Pathfinder algorithm (PFA) is a swarm intelligent optimization algorithm inspired by the collective activity behavior of swarm animals, imitating the leader in the population to guide followers in finding the best food source. This algorithm has the characteristics of a simple structure and high performance. However, PFA faces challenges such as insufficient population diversity and susceptibility to local optima due to its inability to effectively balance the exploration and exploitation capabilities. This paper proposes an Ameliorated Pathfinder Algorithm called APFA to solve complex engineering optimization problems. Firstly, a guidance mechanism based on multiple elite individuals is presented to enhance the global search capability of the algorithm. Secondly, to improve the exploration efficiency of the algorithm, the Logistic chaos mapping is introduced to help the algorithm find more high-quality potential solutions while avoiding the worst solutions. Thirdly, a comprehensive following strategy is designed to avoid the algorithm falling into local optima and further improve the convergence speed. These three strategies achieve an effective balance between exploration and exploitation overall, thus improving the optimization performance of the algorithm. In performance evaluation, APFA is validated by the CEC2022 benchmark test set and five engineering optimization problems, and compared with the state-of-the-art metaheuristic algorithms. The numerical experimental results demonstrated the superiority of APFA.

探路者算法（PFA）是一种群体智能优化算法，其灵感来自群体动物的集体活动行为，模仿群体中的领导者来引导追随者寻找最佳食物源。该算法具有结构简单、性能较高的特点。然而，PFA由于无法有效平衡勘探与开采能力，面临着种群多样性不足、易受局部最优等挑战。本文提出了一种称为 APFA 的改进探路者算法来解决复杂的工程优化问题。首先，提出基于多个精英个体的引导机制，增强算法的全局搜索能力。其次，为了提高算法的探索效率，引入了Logistic混沌映射，帮助算法找到更多高质量的潜在解，同时避免出现最差解。再次，设计了综合跟随策略，避免算法陷入局部最优，进一步提高收敛速度。这三种策略总体上实现了探索与利用之间的有效平衡，从而提高了算法的优化性能。在性能评估中，APFA通过CEC2022基准测试集和五个工程优化问题进行验证，并与最先进的元启发式算法进行比较。数值实验结果证明了APFA的优越性。