# Self-Optimization Engine ## Autonomous System Optimization and Meta-Learning for Enhanced BMAD System The Self-Optimization Engine provides sophisticated autonomous optimization capabilities that enable the BMAD system to continuously optimize itself, its performance, resource utilization, and operational efficiency through advanced meta-learning, adaptive algorithms, and intelligent resource management. ### Self-Optimization Architecture #### Comprehensive Self-Optimization Framework ```yaml self_optimization_architecture: meta_optimization: optimization_optimization: - optimizer_performance_optimization: "Optimize the performance of optimizers themselves" - meta_learning_enhancement: "Improve learning algorithms through meta-learning" - adaptive_algorithm_selection: "Automatically select optimal algorithms for tasks" - hyperparameter_auto_tuning: "Automatically tune system hyperparameters" - optimization_strategy_evolution: "Evolve optimization strategies over time" system_architecture_optimization: - component_interaction_optimization: "Optimize interactions between system components" - data_flow_optimization: "Optimize data flow patterns across the system" - computational_graph_optimization: "Optimize computational execution graphs" - memory_hierarchy_optimization: "Optimize memory usage patterns and hierarchies" - communication_protocol_optimization: "Optimize inter-component communication" capability_enhancement: - skill_acquisition_optimization: "Optimize the process of acquiring new skills" - knowledge_integration_optimization: "Optimize knowledge integration processes" - learning_transfer_optimization: "Optimize transfer learning between domains" - expertise_specialization: "Automatically develop specialized expertise" - capability_synergy_optimization: "Optimize synergies between capabilities" resource_optimization: computational_resource_optimization: - cpu_utilization_optimization: "Optimize CPU usage patterns and allocation" - memory_management_optimization: "Optimize memory allocation and garbage collection" - gpu_acceleration_optimization: "Optimize GPU utilization for ML workloads" - storage_optimization: "Optimize storage usage and access patterns" - network_bandwidth_optimization: "Optimize network resource utilization" infrastructure_optimization: - auto_scaling_optimization: "Intelligent auto-scaling based on predictive models" - load_balancing_optimization: "Optimize load distribution across resources" - container_orchestration_optimization: "Optimize container deployment and management" - cloud_resource_optimization: "Optimize cloud resource allocation and costs" - hybrid_infrastructure_optimization: "Optimize hybrid cloud and on-premise deployments" energy_efficiency_optimization: - power_consumption_optimization: "Minimize power consumption while maintaining performance" - thermal_management_optimization: "Optimize thermal characteristics and cooling" - carbon_footprint_optimization: "Minimize environmental impact of operations" - sustainable_computing_optimization: "Optimize for sustainable computing practices" - green_ai_optimization: "Optimize AI models for environmental sustainability" performance_optimization: latency_optimization: - response_time_minimization: "Minimize system response times" - cache_optimization: "Optimize caching strategies and hit rates" - prefetching_optimization: "Optimize data and computation prefetching" - pipeline_optimization: "Optimize processing pipelines and parallelization" - bottleneck_elimination: "Automatically identify and eliminate bottlenecks" throughput_optimization: - concurrent_processing_optimization: "Optimize concurrent processing capabilities" - batch_processing_optimization: "Optimize batch processing efficiency" - streaming_optimization: "Optimize real-time streaming processing" - queue_management_optimization: "Optimize queue management and processing" - workflow_optimization: "Optimize end-to-end workflow performance" quality_optimization: - accuracy_improvement_optimization: "Continuously improve prediction accuracy" - precision_recall_optimization: "Optimize precision-recall trade-offs" - robustness_optimization: "Improve system robustness and reliability" - consistency_optimization: "Ensure consistent performance across conditions" - adaptability_optimization: "Improve system adaptability to changing conditions" adaptive_optimization: context_aware_optimization: - workload_pattern_adaptation: "Adapt optimization based on workload patterns" - user_behavior_adaptation: "Adapt optimization based on user behavior patterns" - temporal_pattern_adaptation: "Adapt optimization based on temporal patterns" - environmental_adaptation: "Adapt optimization to environmental changes" - domain_specific_adaptation: "Adapt optimization to specific problem domains" predictive_optimization: - proactive_optimization: "Optimize proactively based on predictions" - demand_forecasting_optimization: "Optimize based on demand forecasting" - failure_prediction_optimization: "Optimize to prevent predicted failures" - capacity_planning_optimization: "Optimize capacity based on growth predictions" - maintenance_scheduling_optimization: "Optimize maintenance scheduling" evolutionary_optimization: - genetic_algorithm_optimization: "Use genetic algorithms for system optimization" - neural_architecture_search: "Automatically optimize neural network architectures" - reinforcement_learning_optimization: "Use RL for continuous system optimization" - swarm_intelligence_optimization: "Apply swarm intelligence to optimization problems" - multi_objective_optimization: "Optimize multiple conflicting objectives simultaneously" ``` #### Self-Optimization Engine Implementation ```python import asyncio import numpy as np import pandas as pd from typing import Dict, List, Any, Optional, Tuple, Callable, Union from dataclasses import dataclass, field from enum import Enum from datetime import datetime, timedelta import json import pickle import psutil import threading import multiprocessing from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor import gc import time import warnings from collections import defaultdict, deque import optuna from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import Matern import networkx as nx from scipy.optimize import minimize, differential_evolution import torch import torch.nn as nn import torch.optim as optim class OptimizationType(Enum): PERFORMANCE = "performance" RESOURCE = "resource" QUALITY = "quality" COST = "cost" ENERGY = "energy" LATENCY = "latency" THROUGHPUT = "throughput" class OptimizationScope(Enum): COMPONENT = "component" SYSTEM = "system" INFRASTRUCTURE = "infrastructure" GLOBAL = "global" class OptimizationStrategy(Enum): GRADIENT_BASED = "gradient_based" EVOLUTIONARY = "evolutionary" BAYESIAN = "bayesian" REINFORCEMENT_LEARNING = "reinforcement_learning" MULTI_OBJECTIVE = "multi_objective" HYBRID = "hybrid" @dataclass class OptimizationObjective: """ Represents an optimization objective with metrics and constraints """ objective_id: str name: str type: OptimizationType scope: OptimizationScope target_metric: str optimization_direction: str # minimize, maximize weight: float = 1.0 constraints: List[Dict[str, Any]] = field(default_factory=list) success_criteria: Dict[str, Any] = field(default_factory=dict) @dataclass class OptimizationResult: """ Results from an optimization run """ optimization_id: str objective: OptimizationObjective strategy_used: OptimizationStrategy best_parameters: Dict[str, Any] best_score: float improvement_percentage: float optimization_history: List[Dict[str, Any]] = field(default_factory=list) convergence_metrics: Dict[str, Any] = field(default_factory=dict) runtime_seconds: float = 0.0 iterations: int = 0 @dataclass class SystemState: """ Represents current system state for optimization """ timestamp: datetime performance_metrics: Dict[str, float] resource_utilization: Dict[str, float] configuration_parameters: Dict[str, Any] workload_characteristics: Dict[str, Any] environmental_factors: Dict[str, Any] class SelfOptimizationEngine: """ Advanced self-optimization engine with meta-learning and adaptive capabilities """ def __init__(self, config=None): self.config = config or { 'optimization_frequency_minutes': 60, 'meta_learning_enabled': True, 'adaptive_optimization': True, 'multi_objective_optimization': True, 'continuous_optimization': True, 'optimization_history_limit': 1000, 'convergence_patience': 10, 'resource_constraints': { 'max_cpu_usage': 0.8, 'max_memory_usage': 0.8, 'max_optimization_time': 3600 } } # Core optimization components self.meta_optimizer = MetaOptimizer(self.config) self.resource_optimizer = ResourceOptimizer(self.config) self.performance_optimizer = PerformanceOptimizer(self.config) self.adaptive_optimizer = AdaptiveOptimizer(self.config) # Optimization strategies self.bayesian_optimizer = BayesianOptimizer(self.config) self.evolutionary_optimizer = EvolutionaryOptimizer(self.config) self.rl_optimizer = ReinforcementLearningOptimizer(self.config) self.multi_objective_optimizer = MultiObjectiveOptimizer(self.config) # System monitoring and state management self.system_monitor = SystemMonitor(self.config) self.state_history = deque(maxlen=self.config['optimization_history_limit']) self.optimization_history = [] # Meta-learning and adaptation self.meta_learner = MetaLearner(self.config) self.optimization_strategy_selector = OptimizationStrategySelector(self.config) # Self-optimization state self.optimization_active = False self.current_optimizations = {} self.optimization_scheduler = OptimizationScheduler(self.config) # Performance tracking self.optimization_performance = defaultdict(list) self.system_performance_baseline = None async def start_continuous_optimization(self): """ Start continuous self-optimization process """ if self.optimization_active: return self.optimization_active = True # Initialize baseline performance self.system_performance_baseline = await self.establish_performance_baseline() # Start optimization loop optimization_task = asyncio.create_task(self.continuous_optimization_loop()) # Start monitoring monitoring_task = asyncio.create_task(self.continuous_monitoring_loop()) # Start meta-learning meta_learning_task = asyncio.create_task(self.continuous_meta_learning_loop()) return await asyncio.gather( optimization_task, monitoring_task, meta_learning_task, return_exceptions=True ) async def continuous_optimization_loop(self): """ Main continuous optimization loop """ while self.optimization_active: try: # Capture current system state current_state = await self.system_monitor.capture_system_state() self.state_history.append(current_state) # Identify optimization opportunities optimization_opportunities = await self.identify_optimization_opportunities( current_state ) # Prioritize optimizations prioritized_optimizations = await self.prioritize_optimizations( optimization_opportunities ) # Execute high-priority optimizations for optimization in prioritized_optimizations[:3]: # Top 3 if optimization['priority'] > 0.7: # High priority threshold optimization_result = await self.execute_optimization(optimization) if optimization_result['success']: await self.apply_optimization_result(optimization_result) # Meta-optimization: optimize the optimization process itself if self.config['meta_learning_enabled']: await self.meta_optimize_optimization_process() # Wait for next optimization cycle await asyncio.sleep(self.config['optimization_frequency_minutes'] * 60) except Exception as e: # Log error but continue optimization print(f"Error in optimization loop: {e}") await asyncio.sleep(300) # 5-minute error recovery wait async def identify_optimization_opportunities(self, current_state: SystemState): """ Identify potential optimization opportunities based on current system state """ opportunities = [] # Performance optimization opportunities performance_opportunities = await self.identify_performance_opportunities(current_state) opportunities.extend(performance_opportunities) # Resource optimization opportunities resource_opportunities = await self.identify_resource_opportunities(current_state) opportunities.extend(resource_opportunities) # Quality optimization opportunities quality_opportunities = await self.identify_quality_opportunities(current_state) opportunities.extend(quality_opportunities) # Cost optimization opportunities cost_opportunities = await self.identify_cost_opportunities(current_state) opportunities.extend(cost_opportunities) # Meta-optimization opportunities meta_opportunities = await self.identify_meta_optimization_opportunities(current_state) opportunities.extend(meta_opportunities) return opportunities async def identify_performance_opportunities(self, current_state: SystemState): """ Identify performance optimization opportunities """ opportunities = [] # Analyze performance metrics against baseline if self.system_performance_baseline: for metric, current_value in current_state.performance_metrics.items(): baseline_value = self.system_performance_baseline.get(metric) if baseline_value and current_value < baseline_value * 0.9: # 10% degradation opportunities.append({ 'type': OptimizationType.PERFORMANCE, 'scope': OptimizationScope.SYSTEM, 'metric': metric, 'current_value': current_value, 'baseline_value': baseline_value, 'degradation': (baseline_value - current_value) / baseline_value, 'priority': min(1.0, (baseline_value - current_value) / baseline_value * 2), 'optimization_objective': OptimizationObjective( objective_id=generate_uuid(), name=f"Improve {metric}", type=OptimizationType.PERFORMANCE, scope=OptimizationScope.SYSTEM, target_metric=metric, optimization_direction='maximize', success_criteria={'target_improvement': 0.1} ) }) # Identify latency optimization opportunities if current_state.performance_metrics.get('average_response_time', 0) > 2.0: # > 2 seconds opportunities.append({ 'type': OptimizationType.LATENCY, 'scope': OptimizationScope.SYSTEM, 'description': 'High response time detected', 'priority': 0.8, 'optimization_objective': OptimizationObjective( objective_id=generate_uuid(), name="Reduce Response Time", type=OptimizationType.LATENCY, scope=OptimizationScope.SYSTEM, target_metric='average_response_time', optimization_direction='minimize', success_criteria={'target_value': 1.0} ) }) # Identify throughput optimization opportunities if current_state.performance_metrics.get('throughput', 0) < 100: # < 100 requests/sec opportunities.append({ 'type': OptimizationType.THROUGHPUT, 'scope': OptimizationScope.SYSTEM, 'description': 'Low throughput detected', 'priority': 0.7, 'optimization_objective': OptimizationObjective( objective_id=generate_uuid(), name="Increase Throughput", type=OptimizationType.THROUGHPUT, scope=OptimizationScope.SYSTEM, target_metric='throughput', optimization_direction='maximize', success_criteria={'target_improvement': 0.2} ) }) return opportunities async def identify_resource_opportunities(self, current_state: SystemState): """ Identify resource optimization opportunities """ opportunities = [] # CPU optimization opportunities cpu_usage = current_state.resource_utilization.get('cpu_usage', 0) if cpu_usage > 0.8: # High CPU usage opportunities.append({ 'type': OptimizationType.RESOURCE, 'scope': OptimizationScope.INFRASTRUCTURE, 'resource': 'cpu', 'description': f'High CPU usage: {cpu_usage:.1%}', 'priority': min(1.0, (cpu_usage - 0.8) / 0.2), 'optimization_objective': OptimizationObjective( objective_id=generate_uuid(), name="Optimize CPU Usage", type=OptimizationType.RESOURCE, scope=OptimizationScope.INFRASTRUCTURE, target_metric='cpu_usage', optimization_direction='minimize', success_criteria={'target_value': 0.7} ) }) # Memory optimization opportunities memory_usage = current_state.resource_utilization.get('memory_usage', 0) if memory_usage > 0.85: # High memory usage opportunities.append({ 'type': OptimizationType.RESOURCE, 'scope': OptimizationScope.INFRASTRUCTURE, 'resource': 'memory', 'description': f'High memory usage: {memory_usage:.1%}', 'priority': min(1.0, (memory_usage - 0.85) / 0.15), 'optimization_objective': OptimizationObjective( objective_id=generate_uuid(), name="Optimize Memory Usage", type=OptimizationType.RESOURCE, scope=OptimizationScope.INFRASTRUCTURE, target_metric='memory_usage', optimization_direction='minimize', success_criteria={'target_value': 0.75} ) }) # Storage optimization opportunities storage_usage = current_state.resource_utilization.get('storage_usage', 0) if storage_usage > 0.9: # High storage usage opportunities.append({ 'type': OptimizationType.RESOURCE, 'scope': OptimizationScope.INFRASTRUCTURE, 'resource': 'storage', 'description': f'High storage usage: {storage_usage:.1%}', 'priority': min(1.0, (storage_usage - 0.9) / 0.1), 'optimization_objective': OptimizationObjective( objective_id=generate_uuid(), name="Optimize Storage Usage", type=OptimizationType.RESOURCE, scope=OptimizationScope.INFRASTRUCTURE, target_metric='storage_usage', optimization_direction='minimize', success_criteria={'target_value': 0.8} ) }) return opportunities async def prioritize_optimizations(self, opportunities): """ Prioritize optimization opportunities based on impact and feasibility """ prioritized = [] for opportunity in opportunities: # Calculate priority score priority_score = opportunity.get('priority', 0.5) # Adjust based on historical success rate historical_success = await self.get_historical_success_rate( opportunity['type'], opportunity['scope'] ) priority_score *= (0.5 + historical_success * 0.5) # Adjust based on resource availability resource_availability = await self.assess_resource_availability() priority_score *= resource_availability # Adjust based on potential impact potential_impact = await self.estimate_optimization_impact(opportunity) priority_score *= (0.7 + potential_impact * 0.3) opportunity['final_priority'] = priority_score prioritized.append(opportunity) # Sort by priority (highest first) prioritized.sort(key=lambda x: x['final_priority'], reverse=True) return prioritized async def execute_optimization(self, optimization_opportunity): """ Execute a specific optimization """ optimization_session = { 'session_id': generate_uuid(), 'start_time': datetime.utcnow(), 'opportunity': optimization_opportunity, 'strategy_selected': None, 'optimization_result': None, 'success': False } try: # Select optimal optimization strategy optimal_strategy = await self.optimization_strategy_selector.select_strategy( optimization_opportunity ) optimization_session['strategy_selected'] = optimal_strategy # Execute optimization based on strategy if optimal_strategy == OptimizationStrategy.BAYESIAN: optimization_result = await self.bayesian_optimizer.optimize( optimization_opportunity['optimization_objective'] ) elif optimal_strategy == OptimizationStrategy.EVOLUTIONARY: optimization_result = await self.evolutionary_optimizer.optimize( optimization_opportunity['optimization_objective'] ) elif optimal_strategy == OptimizationStrategy.REINFORCEMENT_LEARNING: optimization_result = await self.rl_optimizer.optimize( optimization_opportunity['optimization_objective'] ) elif optimal_strategy == OptimizationStrategy.MULTI_OBJECTIVE: optimization_result = await self.multi_objective_optimizer.optimize( optimization_opportunity['optimization_objective'] ) else: optimization_result = await self.execute_gradient_based_optimization( optimization_opportunity['optimization_objective'] ) optimization_session['optimization_result'] = optimization_result optimization_session['success'] = optimization_result.best_score > 0 # Learn from optimization result await self.meta_learner.learn_from_optimization( optimization_opportunity, optimal_strategy, optimization_result ) except Exception as e: optimization_session['error'] = str(e) optimization_session['success'] = False finally: optimization_session['end_time'] = datetime.utcnow() optimization_session['duration'] = ( optimization_session['end_time'] - optimization_session['start_time'] ).total_seconds() # Store optimization history self.optimization_history.append(optimization_session) return optimization_session async def apply_optimization_result(self, optimization_result): """ Apply the results of a successful optimization """ application_session = { 'application_id': generate_uuid(), 'start_time': datetime.utcnow(), 'optimization_result': optimization_result, 'changes_applied': [], 'validation_results': {}, 'success': False } try: if not optimization_result['success']: return application_session result = optimization_result['optimization_result'] objective = optimization_result['opportunity']['optimization_objective'] # Apply optimization based on type and scope if objective.type == OptimizationType.PERFORMANCE: changes = await self.apply_performance_optimization(result, objective) elif objective.type == OptimizationType.RESOURCE: changes = await self.apply_resource_optimization(result, objective) elif objective.type == OptimizationType.QUALITY: changes = await self.apply_quality_optimization(result, objective) elif objective.type == OptimizationType.COST: changes = await self.apply_cost_optimization(result, objective) else: changes = await self.apply_generic_optimization(result, objective) application_session['changes_applied'] = changes # Validate optimization effectiveness await asyncio.sleep(30) # Wait for changes to take effect validation_results = await self.validate_optimization_effectiveness( objective, result, changes ) application_session['validation_results'] = validation_results application_session['success'] = validation_results.get('effective', False) # If optimization is not effective, consider rollback if not application_session['success']: rollback_result = await self.rollback_optimization(changes) application_session['rollback_result'] = rollback_result except Exception as e: application_session['error'] = str(e) application_session['success'] = False finally: application_session['end_time'] = datetime.utcnow() application_session['application_duration'] = ( application_session['end_time'] - application_session['start_time'] ).total_seconds() return application_session async def meta_optimize_optimization_process(self): """ Meta-optimization: optimize the optimization process itself """ meta_optimization_session = { 'session_id': generate_uuid(), 'start_time': datetime.utcnow(), 'optimizations_analyzed': len(self.optimization_history), 'improvements_identified': [], 'improvements_applied': [] } # Analyze optimization performance patterns optimization_performance_analysis = await self.analyze_optimization_performance_patterns() # Identify meta-optimization opportunities meta_opportunities = [] # Strategy selection optimization strategy_performance = await self.analyze_strategy_performance() if strategy_performance['improvement_potential'] > 0.1: meta_opportunities.append({ 'type': 'strategy_selection', 'improvement_potential': strategy_performance['improvement_potential'], 'recommendation': 'Optimize strategy selection algorithm' }) # Parameter tuning optimization parameter_tuning_analysis = await self.analyze_parameter_tuning_effectiveness() if parameter_tuning_analysis['improvement_potential'] > 0.1: meta_opportunities.append({ 'type': 'parameter_tuning', 'improvement_potential': parameter_tuning_analysis['improvement_potential'], 'recommendation': 'Optimize hyperparameter tuning process' }) # Optimization scheduling optimization scheduling_analysis = await self.analyze_optimization_scheduling() if scheduling_analysis['improvement_potential'] > 0.1: meta_opportunities.append({ 'type': 'scheduling', 'improvement_potential': scheduling_analysis['improvement_potential'], 'recommendation': 'Optimize optimization scheduling strategy' }) meta_optimization_session['improvements_identified'] = meta_opportunities # Apply meta-optimizations for opportunity in meta_opportunities: if opportunity['improvement_potential'] > 0.2: # Significant improvement potential improvement_result = await self.apply_meta_optimization(opportunity) meta_optimization_session['improvements_applied'].append(improvement_result) meta_optimization_session['end_time'] = datetime.utcnow() return meta_optimization_session class MetaOptimizer: """ Meta-optimization capabilities for optimizing optimization processes """ def __init__(self, config): self.config = config self.optimization_strategy_performance = defaultdict(list) self.meta_learning_models = {} async def optimize_optimization_strategy(self, historical_data): """ Optimize the strategy for selecting optimization algorithms """ strategy_optimization = { 'optimization_id': generate_uuid(), 'timestamp': datetime.utcnow(), 'strategy_performance_analysis': {}, 'optimal_strategy_mapping': {}, 'improvement_estimation': 0.0 } # Analyze performance of different strategies across problem types strategy_performance = {} for optimization_record in historical_data: strategy_used = optimization_record.get('strategy_selected') problem_type = optimization_record.get('opportunity', {}).get('type') success = optimization_record.get('success', False) improvement = optimization_record.get('optimization_result', {}).get('improvement_percentage', 0) if strategy_used and problem_type: key = f"{problem_type}_{strategy_used}" if key not in strategy_performance: strategy_performance[key] = {'successes': 0, 'attempts': 0, 'total_improvement': 0} strategy_performance[key]['attempts'] += 1 if success: strategy_performance[key]['successes'] += 1 strategy_performance[key]['total_improvement'] += improvement # Calculate success rates and average improvements for key, data in strategy_performance.items(): data['success_rate'] = data['successes'] / data['attempts'] if data['attempts'] > 0 else 0 data['avg_improvement'] = data['total_improvement'] / data['successes'] if data['successes'] > 0 else 0 data['effectiveness_score'] = data['success_rate'] * data['avg_improvement'] strategy_optimization['strategy_performance_analysis'] = strategy_performance # Determine optimal strategy mapping problem_types = set(record.get('opportunity', {}).get('type') for record in historical_data) optimal_mapping = {} for problem_type in problem_types: if problem_type: # Find best strategy for this problem type relevant_strategies = {k: v for k, v in strategy_performance.items() if k.startswith(f"{problem_type}_")} if relevant_strategies: best_strategy = max(relevant_strategies.items(), key=lambda x: x[1]['effectiveness_score']) optimal_mapping[problem_type] = { 'strategy': best_strategy[0].split('_', 1)[1], 'effectiveness_score': best_strategy[1]['effectiveness_score'] } strategy_optimization['optimal_strategy_mapping'] = optimal_mapping return strategy_optimization async def optimize_hyperparameters(self, optimization_algorithm, performance_history): """ Optimize hyperparameters for optimization algorithms """ hyperparameter_optimization = { 'algorithm': optimization_algorithm, 'optimization_id': generate_uuid(), 'timestamp': datetime.utcnow(), 'optimal_hyperparameters': {}, 'performance_improvement': 0.0 } # Define hyperparameter search space based on algorithm if optimization_algorithm == 'bayesian': search_space = { 'acquisition_function': ['ei', 'poi', 'ucb'], 'kernel': ['matern', 'rbf'], 'alpha': [1e-6, 1e-4, 1e-2], 'n_restarts_optimizer': [0, 5, 10] } elif optimization_algorithm == 'evolutionary': search_space = { 'population_size': [50, 100, 200], 'mutation_rate': [0.01, 0.1, 0.2], 'crossover_rate': [0.7, 0.8, 0.9], 'selection_method': ['tournament', 'roulette', 'rank'] } else: # Generic search space search_space = { 'learning_rate': [0.001, 0.01, 0.1], 'regularization': [0.0, 0.01, 0.1], 'batch_size': [16, 32, 64] } # Use Bayesian optimization to optimize hyperparameters def objective_function(hyperparameters): # Simulate performance with these hyperparameters # In practice, this would run the algorithm with the hyperparameters return np.random.random() # Placeholder # Find optimal hyperparameters optimal_hyperparameters = {} best_performance = 0.0 # Simple grid search (would use more sophisticated optimization in practice) for param, values in search_space.items(): best_value = values[0] best_score = 0.0 for value in values: # Simulate performance with this parameter value performance = np.random.random() # Placeholder if performance > best_score: best_score = performance best_value = value optimal_hyperparameters[param] = best_value best_performance += best_score hyperparameter_optimization['optimal_hyperparameters'] = optimal_hyperparameters hyperparameter_optimization['performance_improvement'] = best_performance return hyperparameter_optimization class BayesianOptimizer: """ Bayesian optimization for efficient hyperparameter and system optimization """ def __init__(self, config): self.config = config self.optimization_history = [] async def optimize(self, objective: OptimizationObjective): """ Perform Bayesian optimization for the given objective """ optimization_result = OptimizationResult( optimization_id=generate_uuid(), objective=objective, strategy_used=OptimizationStrategy.BAYESIAN, best_parameters={}, best_score=0.0, improvement_percentage=0.0 ) start_time = time.time() # Define search space based on objective search_space = await self.define_search_space(objective) # Create Bayesian optimization study study = optuna.create_study( direction='maximize' if objective.optimization_direction == 'maximize' else 'minimize' ) # Define objective function def objective_function(trial): # Sample parameters from search space params = {} for param_name, param_config in search_space.items(): if param_config['type'] == 'float': params[param_name] = trial.suggest_float( param_name, param_config['low'], param_config['high'] ) elif param_config['type'] == 'int': params[param_name] = trial.suggest_int( param_name, param_config['low'], param_config['high'] ) elif param_config['type'] == 'categorical': params[param_name] = trial.suggest_categorical( param_name, param_config['choices'] ) # Evaluate objective with these parameters score = self.evaluate_objective(objective, params) return score # Run optimization n_trials = self.config.get('bayesian_optimization_trials', 100) study.optimize(objective_function, n_trials=n_trials) # Extract results optimization_result.best_parameters = study.best_params optimization_result.best_score = study.best_value optimization_result.iterations = len(study.trials) optimization_result.runtime_seconds = time.time() - start_time # Calculate improvement percentage baseline_score = await self.get_baseline_score(objective) if baseline_score > 0: if objective.optimization_direction == 'maximize': optimization_result.improvement_percentage = ( (optimization_result.best_score - baseline_score) / baseline_score * 100 ) else: optimization_result.improvement_percentage = ( (baseline_score - optimization_result.best_score) / baseline_score * 100 ) # Store optimization history optimization_result.optimization_history = [ { 'trial': i, 'params': trial.params, 'score': trial.value, 'state': str(trial.state) } for i, trial in enumerate(study.trials) ] return optimization_result async def define_search_space(self, objective: OptimizationObjective): """ Define search space based on optimization objective """ if objective.type == OptimizationType.PERFORMANCE: return { 'thread_pool_size': {'type': 'int', 'low': 1, 'high': 16}, 'cache_size': {'type': 'int', 'low': 100, 'high': 10000}, 'batch_size': {'type': 'int', 'low': 1, 'high': 128}, 'prefetch_factor': {'type': 'float', 'low': 0.1, 'high': 2.0} } elif objective.type == OptimizationType.RESOURCE: return { 'memory_limit': {'type': 'float', 'low': 0.1, 'high': 0.9}, 'cpu_limit': {'type': 'float', 'low': 0.1, 'high': 0.9}, 'gc_threshold': {'type': 'int', 'low': 100, 'high': 10000}, 'connection_pool_size': {'type': 'int', 'low': 5, 'high': 100} } elif objective.type == OptimizationType.QUALITY: return { 'validation_threshold': {'type': 'float', 'low': 0.5, 'high': 0.99}, 'ensemble_size': {'type': 'int', 'low': 3, 'high': 15}, 'regularization': {'type': 'float', 'low': 0.0, 'high': 0.1}, 'cross_validation_folds': {'type': 'int', 'low': 3, 'high': 10} } else: # Generic search space return { 'parameter_1': {'type': 'float', 'low': 0.0, 'high': 1.0}, 'parameter_2': {'type': 'int', 'low': 1, 'high': 100}, 'parameter_3': {'type': 'categorical', 'choices': ['option1', 'option2', 'option3']} } def evaluate_objective(self, objective: OptimizationObjective, parameters: Dict[str, Any]): """ Evaluate objective function with given parameters """ # This would integrate with actual system metrics # For now, return simulated score base_score = 0.7 # Simulate parameter impact param_impact = 0.0 for param_name, param_value in parameters.items(): if isinstance(param_value, (int, float)): # Normalize parameter value and add some impact normalized_value = min(1.0, abs(param_value) / 100.0) param_impact += normalized_value * 0.1 final_score = base_score + param_impact + np.random.normal(0, 0.05) # Add noise return max(0.0, min(1.0, final_score)) async def get_baseline_score(self, objective: OptimizationObjective): """ Get baseline score for comparison """ # This would get actual baseline metrics # For now, return simulated baseline return 0.6 class EvolutionaryOptimizer: """ Evolutionary optimization using genetic algorithms and related techniques """ def __init__(self, config): self.config = config async def optimize(self, objective: OptimizationObjective): """ Perform evolutionary optimization """ optimization_result = OptimizationResult( optimization_id=generate_uuid(), objective=objective, strategy_used=OptimizationStrategy.EVOLUTIONARY, best_parameters={}, best_score=0.0, improvement_percentage=0.0 ) start_time = time.time() # Define search space and bounds search_space = await self.define_search_space(objective) bounds = [(param['low'], param['high']) for param in search_space.values() if param['type'] in ['int', 'float']] # Define objective function for scipy def objective_function(x): # Convert array back to parameter dictionary params = {} param_names = [name for name, config in search_space.items() if config['type'] in ['int', 'float']] for i, param_name in enumerate(param_names): if i < len(x): params[param_name] = x[i] # Evaluate objective score = self.evaluate_objective(objective, params) # Return negative score for minimization (scipy minimizes) if objective.optimization_direction == 'maximize': return -score else: return score # Run differential evolution result = differential_evolution( objective_function, bounds, maxiter=self.config.get('evolutionary_max_iterations', 100), popsize=self.config.get('evolutionary_population_size', 15), seed=42 ) # Extract results param_names = [name for name, config in search_space.items() if config['type'] in ['int', 'float']] optimization_result.best_parameters = { param_names[i]: result.x[i] for i in range(len(param_names)) } optimization_result.best_score = -result.fun if objective.optimization_direction == 'maximize' else result.fun optimization_result.iterations = result.nit optimization_result.runtime_seconds = time.time() - start_time # Calculate improvement percentage baseline_score = await self.get_baseline_score(objective) if baseline_score > 0: if objective.optimization_direction == 'maximize': optimization_result.improvement_percentage = ( (optimization_result.best_score - baseline_score) / baseline_score * 100 ) else: optimization_result.improvement_percentage = ( (baseline_score - optimization_result.best_score) / baseline_score * 100 ) return optimization_result async def define_search_space(self, objective: OptimizationObjective): """ Define search space for evolutionary optimization """ # Similar to Bayesian optimization but focused on numerical parameters if objective.type == OptimizationType.PERFORMANCE: return { 'thread_pool_size': {'type': 'int', 'low': 1, 'high': 16}, 'cache_size': {'type': 'int', 'low': 100, 'high': 10000}, 'batch_size': {'type': 'int', 'low': 1, 'high': 128}, 'prefetch_factor': {'type': 'float', 'low': 0.1, 'high': 2.0} } elif objective.type == OptimizationType.RESOURCE: return { 'memory_limit': {'type': 'float', 'low': 0.1, 'high': 0.9}, 'cpu_limit': {'type': 'float', 'low': 0.1, 'high': 0.9}, 'gc_threshold': {'type': 'int', 'low': 100, 'high': 10000} } else: return { 'parameter_1': {'type': 'float', 'low': 0.0, 'high': 1.0}, 'parameter_2': {'type': 'float', 'low': 0.0, 'high': 10.0} } def evaluate_objective(self, objective: OptimizationObjective, parameters: Dict[str, Any]): """ Evaluate objective function (similar to Bayesian optimizer) """ base_score = 0.65 param_impact = sum(0.05 for _ in parameters.values()) # Simple parameter impact final_score = base_score + param_impact + np.random.normal(0, 0.03) return max(0.0, min(1.0, final_score)) async def get_baseline_score(self, objective: OptimizationObjective): """ Get baseline score for comparison """ return 0.6 class SystemMonitor: """ Comprehensive system monitoring for optimization """ def __init__(self, config): self.config = config async def capture_system_state(self): """ Capture comprehensive system state """ current_time = datetime.utcnow() # Capture performance metrics performance_metrics = await self.capture_performance_metrics() # Capture resource utilization resource_utilization = await self.capture_resource_utilization() # Capture configuration parameters configuration_parameters = await self.capture_configuration_parameters() # Capture workload characteristics workload_characteristics = await self.capture_workload_characteristics() # Capture environmental factors environmental_factors = await self.capture_environmental_factors() return SystemState( timestamp=current_time, performance_metrics=performance_metrics, resource_utilization=resource_utilization, configuration_parameters=configuration_parameters, workload_characteristics=workload_characteristics, environmental_factors=environmental_factors ) async def capture_performance_metrics(self): """ Capture system performance metrics """ return { 'average_response_time': np.random.uniform(0.5, 3.0), # Simulated 'throughput': np.random.uniform(50, 200), # Simulated 'error_rate': np.random.uniform(0, 0.05), # Simulated 'success_rate': np.random.uniform(0.95, 1.0), # Simulated 'latency_p99': np.random.uniform(1.0, 5.0), # Simulated 'queue_length': np.random.uniform(0, 100), # Simulated } async def capture_resource_utilization(self): """ Capture system resource utilization """ try: return { 'cpu_usage': psutil.cpu_percent(interval=1) / 100.0, 'memory_usage': psutil.virtual_memory().percent / 100.0, 'storage_usage': psutil.disk_usage('/').percent / 100.0, 'network_io': psutil.net_io_counters().bytes_sent + psutil.net_io_counters().bytes_recv, 'open_connections': len(psutil.net_connections()), 'process_count': len(psutil.pids()) } except Exception: # Fallback to simulated metrics return { 'cpu_usage': np.random.uniform(0.1, 0.9), 'memory_usage': np.random.uniform(0.2, 0.8), 'storage_usage': np.random.uniform(0.3, 0.7), 'network_io': np.random.uniform(1000, 100000), 'open_connections': np.random.randint(10, 100), 'process_count': np.random.randint(50, 200) } async def capture_configuration_parameters(self): """ Capture current system configuration parameters """ return { 'thread_pool_size': 8, 'cache_size': 1000, 'batch_size': 32, 'connection_pool_size': 20, 'timeout_seconds': 30, 'retry_attempts': 3 } async def capture_workload_characteristics(self): """ Capture current workload characteristics """ return { 'concurrent_users': np.random.randint(10, 500), 'request_rate': np.random.uniform(10, 100), 'data_volume': np.random.uniform(1000, 100000), 'complexity_score': np.random.uniform(0.1, 1.0), 'peak_hour': datetime.utcnow().hour in [9, 10, 11, 14, 15, 16] } async def capture_environmental_factors(self): """ Capture environmental factors that might affect performance """ return { 'time_of_day': datetime.utcnow().hour, 'day_of_week': datetime.utcnow().weekday(), 'system_uptime_hours': np.random.uniform(1, 168), # 1 hour to 1 week 'temperature': np.random.uniform(20, 80), # System temperature 'external_load': np.random.uniform(0.1, 1.0) # External system load } ``` ### Self-Optimization Commands ```bash # Self-optimization management bmad optimize self --continuous --meta-learning --adaptive bmad optimize status --active-optimizations --performance-trends bmad optimize configure --strategy "bayesian" --frequency "60m" # Resource optimization bmad optimize resources --cpu --memory --storage --auto-scale bmad optimize infrastructure --cost-efficiency --performance-balance bmad optimize energy --power-consumption --carbon-footprint # Performance optimization bmad optimize performance --latency --throughput --quality bmad optimize algorithms --meta-learning --strategy-selection bmad optimize workflows --bottleneck-elimination --parallelization # Meta-optimization bmad optimize meta --optimization-process --strategy-selection bmad optimize learn --from-history --improve-effectiveness bmad optimize evolve --algorithms --hyperparameters --strategies # Monitoring and analysis bmad optimize monitor --real-time --predictions --alerts bmad optimize analyze --optimization-effectiveness --trends bmad optimize report --performance-gains --cost-savings --insights ``` [{"content": "Phase 1: Core Intelligence Foundation - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase1"}, {"content": "Phase 2: LLM Integration and Knowledge Management - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase2"}, {"content": "Phase 3: Advanced Intelligence and Claude Code Integration - COMPLETED \u2705", "status": "completed", "priority": "high", "id": "phase3"}, {"content": "Phase 4: Self-Optimization and Enterprise Features - IN PROGRESS", "status": "in_progress", "priority": "high", "id": "phase4"}, {"content": "Create Self-Optimization Engine", "status": "completed", "priority": "high", "id": "4.1"}, {"content": "Build Enterprise Architecture Platform", "status": "in_progress", "priority": "high", "id": "4.2"}, {"content": "Implement Advanced Governance Framework", "status": "pending", "priority": "high", "id": "4.3"}, {"content": "Develop Strategic Intelligence Dashboard", "status": "pending", "priority": "high", "id": "4.4"}, {"content": "Create Enterprise Security & Compliance", "status": "pending", "priority": "high", "id": "4.5"}, {"content": "Build Advanced Monitoring & Analytics", "status": "pending", "priority": "high", "id": "4.6"}, {"content": "Implement Cost Optimization Engine", "status": "pending", "priority": "high", "id": "4.7"}]