Sustainable and Energy-Efficient Industrial Control System: Comparative Analysis of Swarm and Evolutionary Algorithms

The energy consumption of Industrial Control System (ICS) environments is a critical concern, impacting their overall efficiency and sustainability. This study presents a comprehensive approach to optimizing the energy consumption in an ICS environment by leveraging three different optimization algorithms: Grey Wolf Optimization (GWO), Harris Hawk Optimization (HHO), and Genetic Algorithm (GA). The ICS environment is modeled with various components, including sensors, Programmable Logic Controllers (PLCs), Supervisory Control and Data Acquisition (SCADA) systems, and more. The energy consumption of each component is formulated as a mathematical function, and the goal is to minimize the total energy consumption by optimizing the parameters of these components. The three optimization algorithms are implemented, and their performance is compared regarding the final energy consumption achieved. The results show that all three algorithms effectively reduce energy consumption compared to the initial, unoptimized scenario. The GWO algorithm demonstrates the best performance, followed by the HHO and GA algorithms. The study provides valuable insights into applying swarm intelligence and evolutionary computation techniques for energy optimization in ICS environments. The findings can inform the design and operation of ICS systems, contributing to improved energy efficiency and sustainability in industrial settings.