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adaptive genetic algorithm inspired energy-efficient cache deployment strategy for caching-enabled networks

INTERNET TECHNOLOGY LETTERS 2021年第5期4卷

作者： Wang, Liwei Army Engn Univ Shijiazhuang Campus Shijiazhuang Hebei Peoples R China Air Force Engn Univ Aviat Maintenance Sch NCO 23 Hangkong Rd Xinyang 464000 Peoples R China

Caching-Enabled Networks (CEN) has the wide application value due to the feature of in-network caching, which has attracted more researchers paying attention to the cache deployment. However, different caching strategies cause different network performance, such as cache hit ratio and energy consumption. In fact, although the current strategies can provide the satisfied cache hit ratio, they consume too much energy for completing the cache deployment. In this paper, an energy-efficient cache deployment strategy is proposed, including two stages. The first stage is the mathematical modeling for cache hit ratio and energy consumption, in which a bi-objective optimization problem is built. The second stage is the model solving, where the adaptive genetic algorithm is used. The proposed cache deployment is evaluated based on Europe-GTS network topology, and the experimental results on cache hit ratio and energy consumption demonstrate its feasibility and efficiency.

关键词： adaptive genetic algorithm bi-objective optimization cache deployment CEN

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An adaptive genetic algorithm for the time dependent inventory routing problem

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JOURNAL OF INTELLIGENT MANUFACTURING 2014年第5期25卷 1025-1042页

作者： Cho, Dong Won Lee, Young Hae Lee, Tae Youn Gen, Mitsuo Hanyang Univ Dept Ind & Management Engn Ansan 426791 Gyeonggi Do South Korea Fuzzy Log Syst Inst Iizuka Fukuoka Japan

In this paper we propose an adaptive genetic algorithm that produces good quality solutions to the time dependent inventory routing problem (TDIRP) in which inventory control and time dependent vehicle routing decisions for a set of retailers are made simultaneously over a specific planning horizon. This work is motivated by the effect of dynamic traffic conditions in an urban context and the resulting inventory and transportation costs. We provide a mixed integer programming formulation for TDIRP. Since finding the optimal solutions for TDIRP is a NP-hard problem, an adaptive genetic algorithm is applied. We develop new genetic representation and design suitable crossover and mutation operators for the improvement phase. We use adaptive genetic operator proposed by Yun and Gen (Fuzzy Optim Decis Mak 2(2):161-175, 2003) for the automatic setting of the genetic parameter values. The comparison of results shows the significance of the designed AGA and demonstrates the capability of reaching solutions within 0.5 % of the optimum on sets of test problems.

关键词： Time dependent inventory routing problem adaptive genetic algorithm Mixed integer programming

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An Improved adaptive genetic algorithm for Vehicle Routing Problem

An Improved Adaptive Genetic Algorithm for Vehicle Routing P...

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International Conference on Logistics Systems and Intelligent Management

作者： Sun Zhong-yue Guan Zhong-liang Wang Qin Beijing Jiao Tong Univ Sch Econ & Management Beijing 100044 Peoples R China

ISBN: (纸本)9781424473281

In order to solve the problem of slow convergence speed of adaptive genetic algorithm (AGA) in the early stage of evolution, an improved adaptive genetic algorithm (IAGA) was presented. With the introduction of an indicator evaluating the degree of population diversity, the new algorithm can adaptively adjust the probabilities of crossover. Furthermore, the IAGA was applied to vehicle routing problem. The experimental results demonstrate that the new algorithm can effectively improve convergence speed compared to the AGA and the optimal or nearly optimal solutions to the vehicle routing problem can be easily obtained.

关键词： adaptive genetic algorithm Vehicle Routing Problem Population Diversity

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Research on the Sensor Coarse Signal Processing Model Based on adaptive genetic algorithm

Research on the Sensor Coarse Signal Processing Model Based ...

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3rd International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation(CDMMS 2013)

作者： Zou Donglan XinYuCollege Math & Comp Sci Inst Xinyu Jiangxi Peoples R China

ISBN: (纸本)9783037859100

With the rapid development of electronic information science and network transmission technology, the signal processing technology has been widely applied to various fields, which is the most important component of signal detection and transmission, and the key signal processing technology for processing sensor crude signals. Based on this, the experimental system of sensor coarse signal processing model is established, and in the experimental system, the transformer can carry out signal recognition for voltage and current, the use of PC microcontroller and embedded AD converter carries out analog / digital conversion for sensor crude signal. For the amplification process of sensor coarse signal, the use of adaptive genetic algorithm carries out mathematical modeling, the realization of the signal identification, acquisition and processing functions through software programming control. Finally, the intelligent processing of sensor coarse signal is successfully completed by the experiment system, and the signal processing effect is given as well.

关键词： adaptive genetic algorithm Sensor Coarse signal processing PC microcontroller Embedded software AD converter

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EEO-AGA: Energy Efficiency Optimisation in D2D Communications using adaptive genetic algorithm 17

EEO-AGA: Energy Efficiency Optimisation in D2D Communication...

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International Conference on Internet of Things and Machine Learning (IML)

作者： De-Thu Huynh Dung Ong Mau Chu Hong Hai Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Hubei Peoples R China Ind Univ Hochiminh City Fac Elect Technol Hochiminh Vietnam Hochiminh City Univ Transport Fac Elect & Elect Engn Hochiminh Vietnam Hochiminh City Univ Transport Hochiminh Vietnam

ISBN: (纸本)9781450352437

In the next generation network, Device-to-Device communications are viewed as the prospective technical solution to improve the capacity and reduce the power consumption. Moreover, multimedia applications in Device-to-Device communications consume very much energy also large bandwidth to meet the ever-increasing Quality of Services (QoS) requirement for mobile users. In this paper, the new Energy Efficiency Optimisation solution in Device to -Device communication thanks to utilising the adaptive genetic algorithm (EEO-AGA), which is proposed to deal with the aforementioned problems. Specially, we combine the LMDC and FEC for encoding the video. For this result, the video transmitting can get the higher rates while minimising the error, delay under the unsteady property of wireless environment. Next, we intensive analyse the complicated problem about the consumption of energy for packetisations and transmissions videos. Then, we will optimise the energy efficiency for all users while ensuring the bandwidth constraint also recovery error of obtained videos (to higher quality of obtained videos). It has been shown by simulation results that the developed EEO-AGA solution can obtain the outstanding enhancements in reducing energy consumption and ensuring high user's QoS, compared with the other non-optimal methods.

关键词： Energy Efficiency Optimisation D2D Communications adaptive genetic algorithm

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An adaptive genetic algorithm for the Grid Scheduling Problem

An Adaptive Genetic Algorithm for the Grid Scheduling Proble...

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24th Chinese Control and Decision Conference (CCDC)

作者： Zhou Wei Bu Yan-ping E China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China Shanghai Jiaotong Uni Sch Technol Shanghai 201101 Peoples R China

ISBN: (纸本)9781457720727

Grid computing is a service that shares computational power and data storage capacity over the Internet. The goal of grid tasks scheduling is to achieve high system throughput and to match the application need with the available computing resources. Aiming at the evolution characteristics of genetic algorithm (GA), an adaptive genetic algorithm (AGA) is presented in this paper. The AGA is used to solve the grid scheduling problem. It can keep all the advantages of the standard GA, such as implementation simplicity, low computational burden, and few control parameters, etc. A set of experiments show that the algorithm is stable and presents low variability. The preliminary results obtained in this research are auspicious. We analyze the laboratory results to show that the modified algorithm has better characteristics than standard GA and Max-Min algorithm when it was used in task scheduling.

关键词： grid task scheduling adaptive genetic algorithm makespan

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Research on Feature Selection in Near-infrared Spectroscopy Classification Based on Improved adaptive genetic algorithm Combined with ReliefF 9

Research on Feature Selection in Near-infrared Spectroscopy ...

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9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Liu, Ming Ding, Xiangqian Yu, Shusong Hou, Ruichun Ocean Univ China Coll Informat Sci & Engn Qingdao Peoples R China

ISBN: (纸本)9781538630228

In order to solve the problem of feature selection and the low classification accuracy in high-dimensional data, a feature selection algorithm ReliefF-IAGA which is based on ReliefF and IAGA(improved adaptive genetic algorithm) is proposed in this paper. The algorithm obtains the feature importance score using ReliefF feature selection algorithm and eliminates the irrelevant features. Then, the optimal feature subset is obtained by using IAGA. In order to verify the effectiveness of the algorithm, the tobaccos origin classification model was constructed by using the tobaccos from different places of origin. Simulation results show that compared with other methods, the optimal feature subset obtained by the proposed method is more relevant, improving the classification accuracy of the origin of tobaccos.

关键词： feature selection high-dimensional data random forest ReliefF adaptive genetic algorithm

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Clustering Analysis based on Chaos Micro Variation adaptive genetic algorithm for Radio Fuze Jamming 29

Clustering Analysis based on Chaos Micro Variation Adaptive ...

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第29届中国控制与决策会议

作者： Xu Guoxin Tong Xin Pan Wei Ren Tao Training Department Shenyang Artillery Academy Institute of Scientific & Technical Information of Liaoning Province Software College Northeastern University

ISBN: (纸本)9781509046584

To improve the accuracy of clustering classification the adaptive genetic algorithm was *** code is float,the selection operator is rank-based fitness assignment and elitist model,the crossover operator is real valued recombination,the mutation operator is real *** adaptive crossover probability and adaptive mutation probability are proposed,which consider the influence of every generation to algorithm and the effect of different individual fitness in every *** and experiment shows that the algorithm can get global optimum clustering center,and greatly improve the amplitude of operation.

关键词： adaptive genetic algorithm cluster analysis crossover probability mutation probability

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NURBS based Ship Form Design Using adaptive genetic algorithm

NURBS based Ship Form Design Using Adaptive Genetic Algorith...

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18th International Offshore and Polar Engineering Conference (ISOPE 2008)

作者： Lu, Conghong Lin, Yan Ji, Zhuoshang Chen, Ming Dalian Univ Technol Ship CAD Engn Ctr Dalian Peoples R China

ISBN: (纸本)9781880653708

It is studied to design the ship waterline with as few NURBS (Non-Uniform Rational B-Spline) control points as possible, based on the geometric properties of the waterplane. The appropriate multi-objective constrained optimization model is set and an adaptive genetic algorithm (AGA) is applied to solve the optimization problem. The instances of the typical waterlines design for full-scale ship indicate that it is feasible and can satisfy the engineering precision to design the waterline applying this method and the data used for waterline representation can be reduced. So it is expected to design a bull surface with as few data as possible.

关键词： Ship form waterline NURBS control point weight factor adaptive genetic algorithm

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A Closed-loop Supply Chain Planning Based on adaptive genetic algorithm

A Closed-loop Supply Chain Planning Based on Adaptive Geneti...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM 2009)

作者： Cai, Zhengying Fang, Hanwei Xiao, Renbin Xin, Rong China Three Gorges Univ Ctr Informat Technol Yichang 443002 Hubei Peoples R China Huazhong Univ Sci & Technol Inst Syst Engn Wuhan 430074 Peoples R China

ISBN: (纸本)9781424448692

A closed-loop supply chain operation integrating forward and reverse supplies, productions and distributions are established here based on the control engineering theory, and the status equations and mathematical model of supply chain planning and operation with existed uncertainties are analyzed also. To integrate the uncertain for ward and reverse supply chain planning information, a fuzzy adaptive planning strategy based on genetic algorithm is designed, where the information of demand, pur chasing, recovery, inventory and production is fuzzy. The program uses fuzzy adaptive genetic algorithm to optimize fuzzy rule table, so planning para meters can be changed with the supply chain operation and be self-adjusted according to fuzzy rules to improve the static and dynamic performance characteristics of the system. Finally, a closed-loop operation case is analyzed for authentication.

关键词： Fuzzy planning closed-loop supply chain adaptive genetic algorithm product recycle

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