Metaheuristic algorithm parameters selection for building an optimal hierarchical structure of a control system: a case study

TL;DR

This paper investigates how to select optimal parameters for a metaheuristic algorithm, specifically a modified ant colony algorithm, to effectively design hierarchical control system structures in industrial settings.

Contribution

It provides insights and recommendations on parameter tuning for a modified ant colony algorithm to improve convergence in industrial control system optimization.

Findings

Parameter selection significantly affects algorithm convergence.

Guidelines for tuning the ant colony algorithm are proposed.

Results are applicable to other combinatorial optimization problems.

Abstract

Metaheuristic algorithms are currently widely used to solve a variety of optimization problems across various industries. This article discusses the application of a metaheuristic algorithm to optimize the hierarchical architecture of an industrial distributed control system. The success of the algorithm depends largely on the choice of starting conditions and algorithm parameters. We examine the impact of parameter selection on the convergence of a modified ant colony algorithm and provide recommendations for tuning the algorithm to achieve optimal results for a specific industrial problem. The findings presented in this article can also be applied to other combinatorial optimization problems.