Genetic algorithm‐based content distribution strategy for F‐
            <scp>RAN</scp>
            architectures

Li, Xujie; Wang, Ziya; Sun, Ying; Zhou, Siyuan; Xu, Yanli; Tan, Guoping

doi:10.4218/etrij.2018-0254

Cited by 6 publications

(4 citation statements)

References 32 publications

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“…Some new individuals and parents are selected based on the improved fitness function and selection scheme. Then, two individuals are randomly selected from the parents for crossover or mutation [37] with probabilities

p_c

and

p_m

. Repeat the above operations, and stop the iteration when the convergence condition is reached.…”

Section: Joint Optimization Algorithmmentioning

confidence: 99%

Joint AP‐user association and caching strategy for delivery delay minimization in cell‐free massive MIMO systems

Wang,

Shen,

et al. 2023

IET Communications

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Edge caching at access points (APs) is a promising approach to alleviate the fronthaul burden and reduce user‐perceived delay. However, the edge caching placement is still challenging considering the coupling between caching and AP‐user association, limited fronthaul capacity, and multi‐AP deployment in the cell‐free (CF) massive MIMO systems. To this end, the authors establish a framework for the joint problem of AP‐user association and caching to minimize the content delivery delay which considers both cooperation delivery delay and radio access delay. It is an integer nonlinear programming problem and NP‐hard. The optimization problem is first decomposed into an AP‐user association sub‐problem and a caching placement sub‐problem to address this problem. A two‐stage matching algorithm is further proposed to achieve AP‐user association and a modified genetic algorithm to determine caching placement. A computationally efficient iterative algorithm is developed to solve the joint optimization problem. Finally, the global convergence and computational complexity of the proposed strategy are analyzed theoretically. Simulation results reveal that the proposed strategy can achieve better delivery delay performance than benchmark schemes.

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p_c

and

p_m

. Repeat the above operations, and stop the iteration when the convergence condition is reached.…”

Section: Joint Optimization Algorithmmentioning

confidence: 99%

Joint AP‐user association and caching strategy for delivery delay minimization in cell‐free massive MIMO systems

Wang,

Shen,

et al. 2023

IET Communications

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show abstract

“…The suitability of each chromosome is determined by its fitness value. The chromosomes with high fitness values have an opportunity to be selected as the parent chromosomes [18,19]. Then, the parent chromosomes are processed by crossover and mutation operators.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Multiple-Searching Genetic Algorithm for Whole Test Suites

et al. 2021

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A test suite is a set of test cases that evaluate the quality of software. The aim of whole test suite generation is to create test cases with the highest coverage scores possible. This study investigated the efficiency of a multiple-searching genetic algorithm (MSGA) for whole test suite generation. In previous works, the MSGA has been effectively used in multicast routing of a network system and in the generation of test cases on individual coverage criteria for small- to medium-sized programs. The performance of the algorithms varies depending on the problem instances. In this experiment were generated whole test suites for complex programs. The MSGA was expanded in the EvoSuite test generation tool and compared with the available algorithms on EvoSuite in terms of the number of test cases, the number of statements, mutation score, and coverage score. All algorithms were evaluated on 14 problem instances with different corpus to satisfy multiple coverage criteria. The problem instances were Java open-source projects. Findings demonstrate that the MSGA generated test cases reached greater coverage scores and detected a larger number of faults in the test class when compared with the others.

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“…The authors in [41] have combined GA with hesitant intuitionistic fuzzy sets to obtain the optimal solution for the decision making. The work in [42] presents an efficient GA-based content distribution scheme to reduce the transmission delay and also to improve the throughput of fog radio access network (F-RAN). The GA was used in [43] to enhance the performance of ELM algorithm by selecting the optimal input weights and applying it in breast cancer detection.…”

Section: Genetic Algorithmmentioning

confidence: 99%

Genetic Algorithm Based on Natural Selection Theory for Optimization Problems

et al. 2020

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The metaheuristic genetic algorithm (GA) is based on the natural selection process that falls under the umbrella category of evolutionary algorithms (EA). Genetic algorithms are typically utilized for generating high-quality solutions for search and optimization problems by depending on bio-oriented operators such as selection, crossover, and mutation. However, the GA still suffers from some downsides and needs to be improved so as to attain greater control of exploitation and exploration concerning creating a new population and randomness involvement happening in the population at the solution initialization. Furthermore, the mutation is imposed upon the new chromosomes and hence prevents the achievement of an optimal solution. Therefore, this study presents a new GA that is centered on the natural selection theory and it aims to improve the control of exploitation and exploration. The proposed algorithm is called genetic algorithm based on natural selection theory (GABONST). Two assessments of the GABONST are carried out via (i) application of fifteen renowned benchmark test functions and the comparison of the results with the conventional GA, enhanced ameliorated teaching learning-based optimization (EATLBO), Bat and Bee algorithms. (ii) Apply the GABONST in language identification (LID) through integrating the GABONST with extreme learning machine (ELM) and named (GABONST-ELM). The ELM is considered as one of the most useful learning models for carrying out classifications and regression analysis. The generation of results is carried out grounded upon the LID dataset, which is derived from eight separate languages. The GABONST algorithm has the capability of producing good quality solutions and it also has better control of the exploitation and exploration as compared to the conventional GA, EATLBO, Bat, and Bee algorithms in terms of the statistical assessment. Additionally, the obtained results indicate that (GABONST-ELM)-LID has an effective performance with accuracy reaching up to 99.38%.

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Genetic algorithm‐based content distribution strategy for F‐ RAN architectures

Cited by 6 publications

References 32 publications

Joint AP‐user association and caching strategy for delivery delay minimization in cell‐free massive MIMO systems

Joint AP‐user association and caching strategy for delivery delay minimization in cell‐free massive MIMO systems

Multiple-Searching Genetic Algorithm for Whole Test Suites

Genetic Algorithm Based on Natural Selection Theory for Optimization Problems

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