applied sciences Article  
Semantic-Based Multi-Objective Optimization for QoS and Energy Efficiency in IoT, Fog, and Cloud ERP Using Dynamic Cooperative NSGA-II  
[PERSON]\({}^{\copyright}\) and [PERSON],* \({}^{\copyright}\)  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 3: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 4: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 5: [[https://www.mdpi.com/journal](https://www.mdpi.com/journal)]([https://www.mdpi.com/journal](https://www.mdpi.com/journal))  
+++ ==WARNING: Truncated because of repetitions==
/applsci  
++  
For Enterprise Resource Planning (ERP) [1], the Industrial Internet of Things (IIoT) is crucial as it can provide support for widespread business tasks, including smart tracking items, a smart Cloud ERP system using warehouse robotics and drone deliveries, and so on. With the fast advances of Cloud computing and IIoT, we have witnessed many paradigms to assure the quality and safety of many business tasks. Those solutions provide essential advantages for significant quality enhancement of business outputs and services. Moreover, Cloud and IIoT can provide smart and flexible ERP deliveries, which have become a great shift for Small and Medium Enterprises (SMEs). Nevertheless, the difficulty lies in mastering the evolution of customer needs as well as the considerable growth in the number of IoT devices that include heterogeneous smart sensors. These devices significantly increase the amount of data exposed to processing. Therefore, it is crucial for ERP when managing an increased number of IoT devices, including CPU load, latency, bandwidth, and energy. On the other hand, a great deal of network latency and congestion is managed through Fog computing. This is a new computing model, which is placed between the Cloud and IoT devices, and is a very important solution for facilitating the deployment of relevant IoT services. Furthermore, it is essential to know that the selection of relevant services is not a standardized solution. It can vary according to specific enterprise needs [2]. To reach the goal, more advanced and intelligent techniques might be used via the exploitation of additional semantic information to effectively select and compose services among a large number of service candidates. To this end, researchers shift out to support intelligent QoS-aware service selection and composition approaches as a solution that could make a significant contribution to deal with a wide range of IoT, Fog, and Cloud services. Furthermore, selecting and composing services can be achieved in three main ways: (1) ontologies [3; 4; 5; 6; 7; 8; 9], (2) metaheuristic [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21], and (3) IoT-Fog enabled services [22; 23]. Each one of them has its advantages as well as its drawbacks. Recently, [PERSON] et al. [24] proposed an approach to estimate the QoS of composite services based on intuitionistic fuzzy sets. The importance of the proposed approach is due to the fact that it can be applied to all types of service systems. What can be deduced from techniques mentioned is that none of them can achieve an optimum consensus between QoS, energy, and execution time. These techniques are not efficient and flexible enough to support service failures, and none of them can achieve an optimum consensus between service quality, service speed, service cost, continuous changes of customer's needs, limited device energy, etc.