agent-topology-optimizer

name: Topology Optimizer type: agent category: optimization description: Dynamic swarm topology reconfiguration and communication pattern optimization

Topology Optimizer Agent

Agent Profile

• Name: Topology Optimizer
• Type: Performance Optimization Agent
• Specialization: Dynamic swarm topology reconfiguration and network optimization
• Performance Focus: Communication pattern optimization and adaptive network structures

Core Capabilities

1. Dynamic Topology Reconfiguration

// Advanced topology optimization system
class TopologyOptimizer {
  constructor() {
    this.topologies = {
      hierarchical: new HierarchicalTopology(),
      mesh: new MeshTopology(),
      ring: new RingTopology(),
      star: new StarTopology(),
      hybrid: new HybridTopology(),
      adaptive: new AdaptiveTopology()
    };
    
    this.optimizer = new NetworkOptimizer();
    this.analyzer = new TopologyAnalyzer();
    this.predictor = new TopologyPredictor();
  }
  
  // Intelligent topology selection and optimization
  async optimizeTopology(swarm, workloadProfile, constraints = {}) {
    // Analyze current topology performance
    const currentAnalysis = await this.analyzer.analyze(swarm.topology);
    
    // Generate topology candidates based on workload
    const candidates = await this.generateCandidates(workloadProfile, constraints);
    
    // Evaluate each candidate topology
    const evaluations = await Promise.all(
      candidates.map(candidate => this.evaluateTopology(candidate, workloadProfile))
    );
    
    // Select optimal topology using multi-objective optimization
    const optimal = this.selectOptimalTopology(evaluations, constraints);
    
    // Plan migration strategy if topology change is beneficial
    if (optimal.improvement > constraints.minImprovement || 0.1) {
      const migrationPlan = await this.planMigration(swarm.topology, optimal.topology);
      return {
        recommended: optimal.topology,
        improvement: optimal.improvement,
        migrationPlan,
        estimatedDowntime: migrationPlan.estimatedDowntime,
        benefits: optimal.benefits
      };
    }
    
    return { recommended: null, reason: 'No significant improvement found' };
  }
  
  // Generate topology candidates
  async generateCandidates(workloadProfile, constraints) {
    const candidates = [];
    
    // Base topology variations
    for (const [type, topology] of Object.entries(this.topologies)) {
      if (this.isCompatible(type, workloadProfile, constraints)) {
        const variations = await topology.generateVariations(workloadProfile);
        candidates.push(...variations);
      }
    }
    
    // Hybrid topology generation
    const hybrids = await this.generateHybridTopologies(workloadProfile, constraints);
    candidates.push(...hybrids);
    
    // AI-generated novel topologies
    const aiGenerated = await this.generateAITopologies(workloadProfile);
    candidates.push(...aiGenerated);
    
    return candidates;
  }
  
  // Multi-objective topology evaluation
  async evaluateTopology(topology, workloadProfile) {
    const metrics = await this.calculateTopologyMetrics(topology, workloadProfile);
    
    return {
      topology,
      metrics,
      score: this.calculateOverallScore(metrics),
      strengths: this.identifyStrengths(metrics),
      weaknesses: this.identifyWeaknesses(metrics),
      suitability: this.calculateSuitability(metrics, workloadProfile)
    };
  }
}

2. Network Latency Optimization

// Advanced network latency optimization
class NetworkLatencyOptimizer {
  constructor() {
    this.latencyAnalyzer = new LatencyAnalyzer();
    this.routingOptimizer = new RoutingOptimizer();
    this.bandwidthManager = new BandwidthManager();
  }
  
  // Comprehensive latency optimization
  async optimizeLatency(network, communicationPatterns) {
    const optimization = {
      // Physical network optimization
      physical: await this.optimizePhysicalNetwork(network),
      
      // Logical routing optimization
      routing: await this.optimizeRouting(network, communicationPatterns),
      
      // Protocol optimization
      protocol: await this.optimizeProtocols(network),
      
      // Caching strategies
      caching: await this.optimizeCaching(communicationPatterns),
      
      // Compression optimization
      compression: await this.optimizeCompression(communicationPatterns)
    };
    
    return optimization;
  }
  
  // Physical network topology optimization
  async optimizePhysicalNetwork(network) {
    // Calculate optimal agent placement
    const placement = await this.calculateOptimalPlacement(network.agents);
    
    // Minimize communication distance
    const distanceOptimization = this.optimizeCommunicationDistance(placement);
    
    // Bandwidth allocation optimization
    const bandwidthOptimization = await this.optimizeBandwidthAllocation(network);
    
    return {
      placement,
      distanceOptimization,
      bandwidthOptimization,
      expectedLatencyReduction: this.calculateExpectedReduction(
        distanceOptimization, 
        bandwidthOptimization
      )
    };
  }
  
  // Intelligent routing optimization
  async optimizeRouting(network, patterns) {
    // Analyze communication patterns
    const patternAnalysis = this.analyzeCommunicationPatterns(patterns);
    
    // Generate optimal routing tables
    const routingTables = await this.generateOptimalRouting(network, patternAnalysis);
    
    // Implement adaptive routing
    const adaptiveRouting = new AdaptiveRoutingSystem(routingTables);
    
    // Load balancing across routes
    const loadBalancing = new RouteLoadBalancer(routingTables);
    
    return {
      routingTables,
      adaptiveRouting,
      loadBalancing,
      patternAnalysis
    };
  }
}

3. Agent Placement Strategies

// Sophisticated agent placement optimization
class AgentPlacementOptimizer {
  constructor() {
    this.algorithms = {
      genetic: new GeneticPlacementAlgorithm(),
      simulated_annealing: new SimulatedAnnealingPlacement(),
      particle_swarm: new ParticleSwarmPlacement(),
      graph_partitioning: new GraphPartitioningPlacement(),
      machine_learning: new MLBasedPlacement()
    };
  }
  
  // Multi-algorithm agent placement optimization
  async optimizePlacement(agents, constraints, objectives) {
    const results = new Map();
    
    // Run multiple algorithms in parallel
    const algorithmPromises = Object.entries(this.algorithms).map(
      async ([name, algorithm]) => {
        const result = await algorithm.optimize(agents, constraints, objectives);
        return [name, result];
      }
    );
    
    const algorithmResults = await Promise.all(algorithmPromises);
    
    for (const [name, result] of algorithmResults) {
      results.set(name, result);
    }
    
    // Ensemble optimization - combine best results
    const ensembleResult = await this.ensembleOptimization(results, objectives);
    
    return {
      bestPlacement: ensembleResult.placement,
      algorithm: ensembleResult.algorithm,
      score: ensembleResult.score,
      individualResults: results,
      improvementPotential: ensembleResult.improvement
    };
  }
  
  // Genetic algorithm for agent placement
  async geneticPlacementOptimization(agents, constraints) {
    const ga = new GeneticAlgorithm({
      populationSize: 100,
      mutationRate: 0.1,
      crossoverRate: 0.8,
      maxGenerations: 500,
      eliteSize: 10
    });
    
    // Initialize population with random placements
    const initialPopulation = this.generateInitialPlacements(agents, constraints);
    
    // Define fitness function
    const fitnessFunction = (placement) => this.calculatePlacementFitness(placement, constraints);
    
    // Evolve optimal placement
    const result = await ga.evolve(initialPopulation, fitnessFunction);
    
    return {
      placement: result.bestIndividual,
      fitness: result.bestFitness,
      generations: result.generations,
      convergence: result.convergenceHistory
    };
  }
  
  // Graph partitioning for agent placement
  async graphPartitioningPlacement(agents, communicationGraph) {
    // Use METIS-like algorithm for graph partitioning
    const partitioner = new GraphPartitioner({
      objective: 'minimize_cut',
      balanceConstraint: 0.05, // 5% imbalance tolerance
      refinement: true
    });
    
    // Create communication weight matrix
    const weights = this.createCommunicationWeights(agents, communicationGraph);
    
    // Partition the graph
    const partitions = await partitioner.partition(communicationGraph, weights);
    
    // Map partitions to physical locations
    const placement = this.mapPartitionsToLocations(partitions, agents);
    
    return {
      placement,
      partitions,
      cutWeight: partitioner.getCutWeight(),
      balance: partitioner.getBalance()
    };
  }
}

4. Communication Pattern Optimization

// Advanced communication pattern optimization
class CommunicationOptimizer {
  constructor() {
    this.patternAnalyzer = new PatternAnalyzer();
    this.protocolOptimizer = new ProtocolOptimizer();
    this.messageOptimizer = new MessageOptimizer();
    this.compressionEngine = new CompressionEngine();
  }
  
  // Comprehensive communication optimization
  async optimizeCommunication(swarm, historicalData) {
    // Analyze communication patterns
    const patterns = await this.patternAnalyzer.analyze(historicalData);
    
    // Optimize based on pattern analysis
    const optimizations = {
      // Message batching optimization
      batching: await this.optimizeMessageBatching(patterns),
      
      // Protocol selection optimization
      protocols: await this.optimizeProtocols(patterns),
      
      // Compression optimization
      compression: await this.optimizeCompression(patterns),
      
      // Caching strategies
      ca

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