An enhanced vortex search algorithm based on fluid particle density transfer for global and engineering optimization

Abstract

The vortex search algorithm is a new meta-heuristic algorithm for solving optimization problems. However, it has some disadvantages such as weak stability, tendency to trap into local minima, and high iterations. To overcome these disadvantages, this paper proposes a density transfer vortex search algorithm based on Cauchy distribution and Archimedean spiral surrounding for global and engineering optimization. The algorithm is divided into two populations. In the first population, the mutual action of the Cauchy random step and Gaussian step is introduced to improve the searchability. To improve the exploitation ability of the algorithm in the current optimal solution, an Archimedean spiral surrounding mechanism is introduced in the second population. Moreover, we introduce the vortex flow mechanism, which mimics the physical characteristics of the vortex flow. With the number of iterations increasing, candidate solutions of the first population are gradually affected by the vortex, and their density continuously shifts to the second population. The relationship between the exploration and exploitation in different iteration periods is reasonably adjusted through the density transfer of two populations. In the experiment, the performance of the proposed algorithm and six competitive algorithms are tested on the IEEE CEC2021 global optimization benchmark suite, eight real-world engineering optimizations and a scheduling problem for automated storage and retrieval systems (AS/RS). The experimental results show that the proposed algorithm has significant improvements in convergence precision, speed and stability. The proposed algorithm not only solves complex global optimizations and real-world engineering optimizations with good convergence performance, but also improves efficiency and reduces energy consumption when solving the scheduling problem of AS/RS.