A Modified Vortex Search Algorithm for Numerical Function Optimization

TL;DR

This paper introduces a modified Vortex Search algorithm that enhances global optimization by generating candidate solutions around multiple points, overcoming local minima trapping and outperforming several existing algorithms on benchmark functions.

Contribution

The paper proposes a modified Vortex Search algorithm that improves global search capability by generating solutions around multiple points, addressing local minima trapping issues.

Findings

MVS outperforms original VS, PSO2011, and ABC algorithms on benchmark functions.

The modification enhances the algorithm's ability to escape local minima.

Experimental results demonstrate improved convergence and solution quality.

Abstract

The Vortex Search (VS) algorithm is one of the recently proposed metaheuristic algorithms which was inspired from the vortical flow of the stirred fluids. Although the VS algorithm is shown to be a good candidate for the solution of certain optimization problems, it also has some drawbacks. In the VS algorithm, candidate solutions are generated around the current best solution by using a Gaussian distribution at each iteration pass. This provides simplicity to the algorithm but it also leads to some problems along. Especially, for the functions those have a number of local minimum points, to select a single point to generate candidate solutions leads the algorithm to being trapped into a local minimum point. Due to the adaptive step-size adjustment scheme used in the VS algorithm, the locality of the created candidate solutions is increased at each iteration pass. Therefore, if the algorithm cannot escape a local point as quickly as possible, it becomes much more difficult for the algorithm to escape from that point in the latter iterations. In this study, a modified Vortex Search algorithm (MVS) is proposed to overcome above mentioned drawback of the existing VS algorithm. In the MVS algorithm, the candidate solutions are generated around a number of points at each iteration pass. Computational results showed that with the help of this modification the global search ability of the existing VS algorithm is improved and the MVS algorithm outperformed the existing VS algorithm, PSO2011 and ABC algorithms for the benchmark numerical function set.