Abstract
Emerging commucation technologies, such as mobile edge computing (MEC), Internet of Things (IoT), and fifth-generation (5G) broadband cellular networks, have recently drawn a great deal of interest. Therefore, numerous multiobjective optimization problems (MOOP) associated with the aforementioned technologies have arisen, for example, energy consumption, cost-effective edge user allocation (EUA), and efficient scheduling. Accordingly, the formularization of these problems through fuzzy relation equations (FRE) should be taken into consideration as a capable approach to achieving an optimized solution. In this paper, a modified technique based on a genetic algorithm (GA) to solve MOOPs, which are formulated by fuzzy relation constraints with s -norm, is proposed. In this method, firstly, some techniques are utilized to reduce the size of the problem, so that the reduced problem can be solved easily. The proposed GA-based technique is then applied to solve the reduced problem locally. The most important advantage of this method is to solve a wide variety of MOOPs in the field of IoT, EC, and 5G. Furthermore, some numerical experiments are conducted to show the capability of the proposed technique. Not only does this method overcome the weaknesses of conventional methods owing to its potentials in the nonconvex feasible domain, but it also is useful to model complex systems.
Highlights
Fuzzy relation equations (FRE) theory has a large number of features making it capable of formulating uncertain information and nonlinear functions for complex systems [1,2,3]
There are many problems in different fields of studies based on communication technologies which can be Mathematical Problems in Engineering solved by FRE, such as the instructional information resources allocation, optimizing the size and performance of the microstrip components, the fifth-generation (5G) of broadband cellular networks, Internet of ings (IoT), could computing, and mobile edge computing (MEC), and a large number of engineering systems [1, 2, 4,5,6,7,8,9]
As technologies and new computational techniques based on fuzzy systems advance, we have faced a large number of issues that can be described by multiobjective optimization problems (MOOP) [10,11,12,13,14]
Summary
Fuzzy relation equations (FRE) theory has a large number of features making it capable of formulating uncertain information and nonlinear functions for complex systems [1,2,3]. As technologies and new computational techniques based on fuzzy systems advance, we have faced a large number of issues that can be described by multiobjective optimization problems (MOOP) [10,11,12,13,14]. Deploying 5G networks, which are integrated mMIMO technology for wireless application, needs the synchronization of a couple of different objectives and variables to reach an optimized operation and performance [24] It is an obvious example of MOOP that could be solved using the proposed method. Densification is a paradigm of 5G that can be described based on the fuzzy MOOP It relates to facilitating the conditions, in which the provision of services is improved to predict a large number of user equipment in the future for IoT applications. No utility function of the Pareto set is used to determine the optimal solution
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