The experimental results show that the proposed TLPFA algorithm is superior to the state-of-the-art metaheuristic algorithms in terms of the performance measures. 19 benchmark functions of four different types and six engineering design problems are used to test of the TLPFA exploration and exploiting capabilities. Therefore, a teaching-learning-based pathfinder algorithm (TLPFA) is proposed. In order to further enhance the depth search ability of the algorithm and increase the convergence speed, the exponential step is given to the followers. In order to balance the exploration and mining capabilities of the algorithm, the learning phase of the teaching and learning algorithm is added to the follower phase in the article.
In order to solve this problem, the teaching phase in the teaching and learning algorithm is added to the pathfinder stage in the text. However, the original algorithm also has the problem of falling into a local optimum. They represent the exploration phase and mining phase of PFA respectively. PFA is divided into two stages to search: pathfinder stage and follower stage. Pathfinder algorithm (PFA) for finding the best food area or prey based on the leadership of collective action in animal groups is a new metaheuristic algorithm for solving optimization problems with different structures.
0 kommentar(er)
