A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy

Abstract

In this study, a particle swarm optimization (PSO) algorithm with a negative gradient perturbation and binary tree depth‐first strategy (GB‐PSO) is proposed. The negative gradient term accelerates particle optimization in the direction of decreasing the objective function value. To calculate the step size of this gradient term more easily, a method based on the ratio was proposed. In addition, a new PSO strategy is also proposed. Each iteration of PSO yields not only the current optimal solution of the group, but also the solution based on the 2‐norm maximum. Under the current iteration solution of PSO, these two solutions are the children nodes. In the sense of the binary tree concept, the three solutions constitute the father‐son relationship, and the solution generated throughout the entire search process constitutes the binary tree. PSO uses a traceable depth‐first strategy to determine the optimal solution. Compared with the linear search strategy adopted by several algorithms, it can fully utilize the useful information obtained during the iterative process, construct a variety of particle swarm search paths, and prevent premature and enhance global optimization. The experimental results show that the algorithm outperforms some state‐of‐the‐art PSO algorithms in terms of search performance.