Journal of Bionic Engineering (2024) 21:1592–1616 https://doi.org/10.1007/s42235-024-00510-w 

APFA: Ameliorated Pathfnder Algorithm for Engineering Applications

Keyu Zhong1,2 · Fen Xiao3 · Xieping Gao1,4

1 Key Laboratory of Computing and Stochastic Mathematics of Ministry of Education, Hunan Normal University, Changsha 410081, China

2 School of Mathematics and Statistics, Hunan Normal University, Changsha 410081, China

3 School of Computer Science, Xiangtan University, Xiangtan 411105, China

4 Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China

Abstract Pathfnder 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 fnding the best food source. This algorithm has the characteristics of a simple structure and high performance. However, PFA faces challenges such as insufcient population diversity and susceptibility to local optima due to its inability to efectively balance the exploration and exploitation capabilities. This paper proposes an Ameliorated Pathfnder 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 efciency of the algorithm, the Logistic chaos mapping is introduced to help the algorithm fnd 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 efective 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 fve engineering optimization problems, and compared with the state-of-the-art metaheuristic algorithms. The numerical experimental results demonstrated the superiority of APFA.

Keywords Pathfnder algorithm · Swarm intelligent · Metaheuristic · Engineering problems