A Heuristic Genetic Algorithm for Solving Resource Allocation Problems

Lee, Zne-Jung; Su, Shun‐Feng; Lee, Chou‐Yuan; Hung, Yao-Shan

doi:10.1007/s10115-003-0082-0

Cited by 28 publications

(7 citation statements)

References 13 publications

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“…This is not the first attempt to deploy GA for optimization of resource allocation. Mature solutions have been proposed for allocation of generic resource [23], [24], radio resource [25] and cloud resource [26], [27]. All these approaches consider the problem of global resource optimization, where the system allocates resource blocks from a certain pool to a known set of targets (activities, links, users, etc.).…”

Section: Proposed Methods a Slicing Strategies As Binary Sequence Codesmentioning

confidence: 99%

Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks

2018

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In the context of Fifth Generation (5G) mobile networks, the concept of "Slice as a Service" (SlaaS) promotes mobile network operators to flexibly share infrastructures with mobile service providers and stakeholders. However, it also challenges with an emerging demand for efficient online algorithms to optimize the request-and-decision-based inter-slice resource management strategy. Based on genetic algorithms, this paper presents a novel online optimizer that efficiently approaches towards the ideal slicing strategy with maximized long-term network utility. The proposed method encodes slicing strategies into binary sequences to cope with the request-and-decision mechanism. It requires no a priori knowledge about the traffic/utility models, and therefore supports heterogeneous slices, while providing solid effectiveness, good robustness against non-stationary service scenarios, and high scalability.INDEX TERMS 5G mobile communication, business model, communication system operations and management, genetic algorithms, network slicing, optimal scheduling, optimization, resource management

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Section: Proposed Methods a Slicing Strategies As Binary Sequence Codesmentioning

confidence: 99%

Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks

2018

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“…3) For random dispersion, the mutation probability at this time is multiplied by several multiples. We set the probability to amplify by 5 times to perform a large-variation operation, which causes individuals in the population to exhibit a relatively large variation [15].…”

Section: E Improved Genetic Algorithmmentioning

confidence: 99%

Cooperative Content Caching for Mobile Edge Computing With Network Coding

et al. 2019

IEEE Access

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As an important technology in next-generation networks, mobile edge computing (MEC) can bring high-speed intelligent network services to users. However, planning MEC file transmission represents a problem to be urgently solved. In addition, network coding has been an emerging technology in recent years, and certain forms of cooperation with wireless relays can improve the transmission efficiency of network data. In this paper, we focus on cooperation in MEC intelligent cache with network coding. First, we built a MEC intelligent cache analysis platform that includes multiple algorithms; it can predetermine which streaming media files should be stored in the limited storage space of the MEC device to provide users the most efficient intelligent network experience. The platform's decision algorithm includes an innovative composite recommendation algorithm that is more comprehensive than the traditional recommendation algorithm. The MEC file extracted from the core network is then simultaneously transmitted to the edge with the real-time normal communication file using an innovative network coding and wireless relay cooperation method; with that, the files stored in the MEC can be cached to the edge. The normal network communication process does not require additional slot costs. The simulation results show that the proposed cooperation scheme can effectively improve network data transmission, improve users' average access rate, and offer users better intelligent network services. INDEX TERMS Mobile edge computing, network coding, wireless relay, intelligent cache, cooperative communications.

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“…In this paper, in order to avoid maintenance of infeasible chromosomes, the penalty functions method is applied. The penalty functions method is very effective in constrained optimization problems [39,40].…”

Section: Genetic Algorithm For Solving the Clustering Problemmentioning

confidence: 99%

Classification of Textual E‐Mail Spam Using Data Mining Techniques

Alguliyev

Alıguliyev

Nazirova

2011

Applied Computational Intelligence and Soft Computing

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A new method for clustering of spam messages collected in bases of antispam system is offered. The genetic algorithm is developed for solving clustering problems. The objective function is a maximization of similarity between messages in clusters, which is defined by k-nearest neighbor algorithm. Application of genetic algorithm for solving constrained problems faces the problem of constant support of chromosomes which reduces convergence process. Therefore, for acceleration of convergence of genetic algorithm, a penalty function that prevents occurrence of infeasible chromosomes at ranging of values of function of fitness is used. After classification, knowledge extraction is applied in order to get information about classes. Multidocument summarization method is used to get the information portrait of each cluster of spam messages. Classifying and parametrizing spam templates, it will be also possible to define the thematic dependence from geographical dependence (e.g., what subjects prevail in spam messages sent from certain countries). Thus, the offered system will be capable to reveal purposeful information attacks if those occur. Analyzing origins of the spam messages from collection, it is possible to define and solve the organized social networks of spammers.

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A Heuristic Genetic Algorithm for Solving Resource Allocation Problems

Cited by 28 publications

References 13 publications

Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks

Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks

Cooperative Content Caching for Mobile Edge Computing With Network Coding

Classification of Textual E‐Mail Spam Using Data Mining Techniques

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