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Elite quantum ant colony algorithm based on double chain encoding for static optimization problems
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DOI：10.1007/s10489-025-06958-5.png)
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Abstract
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To address premature convergence, slow convergence, and parameter sensitivity in conventional ant colony algorithms for static optimization, we propose an elite quantum ant colony algorithm based on double chain encoding (DE-QACA). The algorithm employs a sine/cosine, real-valued pheromone representation that explicitly decouples exploration from exploitation. Adaptive quantum rotation angles, triggered by objective improvement, guide search, while a quadratic-decay elite pool mitigates late-stage stagnation and improves robustness to parameter settings. We establish convergence guarantees and derive time/space complexity bounds. Evaluations on Traveling Salesman Problem (TSP) instances and CEC2017 continuous benchmarks show that DE-QACA attains higher success rates on large-scale TSP and converges faster on hybrid functions than competitive baselines, demonstrating fast convergence across discrete and continuous domains.
Keywords:
Elite quantum ant colony algorithm
Static optimization problem
Double chain
Adaptive quantum rotation angles
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