Yukthi Opus: A Multi-Chain Hybrid Metaheuristic for Large-Scale NP-Hard Optimization
SB Danush Vikraman, Hannah Abigail, Prasanna Kesavraj, and Gajanan V Honnavar

TL;DR
Yukthi Opus is a novel multi-chain hybrid metaheuristic designed for large-scale NP-hard optimization problems, combining exploration, exploitation, and adaptive mechanisms to improve solution quality and stability within evaluation constraints.
Contribution
It introduces a structured two-phase architecture with MCMC, greedy local search, and simulated annealing, along with a multi-chain strategy and spatial blacklist to enhance robustness and efficiency.
Findings
YO performs competitively on large, multimodal problems.
MCMC exploration and greedy refinement are key for solution quality.
Simulated annealing and multi-chain execution improve stability and reduce variance.
Abstract
We present Yukthi Opus (YO), a multi-chain hybrid metaheuristic designed for NP-hard optimization under explicit evaluation budget constraints. YO integrates three complementary mechanisms in a structured two-phase architecture: Markov Chain Monte Carlo (MCMC) for global exploration, greedy local search for exploitation, and simulated annealing with adaptive reheating to enable controlled escape from local minima. A dedicated burn-in phase allocates evaluations to probabilistic exploration, after which a hybrid optimization loop refines promising candidates. YO further incorporates a spatial blacklist mechanism to avoid repeated evaluation of poor regions and a multi-chain execution strategy to improve robustness and reduce sensitivity to initialization. We evaluate YO on three benchmarks: the Rastrigin function (5D) with ablation studies, the Traveling Salesman Problem with 50 to 200…
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Taxonomy
TopicsAdvanced Multi-Objective Optimization Algorithms · Risk and Portfolio Optimization · Metaheuristic Optimization Algorithms Research
