Yukthi Opus: A Multi-Chain Hybrid Metaheuristic for Large-Scale NP-Hard Optimization

📝 Original Info

• Title: Yukthi Opus: A Multi-Chain Hybrid Metaheuristic for Large-Scale NP-Hard Optimization
• ArXiv ID: 2601.01832
• Date: 2026-01-05
• Authors: SB Danush Vikraman, Hannah Abigail, Prasanna Kesavraj, Gajanan V Honnavar

📝 Abstract

We present Yukthi Opus (YO), a three-layer hybrid metaheuristic optimizer that systematically integrates Markov Chain Monte Carlo (MCMC) global exploration, greedy local search, and adaptive simulated annealing with reheating. YO addresses critical gaps in existing optimizers through structured burn-in exploration, blacklist mechanisms preventing revisits to poor regions, adaptive temperature reheating for escaping local minima, and multi-chain parallel execution for robustness. We evaluate YO on three challenging NP-hard benchmarks: the Rastrigin function (5D) with comprehensive ablation studies, the Traveling Salesman Problem (50-200 cities), and the Rosenbrock function (5D) with state-of-the-art comparisons. Results demonstrate that YO reaches competitive or superior solution quality on complex problems while maintaining explicit evaluation budget control. Ablation studies quantify the contributions of each component, revealing that MCMC and greedy search are critical for solution quality (removing either causes 30-36% degradation), while simulated annealing and multi-chain execution

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