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Bio-Inspired Optimization Algorithm with Quantum Annealing Hybridization

Jincheng Zhang

Published Sep 2, 2026• 0 citations
Publisher

Abstract

This paper explores a novel hybrid optimization approach combining the strengths of bio-inspired algorithms, specifically Ant Colony Optimization (ACO), with Quantum Annealing (QA). Traditional optimization techniques often struggle with escaping local optima, leading to suboptimal solutions. This research posits that integrating ACO for initial solution generation and leveraging QA for subsequent exploration within the solution space can overcome these limitations. The core mechanism involves ACO producing an initial set of candidate solutions, followed by QA's quantum tunneling effect to navigate the solution landscape and escape local minima, ultimately converging towards a global optimum. The effectiveness of this hybridization is demonstrated through a theoretical framework and conceptual design. The primary goal is to demonstrate a pathway toward more robust and efficient optimization, particularly for complex, high-dimensional problems. This work lays the groundwork for future research and development in this promising area.

DOI
10.5281/zenodo.22248317

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