Ant Colony Optimization (ACO) is a prominent swarm intelligence algorithm extensively applied to path planning. However, traditional ACO methods often exhibit shortcomings, such as blind search behavior and slow convergence within complex environments. To address these challenges, this paper proposes the Pheromone-Focused Ant Colony Optimization (PFACO) algorithm, which introduces three key strategies to enhance the problem-solving ability of the ant colony. First, the initial pheromone distribution is concentrated in more promising regions based on the Euclidean distances of nodes to the start and end points, balancing the trade-off between exploration and exploitation. Second, promising solutions are reinforced during colony iterations to intensify pheromone deposition along high-quality paths, accelerating convergence while maintaining solution diversity. Third, a forward-looking mechanism is implemented to penalize redundant path turns, promoting smoother and more efficient solutions. These strategies collectively produce the focused pheromones to guide the ant colony's search, which enhances the global optimization capabilities of the PFACO algorithm, significantly improving convergence speed and solution quality across diverse optimization problems. The experimental results demonstrate that PFACO consistently outperforms comparative ACO algorithms in terms of convergence speed and solution quality.

翻译：蚁群优化（ACO）是一种广泛应用于路径规划的著名群体智能算法。然而，传统ACO方法在复杂环境中常表现出盲目搜索行为、收敛速度慢等不足。为解决这些问题，本文提出信息素聚焦蚁群优化（PFACO）算法，通过引入三项关键策略增强蚁群的问题求解能力。首先，基于节点到起止点的欧氏距离将初始信息素集中在更有潜力的区域，平衡探索与利用之间的权衡。其次，在群体迭代过程中强化有潜力的解，沿高质量路径增强信息素沉积，在保持解多样性的同时加速收敛。第三，引入前瞻机制对冗余路径转向进行惩罚，从而获得更平滑高效的解。这些策略共同产生聚焦信息素以引导蚁群搜索，增强了PFACO算法的全局优化能力，显著提升了各类优化问题的收敛速度和解质量。实验结果表明，PFACO在收敛速度和解质量方面均持续优于对比的ACO算法。