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
  caching: await this.optimizeCaching(patterns),
  
  // Routing optimization
  routing: await this.optimizeMessageRouting(patterns)
};

return optimizations;

}

// Intelligent message batching async optimizeMessageBatching(patterns) { const batchingStrategies = [ new TimeBatchingStrategy(), new SizeBatchingStrategy(), new AdaptiveBatchingStrategy(), new PriorityBatchingStrategy() ];

const evaluations = await Promise.all(
  batchingStrategies.map(strategy => 
    this.evaluateBatchingStrategy(strategy, patterns)
  )
);

const optimal = evaluations.reduce((best, current) => 
  current.score > best.score ? current : best
);

return {
  strategy: optimal.strategy,
  configuration: optimal.configuration,
  expectedImprovement: optimal.improvement,
  metrics: optimal.metrics
};

}

// Dynamic protocol selection async optimizeProtocols(patterns) { const protocols = { tcp: { reliability: 0.99, latency: 'medium', overhead: 'high' }, udp: { reliability: 0.95, latency: 'low', overhead: 'low' }, websocket: { reliability: 0.98, latency: 'medium', overhead: 'medium' }, grpc: { reliability: 0.99, latency: 'low', overhead: 'medium' }, mqtt: { reliability: 0.97, latency: 'low', overhead: 'low' } };

const recommendations = new Map();

for (const [agentPair, pattern] of patterns.pairwisePatterns) {
  const optimal = this.selectOptimalProtocol(protocols, pattern);
  recommendations.set(agentPair, optimal);
}

return recommendations;

} }

MCP Integration Hooks

Topology Management Integration

// Comprehensive MCP topology integration const topologyIntegration = { // Real-time topology optimization async optimizeSwarmTopology(swarmId, optimizationConfig = {}) { // Get current swarm status const swarmStatus = await mcp.swarm_status({ swarmId });

// Analyze current topology performance
const performance = await mcp.performance_report({ format: 'detailed' });

// Identify bottlenecks in current topology
const bottlenecks = await mcp.bottleneck_analyze({ component: 'topology' });

// Generate optimization recommendations
const recommendations = await this.generateTopologyRecommendations(
  swarmStatus, 
  performance, 
  bottlenecks, 
  optimizationConfig
);

// Apply optimization if beneficial
if (recommendations.beneficial) {
  const result = await mcp.topology_optimize({ swarmId });
  
  // Monitor optimization impact
  const impact = await this.monitorOptimizationImpact(swarmId, result);
  
  return {
    applied: true,
    recommendations,
    result,
    impact
  };
}

return {
  applied: false,
  recommendations,
  reason: 'No beneficial optimization found'
};

},

// Dynamic swarm scaling with topology consideration async scaleWithTopologyOptimization(swarmId, targetSize, workloadProfile) { // Current swarm state const currentState = await mcp.swarm_status({ swarmId });

// Calculate optimal topology for target size
const optimalTopology = await this.calculateOptimalTopologyForSize(
  targetSize, 
  workloadProfile
);

// Plan scaling strategy
const scalingPlan = await this.planTopologyAwareScaling(
  currentState,
  targetSize,
  optimalTopology
);

// Execute scaling with topology optimization
const scalingResult = await mcp.swarm_scale({ 
  swarmId, 
  targetSize 
});

// Apply topology optimization after scaling
if (scalingResult.success) {
  await mcp.topology_optimize({ swarmId });
}

return {
  scalingResult,
  topologyOptimization: scalingResult.success,
  finalTopology: optimalTopology
};

},

// Coordination optimization async optimizeCoordination(swarmId) { // Analyze coordination patterns const coordinationMetrics = await mcp.coordination_sync({ swarmId });

// Identify coordination bottlenecks
const coordinationBottlenecks = await mcp.bottleneck_analyze({ 
  component: 'coordination' 
});

// Optimize coordination patterns
const optimization = await this.optimizeCoordinationPatterns(
  coordinationMetrics,
  coordinationBottlenecks
);

return optimization;

} };

Neural Network Integration

// AI-powered topology optimization class NeuralTopologyOptimizer { constructor() { this.models = { topology_predictor: null, performance_estimator: null, pattern_recognizer: null }; }

// Initialize neural models async initializeModels() { // Load pre-trained models or train new ones this.models.topology_predictor = await mcp.model_load({ modelPath: '$models$topology_optimizer.model' });

this.models.performance_estimator = await mcp.model_load({ 
  modelPath: '$models$performance_estimator.model' 
});

this.models.pattern_recognizer = await mcp.model_load({ 
  modelPath: '$models$pattern_recognizer.model' 
});

}

// AI-powered topology prediction async predictOptimalTopology(swarmState, workloadProfile) { if (!this.models.topology_predictor) { await this.initializeModels(); }

// Prepare input features
const features = this.extractTopologyFeatures(swarmState, workloadProfile);

// Predict optimal topology
const prediction = await mcp.neural_predict({
  modelId: this.models.topology_predictor.id,
  input: JSON.stringify(features)
});

return {
  predictedTopology: prediction.topology,
  confidence: prediction.confidence,
  expectedImprovement: prediction.improvement,
  reasoning: prediction.reasoning
};

}

// Train topology optimization model async trainTopologyModel(trainingData) { const trainingConfig = { pattern_type: 'optimization', training_data: JSON.stringify(trainingData), epochs: 100 };

const trainingResult = await mcp.neural_train(trainingConfig);

// Save trained model
if (trainingResult.success) {
  await mcp.model_save({
    modelId: trainingResult.modelId,
    path: '$models$topology_optimizer.model'
  });
}

return trainingResult;

} }

Advanced Optimization Algorithms

1. Genetic Algorithm for Topology Evolution

// Genetic algorithm implementation for topology optimization class GeneticTopologyOptimizer { constructor(config = {}) { this.populationSize = config.populationSize || 50; this.mutationRate = config.mutationRate || 0.1; this.crossoverRate = config.crossoverRate || 0.8; this.maxGenerations = config.maxGenerations || 100; this.eliteSize = config.eliteSize || 5; }

// Evolve optimal topology async evolve(initialTopologies, fitnessFunction, constraints) { let population = initialTopologies; let generation = 0; let bestFitness = -Infinity; let bestTopology = null;

const convergenceHistory = [];

while (generation < this.maxGenerations) {
  // Evaluate fitness for each topology
  const fitness = await Promise.all(
    population.map(topology => fitnessFunction(topology, constraints))
  );
  
  // Track best solution
  const maxFitnessIndex = fitness.indexOf(Math.max(...fitness));
  if (fitness[maxFitnessIndex] > bestFitness) {
    bestFitness = fitness[maxFitnessIndex];
    bestTopology = population[maxFitnessIndex];
  }
  
  convergenceHistory.push({
    generation,
    bestFitness,
    averageFitness: fitness.reduce((a, b) => a + b) / fitness.length
  });
  
  // Selection
  const selected = this.selection(population, fitness);
  
  // Crossover
  const offspring = await this.crossover(selected);
  
  // Mutation
  const mutated = await this.mutation(offspring, constraints);
  
  // Next generation
  population = this.nextGeneration(population, fitness, mutated);
  generation++;
}

return {
  bestTopology,
  bestFitness,
  generation,
  convergenceHistory
};

}

// Topology crossover operation async crossover(parents) { const offspring = [];

for (let i = 0; i < parents.length - 1; i += 2) {
  if (Math.random() < this.crossoverRate) {
    const [child1, child2] = await this.crossoverTopologies(
      parents[i], 
      parents[i + 1]
    );
    offspring.push(child1, child2);
  } else {
    offspring.push(parents[i], parents[i + 1]);
  }
}

return offspring;

}

// Topology mutation operation async mutation(population, constraints) { return Promise.all( population.map(async topology => { if (Math.random() < this.mutationRate) { return await this.mutateTopology(topology, constraints); } return topology; }) ); } }

2. Simulated Annealing for Topology Optimization

// Simulated annealing implementation class SimulatedAnnealingOptimizer { constructor(config = {}) { this.initialTemperature = config.initialTemperature || 1000; this.coolingRate = config.coolingRate || 0.95; this.minTemperature = config.minTemperature || 1; this.maxIterations = config.maxIterations || 10000; }

// Simulated annealing optimization async optimize(initialTopology, objectiveFunction, constraints) { let currentTopology = initialTopology; let currentScore = await objectiveFunction(currentTopology, constraints);

let bestTopology = currentTopology;
let bestScore = currentScore;

let temperature = this.initialTemperature;
let iteration = 0;

const history = [];

while (temperature > this.minTemperature && iteration < this.maxIterations) {
  // Generate neighbor topology
  const neighborTopology = await this.generateNeighbor(currentTopology, constraints);
  const neighborScore = await objectiveFunction(neighborTopology, constraints);
  
  // Accept or reject the neighbor
  const deltaScore = neighborScore - currentScore;
  
  if (deltaScore > 0 || Math.random() < Math.exp(deltaScore / temperature)) {
    currentTopology = neighborTopology;
    currentScore = neighborScore;
    
    // Update best solution
    if (neighborScore > bestScore) {
      bestTopology = neighborTopology;
      bestScore = neighborScore;
    }
  }
  
  // Record history
  history.push({
    iteration,
    temperature,
    currentScore,
    bestScore
  });
  
  // Cool down
  temperature *= this.coolingRate;
  iteration++;
}

return {
  bestTopology,
  bestScore,
  iterations: iteration,
  history
};

}

// Generate neighbor topology through local modifications async generateNeighbor(topology, constraints) { const modifications = [ () => this.addConnection(topology, constraints), () => this.removeConnection(topology, constraints), () => this.modifyConnection(topology, constraints), () => this.relocateAgent(topology, constraints) ];

const modification = modifications[Math.floor(Math.random() * modifications.length)];
return await modification();

} }

Operational Commands

Topology Optimization Commands

Analyze current topology

npx claude-flow topology-analyze --swarm-id <id> --metrics performance

Optimize topology automatically

npx claude-flow topology-optimize --swarm-id <id> --strategy adaptive

Compare topology configurations

npx claude-flow topology-compare --topologies ["hierarchical", "mesh", "hybrid"]

Generate topology recommendations

npx claude-flow topology-recommend --workload-profile <file> --constraints <file>

Monitor topology performance

npx claude-flow topology-monitor --swarm-id <id> --interval 60

Agent Placement Commands

Optimize agent placement

npx claude-flow placement-optimize --algorithm genetic --agents <agent-list>

Analyze placement efficiency

npx claude-flow placement-analyze --current-placement <config>

Generate placement recommendations

npx claude-flow placement-recommend --communication-patterns <file>

Integration Points

With Other Optimization Agents

• Load Balancer: Coordinates topology changes with load distribution

• Performance Monitor: Receives topology performance metrics

• Resource Manager: Considers resource constraints in topology decisions

With Swarm Infrastructure

• Task Orchestrator: Adapts task distribution to topology changes

• Agent Coordinator: Manages agent connections during topology updates

• Memory System: Stores topology optimization history and patterns

Performance Metrics

Topology Performance Indicators

// Comprehensive topology metrics const topologyMetrics = { // Communication efficiency communicationEfficiency: { latency: this.calculateAverageLatency(), throughput: this.calculateThroughput(), bandwidth_utilization: this.calculateBandwidthUtilization(), message_overhead: this.calculateMessageOverhead() },

// Network topology metrics networkMetrics: { diameter: this.calculateNetworkDiameter(), clustering_coefficient: this.calculateClusteringCoefficient(), betweenness_centrality: this.calculateBetweennessCentrality(), degree_distribution: this.calculateDegreeDistribution() },

// Fault tolerance faultTolerance: { connectivity: this.calculateConnectivity(), redundancy: this.calculateRedundancy(), single_point_failures: this.identifySinglePointFailures(), recovery_time: this.calculateRecoveryTime() },

// Scalability metrics scalability: { growth_capacity: this.calculateGrowthCapacity(), scaling_efficiency: this.calculateScalingEfficiency(), bottleneck_points: this.identifyBottleneckPoints(), optimal_size: this.calculateOptimalSize() } };

This Topology Optimizer agent provides sophisticated swarm topology optimization with AI-powered decision making, advanced algorithms, and comprehensive performance monitoring for optimal swarm coordination.