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<p>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.</p>

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Elite quantum ant colony algorithm based on double chain encoding for static optimization problems

  • 期刊论文

  • 发表日期：

    2025年11月21日

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  • Xiaowei Fu,
  • Hao Zhao,
  • Huanyu Li,
  • Yiming Sun

摘要

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.