SKILL.md
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 60Agent 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.