52 lines
1.7 KiB
TypeScript
52 lines
1.7 KiB
TypeScript
/**
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* Self-Organizing HNSW Analysis
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*
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* Based on: hnsw-self-organizing.md
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* Simulates autonomous graph restructuring, adaptive parameter tuning,
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* dynamic topology evolution, and self-healing mechanisms in HNSW indexes.
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*
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* Research Foundation:
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* - Autonomous graph restructuring (MPC-based control)
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* - Adaptive parameter tuning (online learning)
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* - Dynamic topology evolution
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* - Self-healing mechanisms for deletion artifacts
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*/
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import type { SimulationScenario } from '../../types';
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export interface SelfOrganizingMetrics {
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degradationPrevention: number;
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adaptationSpeed: number;
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autonomyScore: number;
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optimalMFound: number;
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optimalEfConstructionFound: number;
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parameterStability: number;
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initialLatencyP95Ms: number;
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day30LatencyP95Ms: number;
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latencyImprovement: number;
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fragmentationRate: number;
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healingTimeMs: number;
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postHealingRecall: number;
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memoryOverhead: number;
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cpuOverheadPercent: number;
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energyEfficiency: number;
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}
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export interface AdaptationStrategy {
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name: 'static' | 'mpc' | 'online-learning' | 'evolutionary' | 'hybrid';
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parameters: {
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horizon?: number;
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learningRate?: number;
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mutationRate?: number;
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};
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}
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/**
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* Self-Organizing HNSW Scenario
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*
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* This simulation:
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* 1. Tests autonomous graph restructuring under workload shifts
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* 2. Compares static vs self-organizing HNSW performance
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* 3. Analyzes adaptive parameter tuning effectiveness
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* 4. Measures self-healing from deletion artifacts
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* 5. Evaluates long-term stability and efficiency
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*/
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export declare const selfOrganizingHNSWScenario: SimulationScenario;
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export default selfOrganizingHNSWScenario;
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//# sourceMappingURL=self-organizing-hnsw.d.ts.map
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