Ant colony resource optimization for Industrial IoT and CPS

In the wave of industrial modernization, a concept that comprehensively covers the product lifecycle has been proposed, namely the digital twin manufacturing system. The digital twin manufacturing system can conduct three-dimensional simulation of the workshop, thereby achieving dynamic scheduling and energy efficiency optimization of the workshop. The optimization of digital twin manufacturing systems has become a focus of research. In order to reduce power consumption and production time in manufacturing workshops, the study adopted a non-dominated sorting genetic algorithm to improve its elitist retention strategy for the problem of easily falling into local optima. On the ground of the idea of multi-objective optimization, the optimization was carried out with the production time and power consumption of the manufacturing industry as the objectives. The experiment showcased that the improved algorithm outperforms the multi-objective optimization algorithm on the ground of decomposition and the evolutionary algorithm on the ground of Pareto dominance. Compared to the two comparison algorithms, the production time optimization effect and power consumption optimization effect of different numbers of devices were 11.12%-21.37% and 2.14%-6.89% higher, respectively. The optimization time of the improved algorithm was 713.5 seconds, that was reduced by 173.8 seconds and 179.8 seconds compared to the other two algorithms, respectively. The total power consumption of the improved optimization model was 2883.7kWs, which was reduced by 32.0kW*s and 45.5kW*s compared to the other two algorithms, respectively. This study proposed a new multi-objective optimization algorithm for the current digital twin manufacturing industry. This algorithm effectively reduces production time and power consumption, and has important guiding significance for manufacturing system optimization in actual production environments.

Section: Related Workmentioning

confidence: 95%

“…indicates that workpiece i is not processed on device K. For multi-objective optimization problems, they can be represented as shown in Formula (13).…”

Section:  mentioning

confidence: 99%

Optimization Method for Digital Twin Manufacturing System Based on NSGA-II

Ding,

2024

“… Reliability: IoT platforms, applications, and systems must be fully operational, aggregate data from multiple sources, and rely on reliable communication protocols. Constraint nodes should be able to respond quickly to an emergency at any time [27].…”

Section: A Iot Challengesmentioning

confidence: 99%

An Energy‐Aware Technique for Task Allocation in the Internet of Things using Krill Herd Algorithm

Miao¹,

Xu²,

Chen³

et al. 2023

The Internet of Things (IoT) is an innovative technology that connects the digital and physical worlds as well as allows physical devices capable of different capacities to share resources to accomplish tasks. Most IoT objects have limited battery life and are heterogeneous. Assignment of these objects is, therefore, extremely challenging. Energy consumption and reliability are the primary objectives of task allocation algorithms. We present an optimization solution to the IoT task allocation problem based on the krill herd algorithm. The algorithm increases the energy efficiency and stability of the network while providing a reliable task allocation solution. An extensive test of the proposed algorithm has been conducted using the MATLAB simulator. Compared to the most relevant method in the literature, our algorithm provides a higher level of energy efficiency.

“…An evaporation rate reduces all pheromone trails over time. Meanwhile, evaporation presents an opportunity for exploration and minimizes local stalling [27,28].…”

Section: Energy Consumptionmentioning

confidence: 99%

Energy-Aware Clustering in the Internet of Things using Tabu Search and Ant Colony Optimization Algorithms

Li,

2023

The Internet of Things (IoT) significantly impacts communication systems' efficiency and the requirements for applications in our daily lives. Among the major challenges involved in data transmission over IoT networks is the development of an energy-efficient clustering mechanism. Recent methods are challenged by long transmission delays, imbalanced load distribution, and limited network lifespan. This paper suggests a new cluster-based routing method combining Tabu Search (TS) and Ant Colony Optimization (ACO) algorithms. The TS algorithm overcomes the disadvantage of ACO, in which ants move randomly throughout the colony in search of food sources. In the process of solving optimization problems, the ACO algorithm traps ants, resulting in a considerable increase in the time required for local searches. TS can be used to overcome these drawbacks. In fact, the TS algorithm eliminates the problem of getting stuck in local optima due to the randomness of the search process. Experimental results indicate that the proposed hybrid algorithm outperforms ACO, LEACH, and genetic algorithms regarding energy consumption and network lifetime.