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.
