pub problem_characteristics: HashMap<String, f64>,
    /// Performance history for adaptive learning
    pub performance_history: VecDeque<f64>,
    /// Start time for time budget tracking
    pub start_time: Instant,
}

impl AdvancedState {
    fn new(num_params: usize, num_strategies: usize) -> Self {
        Self {
            global_best_solution: Array1::zeros(num_params),
            global_best_objective: f64::INFINITY,
            total_evaluations: 0,
            current_iteration: 0,
            strategy_performances: HashMap::new(),
            knowledge_transfer_matrix: Array2::zeros((num_strategies, num_strategies)),
            strategy_confidences: HashMap::new(),
            fusion_weights: Array1::from_elem(num_strategies, 1.0 / num_strategies as f64),
            problem_characteristics: HashMap::new(),
            performance_history: VecDeque::with_capacity(1000),
            start_time: Instant::now(),
        }
    }
}

/// Main Advanced Coordinator
#[derive(Debug)]
pub struct AdvancedCoordinator {
    /// Configuration
    pub config: AdvancedConfig,
    /// Current optimization state
    pub state: AdvancedState,
    /// Quantum optimizer instance
    pub quantum_optimizer: Option<QuantumInspiredOptimizer>,
    /// Neuromorphic optimizer instance
    pub neuromorphic_optimizer: Option<BasicNeuromorphicOptimizer>,
    /// Meta-learning optimizer instance
    pub meta_learning_optimizer: Option<MetaLearningOptimizer>,
    /// Strategy performance predictor
    pub performance_predictor: PerformancePredictor,
    /// Cross-modal fusion engine
    pub fusion_engine: CrossModalFusionEngine,
    /// Adaptive strategy selector
    pub strategy_selector: AdaptiveStrategySelector,
}

impl AdvancedCoordinator {
    /// Create new Advanced Coordinator
    pub fn new(config: AdvancedConfig, initial_params: &ArrayView1<f64>) -> Self {
        let num_params = initial_params.len();
        let num_strategies = 3; // quantum, neuromorphic, meta-learning
        let state = AdvancedState::new(num_params, num_strategies);

        // Initialize optimizers based on configuration
        let quantum_optimizer = if config.enable_quantum {
            Some(QuantumInspiredOptimizer::new(
                initial_params,
                config.max_nit,
                32, // quantum states
            ))
        } else {
            None
        };

        let neuromorphic_optimizer = if config.enable_neuromorphic {
            let neuro_config = NeuromorphicConfig {
                total_time: 10.0,
                num_neurons: 200,
                ..Default::default()
            };
            Some(BasicNeuromorphicOptimizer::new(neuro_config, num_params))
        } else {
            None
        };

        let meta_learning_optimizer = if config.enable_meta_learning {
            let meta_config = LearnedOptimizationConfig {
                meta_training_episodes: 1000,
                use_transformer: true,
                hidden_size: 512,
                ..Default::default()
            };
            Some(MetaLearningOptimizer::new(meta_config))
        } else {
            None
        };

        Self {
            config,
            state,
            quantum_optimizer,
            neuromorphic_optimizer,
            meta_learning_optimizer,
            performance_predictor: PerformancePredictor::new(),
            fusion_engine: CrossModalFusionEngine::new(num_params),
            strategy_selector: AdaptiveStrategySelector::new(),
        }
    }

    /// Execute Advanced optimization
    pub fn optimize<F>(&mut self, objective: F) -> Result<OptimizeResults<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64 + Send + Sync + Clone,
    {
        self.state.start_time = Instant::now();
        let mut best_result = None;
        let mut consecutive_no_improvement = 0;

        for iteration in 0..self.config.max_nit {
            self.state.current_iteration = iteration;

            // Check time budget
            if let Some(budget) = self.config.time_budget {
                if self.state.start_time.elapsed() > budget {
                    break;
                }
            }

            // Check evaluation budget
            if self.state.total_evaluations >= self.config.max_evaluations {
                break;
            }

            // Execute current strategy
            let iteration_result = match self.config.strategy {
                AdvancedStrategy::QuantumNeuralFusion => {
                    self.execute_quantum_neural_fusion(&objective)?
                }
                AdvancedStrategy::NeuromorphicQuantumHybrid => {
                    self.execute_neuromorphic_quantum_hybrid(&objective)?
                }
                AdvancedStrategy::MetaLearningQuantum => {
                    self.execute_meta_learning_quantum(&objective)?
                }
                AdvancedStrategy::AdaptiveSelection => {
                    self.execute_adaptive_selection(&objective)?
                }
                AdvancedStrategy::FullAdvanced => self.execute_full_advanced(&objective)?,
            };

            // Update global best
            if iteration_result.fun < self.state.global_best_objective {
                self.state.global_best_objective = iteration_result.fun;
                self.state.global_best_solution = iteration_result.x.clone();
                consecutive_no_improvement = 0;
                best_result = Some(iteration_result.clone());
            } else {
                consecutive_no_improvement += 1;
            }

            // Update performance tracking
            self.update_performance_tracking(iteration_result.fun)?;

            // Adaptive strategy switching
            if consecutive_no_improvement > 50 {
                self.adapt_strategy()?;
                consecutive_no_improvement = 0;
            }

            // Convergence check
            if self.state.global_best_objective < self.config.tolerance {
                break;
            }

            // Cross-modal knowledge transfer
            if iteration % 25 == 0 {
                self.perform_knowledge_transfer()?;
            }
        }

        let final_result = best_result.unwrap_or_else(|| OptimizeResults::<f64> {
            x: self.state.global_best_solution.clone(),
            fun: self.state.global_best_objective,
            success: self.state.global_best_objective < f64::INFINITY,
            nit: self.state.current_iteration,
            nfev: self.state.total_evaluations,
            njev: 0,
            nhev: 0,
            maxcv: 0,
            status: 0,
            jac: None,
            hess: None,
            constr: None,
            message: "Advanced optimization completed".to_string(),
        });

        Ok(final_result)
    }

    /// Execute Quantum-Neural Fusion strategy
    fn execute_quantum_neural_fusion<F>(&mut self, objective: &F) -> Result<OptimizeResults<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        if let (Some(quantum_opt), Some(neuro_opt)) = (
            self.quantum_optimizer.as_mut(),
            self.neuromorphic_optimizer.as_mut(),
        ) {
            // Quantum exploration phase
            let quantum_candidate = quantum_opt.quantum_state.measure();
            let quantum_obj = objective(&quantum_candidate.view());
            self.state.total_evaluations += 1;

            // Neural adaptation phase
            neuro_opt
                .network_mut()
                .encode_parameters(&quantum_candidate.view());
            let neural_result = neuro_opt.optimize(objective, &quantum_candidate.view())?;
            self.state.total_evaluations += neural_result.nit;

            // Fusion of results
            let fused_solution = self.fusion_engine.fuse_solutions(
                &quantum_candidate.view(),
                &neural_result.x.view(),
                self.config.fusion_strength,
            )?;

            let fused_objective = objective(&fused_solution.view());
            self.state.total_evaluations += 1;

            Ok(OptimizeResults::<f64> {
                x: fused_solution,
                fun: fused_objective,
                success: fused_objective < f64::INFINITY,
                nit: 1,
                nfev: 1,
                njev: 0,
                nhev: 0,
                maxcv: 0,
                status: 0,
                jac: None,
                hess: None,
                constr: None,
                message: "Quantum-Neural fusion completed".to_string(),
            })
        } else {
            Err(OptimizeError::InitializationError(
                "Required optimizers not available".to_string(),
            ))
        }
    }

    /// Execute Neuromorphic-Quantum Hybrid strategy
    fn execute_neuromorphic_quantum_hybrid<F>(
        &mut self,
        objective: &F,
    ) -> Result<OptimizeResults<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        if let (Some(quantum_opt), Some(neuro_opt)) = (
            self.quantum_optimizer.as_mut(),
            self.neuromorphic_optimizer.as_mut(),
        ) {
            // Neuromorphic spike-based exploration
            let neural_candidate = neuro_opt.network().decode_parameters();
            let neural_obj = objective(&neural_candidate.view());
            self.state.total_evaluations += 1;

            // Quantum tunneling for local minima escape
            if neural_obj > self.state.global_best_objective * 1.1 {
                quantum_opt.quantum_state.quantum_tunnel(
                    5.0, // barrier height
                    0.3, // tunnel probability
                )?;
            }

            // Hybrid evolution
            let quantum_candidate = quantum_opt.quantum_state.measure();
            let quantum_obj = objective(&quantum_candidate.view());
            self.state.total_evaluations += 1;

            // Select best candidate
            let (best_solution, best_obj) = if quantum_obj < neural_obj {
                (quantum_candidate, quantum_obj)
            } else {
                (neural_candidate, neural_obj)
            };

            Ok(OptimizeResults::<f64> {
                x: best_solution,
                fun: best_obj,
                success: best_obj < f64::INFINITY,
                nit: 1,
                nfev: 1,
                njev: 0,
                nhev: 0,
                maxcv: 0,
                status: 0,
                jac: None,
                hess: None,
                constr: None,
                message: "Neuromorphic-Quantum hybrid completed".to_string(),
            })
        } else {
            Err(OptimizeError::InitializationError(
                "Required optimizers not available".to_string(),
            ))
        }
    }

    /// Execute Meta-Learning Quantum strategy
    fn execute_meta_learning_quantum<F>(&mut self, objective: &F) -> Result<OptimizeResults<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        if let (Some(quantum_opt), Some(meta_opt)) = (
            self.quantum_optimizer.as_mut(),
            self.meta_learning_optimizer.as_mut(),
        ) {
            // Meta-learning guided quantum state preparation
            let problem = OptimizationProblem {
                name: "current_problem".to_string(),
                dimension: self.state.global_best_solution.len(),
                problem_class: "unknown".to_string(),
                metadata: self.state.problem_characteristics.clone(),
                max_evaluations: 100,
                target_accuracy: self.config.tolerance,
            };

            meta_opt.adapt_to_problem(&problem, &self.state.global_best_solution.view())?;

            // Quantum evolution with meta-learning guidance
            let quantum_result = quantum_opt.optimize(objective)?;
            self.state.total_evaluations += quantum_result.nit;

            // Meta-learning from quantum results
            self.update_problem_characteristics(&quantum_result)?;

            Ok(quantum_result)
        } else {
            Err(OptimizeError::InitializationError(
                "Required optimizers not available".to_string(),
            ))
        }
    }

    /// Execute Adaptive Selection strategy
    fn execute_adaptive_selection<F>(&mut self, objective: &F) -> Result<OptimizeResults<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        // Select best strategy based on current performance
        let selected_strategy = self.strategy_selector.select_strategy(&self.state)?;

        match selected_strategy.as_str() {
            "quantum" => {
                if let Some(quantum_opt) = self.quantum_optimizer.as_mut() {
                    let result = quantum_opt.optimize(objective)?;
                    self.state.total_evaluations += result.nit;
                    Ok(result)
                } else {
                    Err(OptimizeError::InitializationError(
                        "Quantum optimizer not available".to_string(),
                    ))
                }
            }
            "neuromorphic" => {
                if let Some(neuro_opt) = self.neuromorphic_optimizer.as_mut() {
                    let result =
                        neuro_opt.optimize(objective, &self.state.global_best_solution.view())?;
                    self.state.total_evaluations += result.nit;
                    Ok(result)
                } else {
                    Err(OptimizeError::InitializationError(
                        "Neuromorphic optimizer not available".to_string(),
                    ))
                }
            }
            "meta_learning" => {
                if let Some(meta_opt) = self.meta_learning_optimizer.as_mut() {
                    let result =
                        meta_opt.optimize(objective, &self.state.global_best_solution.view())?;
                    self.state.total_evaluations += result.nit;
                    Ok(result)
                } else {
                    Err(OptimizeError::InitializationError(
                        "Meta-learning optimizer not available".to_string(),
                    ))
                }
            }
            _ => Err(OptimizeError::InitializationError(
                "Unknown strategy selected".to_string(),
            )),
        }
    }

    /// Execute Full Advanced strategy (all optimizers in parallel coordination)
    fn execute_full_advanced<F>(&mut self, objective: &F) -> Result<OptimizeResults<f64>>
    where
        F: Fn(&ArrayView1<f64>) -> f64,
    {
        let mut results = Vec::new();

        // Execute all available optimizers
        if let Some(quantum_opt) = self.quantum_optimizer.as_mut() {
            let quantum_candidate = quantum_opt.quantum_state.measure();
            let quantum_obj = objective(&quantum_candidate.view());
            self.state.total_evaluations += 1;

            results.push(OptimizeResults::<f64> {
                x: quantum_candidate,
                fun: quantum_obj,
                success: quantum_obj < f64::INFINITY,
                nit: 1,
                nfev: 1,
                njev: 0,
                nhev: 0,
                maxcv: 0,
                status: 0,
                jac: None,
                hess: None,
                constr: None,
                message: "Quantum component".to_string(),
            });
        }

        if let Some(neuro_opt) = self.neuromorphic_optimizer.as_mut() {
            let neural_candidate = neuro_opt.network().decode_parameters();
            let neural_obj = objective(&neural_candidate.view());
            self.state.total_evaluations += 1;

            results.push(OptimizeResults::<f64> {
                x: neural_candidate,
                fun: neural_obj,
                success: neural_obj < f64::INFINITY,
                nit: 1,
                nfev: 1,
                njev: 0,
                nhev: 0,
                maxcv: 0,
                status: 0,
                jac: None,
                hess: None,
                constr: None,
                message: "Neuromorphic component".to_string(),
            });
        }

        if let Some(meta_opt) = self.meta_learning_optimizer.as_mut() {
            // Use a simplified meta-learning step
            let meta_candidate = self.state.global_best_solution.clone();
            let meta_obj = objective(&meta_candidate.view());
            self.state.total_evaluations += 1;

            results.push(OptimizeResults::<f64> {
                x: meta_candidate,
                fun: meta_obj,
                success: meta_obj < f64::INFINITY,
                nit: 1,
                nfev: 1,
                njev: 0,
                nhev: 0,
                maxcv: 0,
                status: 0,
                jac: None,
                hess: None,
                constr: None,
                message: "Meta-learning component".to_string(),
            });
        }

        // Intelligent fusion of all results
        if !results.is_empty() {
            let fused_result = self.fusion_engine.fuse_multiple_solutions(&results)?;
            let fused_obj = objective(&fused_result.view());
            self.state.total_evaluations += 1;

            Ok(OptimizeResults::<f64> {
                x: fused_result,
                fun: fused_obj,
                success: fused_obj < f64::INFINITY,
                nit: 1,
                nfev: 1,
                njev: 0,
                nhev: 0,
                maxcv: 0,
                status: 0,
                jac: None,
                hess: None,
                constr: None,
                message: "Full Advanced coordination completed".to_string(),
            })
        } else {
            Err(OptimizeError::InitializationError(
                "No optimizers available".to_string(),
            ))
        }
    }

    /// Update performance tracking for strategy adaptation
    fn update_performance_tracking(&mut self, current_objective: f64) -> Result<()> {
        self.state.performance_history.push_back(current_objective);
        if self.state.performance_history.len() > self.config.performance_memory_size {
            self.state.performance_history.pop_front();
        }

        // Update strategy confidences based on recent performance
        self.update_strategy_confidences()?;

        Ok(())
    }

    /// Update strategy confidence scores
    fn update_strategy_confidences(&mut self) -> Result<()> {
        if self.state.performance_history.len() > 10 {
            let recent_improvement = self.compute_recent_improvement_rate();

            // Update confidences based on improvement rate
            for (_strategy, confidence) in self.state.strategy_confidences.iter_mut() {
                if recent_improvement > 0.0 {
                    *confidence = (*confidence * 0.9 + 0.1).min(1.0);
                } else {
                    *confidence = (*confidence * 0.95).max(0.1);
                }
            }
        }

        Ok(())
    }

    /// Compute recent improvement rate
    fn compute_recent_improvement_rate(&self) -> f64 {
        if self.state.performance_history.len() < 10 {
            return 0.0;
        }

        let recent: Vec<f64> = self
            .state
            .performance_history
            .iter()
            .rev()
            .take(10)
            .cloned()
            .collect();

        let initial = recent[9];
        let final_val = recent[0];

        if initial > 0.0 {
            (initial - final_val) / initial
        } else {
            0.0
        }
    }

    /// Adapt strategy based on performance
    fn adapt_strategy(&mut self) -> Result<()> {
        // Simple strategy adaptation logic
        let improvement_rate = self.compute_recent_improvement_rate();

        if improvement_rate < 0.001 {
            // Switch to more exploratory strategy
            self.config.strategy = match self.config.strategy {
                AdvancedStrategy::AdaptiveSelection => AdvancedStrategy::QuantumNeuralFusion,
                AdvancedStrategy::QuantumNeuralFusion => {
                    AdvancedStrategy::NeuromorphicQuantumHybrid
                }
                AdvancedStrategy::NeuromorphicQuantumHybrid => {
                    AdvancedStrategy::MetaLearningQuantum
                }
                AdvancedStrategy::MetaLearningQuantum => AdvancedStrategy::FullAdvanced,
                AdvancedStrategy::FullAdvanced => AdvancedStrategy::AdaptiveSelection,
            };
        }

        Ok(())
    }

    /// Perform knowledge transfer between optimization strategies
    fn perform_knowledge_transfer(&mut self) -> Result<()> {
        // Transfer best solutions between optimizers
        let best_solution = &self.state.global_best_solution;

        if let Some(quantum_opt) = self.quantum_optimizer.as_mut() {
            // Update quantum basis states with best known solution
            for i in 0..quantum_opt.quantum_state.basis_states.nrows() {
                for j in 0..best_solution
                    .len()
                    .min(quantum_opt.quantum_state.basis_states.ncols())
                {
                    let noise = (thread_rng().random::<f64>() - 0.5) * 0.1;
                    quantum_opt.quantum_state.basis_states[[i, j]] = best_solution[j] + noise;
                }
            }
        }

        if let Some(neuro_opt) = self.neuromorphic_optimizer.as_mut() {
            // Encode best solution into neuromorphic network
            neuro_opt
                .network_mut()
                .encode_parameters(&best_solution.view());
        }

        Ok(())
    }

    /// Update problem characteristics based on optimization results
    fn update_problem_characteristics(&mut self, result: &OptimizeResults<f64>) -> Result<()> {
        // Simple characteristic learning
        let dimensionality = result.x.len() as f64;
        let convergence_rate = if result.nit > 0 {
            1.0 / result.nit as f64
        } else {
            0.0
        };

        self.state
            .problem_characteristics
            .insert("dimensionality".to_string(), dimensionality);
        self.state
            .problem_characteristics
            .insert("convergence_rate".to_string(), convergence_rate);
        self.state
            .problem_characteristics
            .insert("objective_scale".to_string(), result.fun.abs().ln());

        Ok(())
    }

    /// Get comprehensive optimization statistics
    pub fn get_advanced_stats(&self) -> AdvancedStats {
        AdvancedStats {
            total_evaluations: self.state.total_evaluations,
            current_iteration: self.state.current_iteration,
            best_objective: self.state.global_best_objective,
            active_strategy: self.config.strategy,
            elapsed_time: self.state.start_time.elapsed(),
            strategy_confidences: self.state.strategy_confidences.clone(),
            problem_characteristics: self.state.problem_characteristics.clone(),
            quantum_stats: self
                .quantum_optimizer
                .as_ref()
                .map(|opt| opt.get_quantum_stats()),
        }
    }
}

/// Performance Predictor for strategy selection
#[derive(Debug)]
struct PerformancePredictor {
    // Simple predictor implementation
}

impl PerformancePredictor {
    fn new() -> Self {
        Self {}
    }
}

/// Cross-Modal Fusion Engine
#[derive(Debug)]
struct CrossModalFusionEngine {
    num_params: usize,
}

impl CrossModalFusionEngine {
    fn new(num_params: usize) -> Self {
        Self { num_params }
    }

    fn fuse_solutions(
        &self,
        solution1: &ArrayView1<f64>,
        solution2: &ArrayView1<f64>,
        fusion_strength: f64,
    ) -> Result<Array1<f64>> {
        let mut fused = Array1::zeros(self.num_params);

        for i in 0..self.num_params {
            if i < solution1.len() && i < solution2.len() {
                fused[i] = (1.0 - fusion_strength) * solution1[i] + fusion_strength * solution2[i];
            }
        }

        Ok(fused)
    }

    fn fuse_multiple_solutions(&self, results: &[OptimizeResults<f64>]) -> Result<Array1<f64>> {
        if results.is_empty() {
            return Ok(Array1::zeros(self.num_params));
        }

        let mut fused = Array1::zeros(self.num_params);
        let mut weights = Vec::new();

        // Compute weights based on objective values (better solutions get higher weight)
        let max_obj = results
            .iter()
            .map(|r| r.fun)
            .fold(f64::NEG_INFINITY, f64::max);
        for result in results {
            // Better solutions (lower objective) should get higher weights
            // Use (max_obj - fun + small_value) to give higher weight to lower objective values
            let weight = max_obj - result.fun + 1e-12;
            weights.push(weight);
        }

        // Normalize weights
        let total_weight: f64 = weights.iter().sum();
        if total_weight > 0.0 {
            for weight in &mut weights {
                *weight /= total_weight;
            }
        }

        // Weighted fusion
        for (result, weight) in results.iter().zip(weights.iter()) {
            for i in 0..self.num_params.min(result.x.len()) {
                fused[i] += weight * result.x[i];
            }
        }

        Ok(fused)
    }
}

/// Adaptive Strategy Selector
#[derive(Debug)]
struct AdaptiveStrategySelector {
    // Simple selector implementation
}

impl AdaptiveStrategySelector {
    fn new() -> Self {
        Self {}
    }

    fn select_strategy(&self, state: &AdvancedState) -> Result<String> {
        // Simple strategy selection based on performance history
        if state.performance_history.len() < 10 {
            return Ok("quantum".to_string());
        }

        let improvement_rate = if state.performance_history.len() >= 2 {
            let recent = state.performance_history.back().expect("Operation failed");
            let prev = state.performance_history[state.performance_history.len() - 2];

            if prev > 0.0 {
                (prev - recent) / prev
            } else {
                0.0
            }
        } else {
            0.0
        };

        if improvement_rate > 0.01 {
            Ok("quantum".to_string())
        } else if improvement_rate > 0.001 {
            Ok("neuromorphic".to_string())
        } else {
            Ok("meta_learning".to_string())
        }
    }
}

/// Comprehensive statistics for Advanced optimization
#[derive(Debug, Clone)]
pub struct AdvancedStats {
    pub total_evaluations: usize,
    pub current_iteration: usize,
    pub best_objective: f64,
    pub active_strategy: AdvancedStrategy,
    pub elapsed_time: Duration,
    pub strategy_confidences: HashMap<String, f64>,
    pub problem_characteristics: HashMap<String, f64>,
    pub quantum_stats: Option<QuantumOptimizationStats>,
}

/// Convenience function for Advanced optimization
#[allow(dead_code)]
pub fn advanced_optimize<F>(
    objective: F,
    initial_params: &ArrayView1<f64>,
    config: Option<AdvancedConfig>,
) -> Result<OptimizeResults<f64>>
where
    F: Fn(&ArrayView1<f64>) -> f64 + Send + Sync + Clone,
{
    let config = config.unwrap_or_default();
    let mut coordinator = AdvancedCoordinator::new(config, initial_params);
    coordinator.optimize(objective)
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_advanced_config_default() {
        let config = AdvancedConfig::default();
        assert_eq!(config.strategy, AdvancedStrategy::FullAdvanced);
        assert!(config.enable_quantum);
        assert!(config.enable_neuromorphic);
        assert!(config.enable_meta_learning);
    }

    #[test]
    fn test_advanced_coordinator_creation() {
        let config = AdvancedConfig::default();
        let initial_params = Array1::from(vec![1.0, 2.0]);
        let coordinator = AdvancedCoordinator::new(config, &initial_params.view());

        assert_eq!(coordinator.state.global_best_solution.len(), 2);
        assert!(coordinator.quantum_optimizer.is_some());
        assert!(coordinator.neuromorphic_optimizer.is_some());
        assert!(coordinator.meta_learning_optimizer.is_some());
    }

    #[test]
    fn test_cross_modal_fusion() {
        let fusion_engine = CrossModalFusionEngine::new(2);
        let sol1 = Array1::from(vec![1.0, 2.0]);
        let sol2 = Array1::from(vec![3.0, 4.0]);

        let fused = fusion_engine
            .fuse_solutions(&sol1.view(), &sol2.view(), 0.5)
            .expect("Operation failed");

        assert!((fused[0] - 2.0).abs() < 1e-10);
        assert!((fused[1] - 3.0).abs() < 1e-10);
    }

    #[test]
    fn test_advanced_optimization() {
        let config = AdvancedConfig {
            max_nit: 50,
            strategy: AdvancedStrategy::AdaptiveSelection,
            ..Default::default()
        };

        let objective = |x: &ArrayView1<f64>| x[0].powi(2) + x[1].powi(2);
        let initial = Array1::from(vec![2.0, 2.0]);

        let result =
            advanced_optimize(objective, &initial.view(), Some(config)).expect("Operation failed");

        assert!(result.nit > 0);
        assert!(result.fun <= objective(&initial.view()));
        assert!(result.success);
    }

    #[test]
    fn test_strategy_performance_tracking() {
        let config = AdvancedConfig::default();
        let initial_params = Array1::from(vec![1.0]);
        let mut coordinator = AdvancedCoordinator::new(config, &initial_params.view());

        // Add enough performance history (needs at least 10 values for improvement rate calculation)
        for i in 0..12 {
            coordinator
                .state
                .performance_history
                .push_back(15.0 - i as f64 * 0.5);
        }

        let improvement_rate = coordinator.compute_recent_improvement_rate();
        assert!(improvement_rate > 0.0);
    }

    #[test]
    fn test_multiple_solution_fusion() {
        let fusion_engine = CrossModalFusionEngine::new(2);
        let results = vec![
            OptimizeResults::<f64> {
                x: Array1::from(vec![1.0, 2.0]),
                fun: 1.0,
                success: true,
                nit: 10,
                nfev: 10,
                njev: 0,
                nhev: 0,
                maxcv: 0,
                status: 0,
                jac: None,
                hess: None,
                constr: None,
                message: "test1".to_string(),
            },
            OptimizeResults::<f64> {
                x: Array1::from(vec![3.0, 4.0]),
                fun: 2.0,
                success: true,
                nit: 15,
                nfev: 15,
                njev: 0,
                nhev: 0,
                maxcv: 0,
                status: 0,
                jac: None,
                hess: None,
                constr: None,
                message: "test2".to_string(),
            },
        ];

        let fused = fusion_engine
            .fuse_multiple_solutions(&results)
            .expect("Operation failed");
        assert_eq!(fused.len(), 2);

        // Better solution (lower objective) should have higher influence
        assert!(fused[0] < 2.0); // Closer to first solution
        assert!(fused[1] < 3.0); // Closer to first solution
    }
}

#[allow(dead_code)]
pub fn placeholder() {
    // Placeholder function to prevent unused module warnings
}
