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<p>The ant colony optimization (ACO) algorithm is commonly used for path optimization to reduce the machining routes, thereby improving the drilling efficiency of machine tools. However, when addressing hole swarm planning problems, such as a tendency to converge to local optima and a significant decrease in convergence speed when approaching the optimal solution, frequently arise. In this study, we propose an optimization strategy for ACO to improve the performance of the ACO algorithm by updating the stochastic pheromone and increasing the cyclic initial state. Algorithm optimization and experiments are carried out, and the efficiency of the optimized algorithm is significantly improved compared with three popular ACO based algorithms, including the elitist ant system (AS), the max–min AS, and the rank-based AS. Results show that the proposed method can find the better path planning than the original versions with similar or even less iteration steps. The experiment conducted on the different ACO variants demonstrates that this strategy exhibits good performance and generalization. Therefore, the improved algorithm can be more efficiently utilized in automatic path planning for hole swarm in machining than the original method.</p>

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Improved strategy of ant colony optimization for path planning via stochastic pheromone updating and cyclic initialization

  • 期刊论文

  • 发表日期：

    2025年3月31日

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  • Shengkun Fang,
  • Zhiwen Deng,
  • Ping Li,
  • Danfeng Long

摘要

The ant colony optimization (ACO) algorithm is commonly used for path optimization to reduce the machining routes, thereby improving the drilling efficiency of machine tools. However, when addressing hole swarm planning problems, such as a tendency to converge to local optima and a significant decrease in convergence speed when approaching the optimal solution, frequently arise. In this study, we propose an optimization strategy for ACO to improve the performance of the ACO algorithm by updating the stochastic pheromone and increasing the cyclic initial state. Algorithm optimization and experiments are carried out, and the efficiency of the optimized algorithm is significantly improved compared with three popular ACO based algorithms, including the elitist ant system (AS), the max–min AS, and the rank-based AS. Results show that the proposed method can find the better path planning than the original versions with similar or even less iteration steps. The experiment conducted on the different ACO variants demonstrates that this strategy exhibits good performance and generalization. Therefore, the improved algorithm can be more efficiently utilized in automatic path planning for hole swarm in machining than the original method.