检索结果-内蒙古大学图书馆

A Chemical Reaction algorithm to Solve the Router Node Placement in Wireless Mesh Networks

MOBILE NETWORKS & APPLICATIONS 2020年第5期25卷 1915-1928页

作者： Sayad, Lamri Bouallouche-Medjkoune, Louiza Aissani, Djamil Univ Bejaia Fac Exact Sci LAMOS Lab Bejaia 06000 Algeria Msila Univ Comp Sci Dept Msila 28000 Algeria

This paper considers the problem of router node placement (WMN-RNP) in wireless mesh networks (WMN). A wireless mesh network consists of three kinds of nodes: mesh clients, mesh routers and gateways interconnected via radio links. The problem considered in this paper is the following: given a set of mesh clients deployed in a rectangular area, determine the best placement of mesh routers so that both client coverage and network connectivity are maximized. This issue is known to be NP-hard since it can be modeled as a facility location problem. To solve this issue, we propose to use a metaheuristic technique inspired from the interactions between molecules in chemical reactions to reach a low stable energy state, namely Chemical Reaction Optimization algorithm (CRO). A simulation tool has been developed to compare the performance of our CRO algorithm to the existing Genetic algorithm (GA) and Simulated Annealing (SA). Simulation results show that our proposed algorithm can improve client coverage by 4.5% to 18% (3% to 17% respectively) and network connectivity by 5% to 61% (4.5% to 41% respectively) when compared to GA algorithm (SA algorithm respectively).

关键词： Wireless mesh network Router node placement bio-inspired algorithm Metaheuristic Chemical reaction optimization

来源：评论

学校读者我要写书评

暂无评论

FPGA-Based Hardware/Software Co-Design of a bio-inspired SAT Solver

引用

IEEE ACCESS 2020年 8卷 49053-49065页

作者： Nguyen, Anh Hoang Ngoc Aono, Masashi Hara-Azumi, Yuko Tokyo Inst Technol Dept Informat & Commun Tokyo 1528550 Japan Keio Univ Fac Environm & Informat Studies Tokyo 1088345 Japan Amoeba Energy Co Ltd Yokohama Kanagawa Japan

For various kinds of Internet of Things (IoT) systems whose control rules can be expressed in a Satisfiability (SAT) problem, this work aims at realizing an IoT-oriented FPGA-based SAT solver leveraging a bio-inspired algorithm, AmoebaSAT, using a hardware/software co-design approach. With regard to the software component, we extended the baseline algorithm to escape from local minima more quickly and achieve significant reduction in iteration count. With regard to hardware, we fully extracted the fine-grained parallelism of the algorithm to further accelerate the solution search. Through our evaluations using several benchmarks of varying variable count and complexity, we demonstrated the efficiency of our solver, especially for larger practical SAT instances. Compared with three state-of-the-art solvers (i.e., one software implementation of the original AmoebaSAT algorithm and two FPGA-based hardware solvers), we achieved an average of 15.9 x and up to 48 x reduction in iteration count. Furthermore, through in-depth analyses of the experimental results, we provided the essential findings on the relationship between the problem's complexity and the SAT algorithm that can be leveraged for extensions of both the hardware and software designs.

关键词： Hardware Stochastic processes Software algorithms Parallel processing Internet of Things Software Search problems bio-inspired algorithm SAT solver FPGA high-level synthesis

来源：评论

学校读者我要写书评

暂无评论

Toward a bio-inspired adaptive spatial clustering approach for IoT applications

引用

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 107卷 736-744页

作者： Jabeur, Nafaa Yasar, Ansar Ul-Haque Shakshuki, Elhadi Haddad, Hedi German Univ Technol Oman Gutech Comp Sci Dept POB 1816 Muscat 130 Oman Hasselt Univ Transportat Res Inst IMOB Wetenschapspk 5 Bus 6 B-3590 Diepenbeek Belgium Acadia Univ Jodrey Sch Comp Sci Wolfville NS Canada Dhofar Univ Dept Comp Sci POB 2509 Salalah 211 Oman

bio-inspired algorithms have demonstrated effective capabilities to solve Wireless Sensor Network (WSN) challenges. As sensors represent a main component in the emergent domain of Internet of Things (IoT), these algorithms are expected to perform also well in this field while adapting to contextual changes and optimizing the use of the limited resources. In this paper, we propose a new firefly-based clustering approach for IoT applications. Our approach includes a micro clustering phase during which Real-World Things (RWTs) compete and self-organize into clusters. These clusters are then polished during a macro-clustering phase where they compete to integrate small neighboring clusters. We extend our approach to allow the IoT clusters to self-adapt by hiring and/or firing RWTs depending on ongoing events and their expected impact on the network and its current deployment area. Initial simulations are showing promising results where the number of clusters tends to stabilize independently from the density of the network and the various communication ranges of RWTs. (c) 2017 Elsevier B.V. All rights reserved.

关键词： Firefly approach bio-inspired algorithm Wireless Sensor Network Internet of Things Micro clustering Macro clustering Attractiveness Hiring candidates Firing candidates

来源：评论

学校读者我要写书评

暂无评论

Cardiac disease detection using cuckoo search enabled deep belief network

引用

INTELLIGENT SYSTEMS WITH APPLICATIONS 2022年 16卷

作者： Nandakumar, P. Narayan, Subhashini Vellore Inst Technol Sch Informat Technol & Engn Vellore India

Cardiac disease is the most infected disease in the world nowadays for all ages of people. An emergency need arises to predict cardiac disease accurately in a short time. In this article, hamming distance feature selection method is proposed for the data preprocessing and data cleaning process in different cardiac disease datasets. Deep learning model such as deep belief networks is used with cuckoo search bio-inspired algorithm for finding the accurate prediction of cardiac disease. The results demonstrate that deep belief networks with the cuckoo search algorithm have achieved good performance with an accuracy of 89.2% from Cleveland, 89.5% from South Africa, and 89.7% from Z-Alizadeh Sani, 90.2% from Framingham, and 91.2% from Statlog cardiac disease datasets.

关键词： Cardiac disease Deep learning bio-inspired algorithm Optimization

来源：评论

学校读者我要写书评

暂无评论

Improved Eagle Strategy algorithm for Dynamic Web Service Composition in the IoT: A Conceptual Approach

引用

FUTURE INTERNET 2022年第2期14卷 56-56页

作者： Rajendran, Venushini Ramasamy, R. Kanesaraj Mohd-Isa, Wan-Noorshahida Multimedia Univ Fac Comp & Informat Cyberjaya 63100 Malaysia

The Internet of Things (IoT) is now expanding and becoming more popular in most industries, which leads to vast growth in cloud computing. The architecture of IoT is integrated with cloud computing through web services. Recently, Dynamic Web Service Composition (DWSC) has been implemented to fulfill the IoT and business processes. In recent years, the number of cloud services has multiplied, resulting in cloud services providing similar services with similar functionality but varying in Quality of Services (QoS), for instance, on the response time of web services;however, existing methods are insufficient in solving large-scale repository issues. bio-inspired algorithm methods have shown better performance in solving the large-scale service composition problems, unlike deterministic algorithms, which are restricted. Thus, an improved eagle strategy algorithm method is proposed to increase the performance that directly indicates an improvement in computation time in large-scale DWSC in a cloud-based platform and on both functional and non-functional attributes of services. By proposing the improved bio-inspired method, the computation time can be improved, especially in a large-scale repository of IoT.

关键词： bio-inspired algorithm Internet of Things improved eagle strategy large-scale repository web service composition

来源：评论

学校读者我要写书评

暂无评论

A novel clustering algorithm by clubbing GHFCM and GWO for microarray gene data

引用

JOURNAL OF SUPERCOMPUTING 2020年第8期76卷 5679-5693页

作者： Edwin Dhas, P. Sankara Gomathi, B. Jayaraj Annapackiam CSI Coll Engn Dept Comp Sci & Engn Nazareth India Natl Engn Coll Dept Elect & Instrumentat Engn Kovilpatti India

The advancement of data mining technology presents a way to examine and analyse the medical databases. Microarray data help in analysing the gene expressions, and the process of clustering helps in categorizing the data into organized groups. Grouping similar gene expressions paves the way for effective analysis, and the relationship between the expressions can be figured out. Recognizing the benefits of clustering, this work intends to present a clustering algorithm by combining generalized hierarchical fuzzy C means (GHFCM) and grey wolf optimization (GWO) algorithms. The GWO algorithm is utilized for selecting the initial clustering point, and the GHFCM algorithm is employed for clustering the microarray gene data. The performance of the proposed clustering algorithm is tested with respect to precision, recall,F-measure and time consumption, and the results are compared with the existing approaches. The performance of the proposed work is satisfactory with betterF-measure rates and minimal time consumption.

关键词： Microarray gene data bio-inspired algorithm Clustering algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved African buffalo optimisation algorithm for petroleum product supply chain management

引用

INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING 2020年第6期11卷 769-779页

作者： Igiri, Chinwe Peace Singh, Yudhveer Bhargava, Deepshikha Shikaa, Samuel Amity Univ Jaipur Rajasthan India Univ Petr & Energy Studies Dehra Dun Uttarakhand India Taraba State Univ Jalingo Nigeria

Real-world supply chain network is complex due to large problem size and constraints. An optimum petroleum products scheduling would not only influence the distribution cost but also result in optimal product scheduling. The bio-inspired method is preferred alternative to exact algorithms because it does not require prior knowledge of the initial solution unlike the latter. The study proposes an improved African Buffalo Optimisation (ABO) algorithm for petroleum supply chain distribution. The ABO is a swarm intelligence-based bio-inspired algorithm with significant performance track record. It models the grazing and defending lifestyle of the African buffaloes in the savannah. The chaotic ABO and chaotic-Levy ABO are the ABO's improved variants with outstanding performance in recent studies. The present study applies the standard ABO and its improved variants to obtain a near optimum petroleum distribution scheduling solution. The comparative result shows that the proposed approach outperformed existing exact algorithms.

关键词： supply chain network computational intelligence petroleum product scheduling bio-inspired algorithm swarm intelligence African buffalo optimisation algorithm chaotic African buffalo optimisation algorithm chaotic-Levy flight African buffalo optimisation algorithm

来源：评论

学校读者我要写书评

暂无评论

An Osmosis-Based Intelligent Agent Scheduling Framework for Cloud Bursting in a Hybrid Cloud

引用

INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2020年第3期11卷 68-88页

作者： Hepsiba, Preethi Sheba Kanaga, Grace Mary E. CMR Inst Technol Comp Sci & Engn Dept Bengaluru India Karunya Inst Technol & Sci Coimbatore Tamil Nadu India

An intelligent system to efficiently provision resources in a hybrid cloud environment is necessary due to the high level of complexity. The semi-permeable agent for hybrid cloud scheduling (SPAH) is a bio-inspired agent that adapts the biological process of osmosis into cloud bursting. The primary objective of the agent is to minimize the makespan. The framework and algorithm for the two phases of SPAH, to recognize the state and decide on action are presented. A QoS (Quality of Service) deadline factor metric is proposed to study the indirect impact of SPAH in deadline satisfaction. SPAH shows significant improvement in deadline satisfaction of up to 85% as compared to other cloud bursting techniques. This is the result of a reduced makespan and a reduced cumulative waiting time. The analysis of SPAH shows that it works in quadratic time complexity.

关键词： bio-inspired algorithm Cloud Bursting Cloud Computing Distributed Computing Intelligent Agent Quality Of Service (Qos) Resource Provisioning Scheduling

来源：评论

学校读者我要写书评

暂无评论

A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions

引用

ENERGY CONVERSION AND MANAGEMENT 2021年 230卷 113773-113773页

作者： Fares, Dalila Fathi, Mohamed Shams, Immad Mekhilef, Saad Univ MHamed Bougara Lab Signaux & Syst Inst Genie Elect & Elect Ave Independence Boumerdes 35000 Algeria UDES CDER Unite Dev Equipements Solaires Ctr Dev Energies Renouvelables Tipasa 42415 Algeria Univ Malaya Dept Elect Engn Power Elect & Renewable Energy Res Lab PEARL Fac Engn Kuala Lumpur 50603 Malaysia Swinburne Sch Software & Elect Engn Melbourne Vic Australia

The partial shading condition (PSC) makes it challenging for the PV system to harvest maximum power via maximum power point tracking (MPPT). Various MPPT algorithms based on bio-inspired optimization methods were proposed in the literature. The mechanism employed by these algorithms varies from one to another, making them perform differently when tracking the GMPP. This paper introduces a novel MPPT technique based on the improved squirrel search algorithm (ISSA). The performance of the proposed ISSA improved the tracking time by 50% in comparison with the conventional SSA algorithm. Similarly, the proposed method has also been compared with popular Genetic algorithm (GA), and particle swarm optimization (PSO). The results proved the ability of the proposed algorithm in tracking the GMPP with faster convergence and fewer power oscillations in comparison. The feasibility and effectiveness of the proposed ISSA based MPPT have been validated experimentally, and the results clearly demonstrate its capability in tracking the GMPP with an average efficiency of 99.48% and average tracking time of 0.66 s.

关键词： Global maximum power point Partial shading PV system bio-inspired algorithm Squirrel search algorithm

来源：评论

学校读者我要写书评

暂无评论

Mining High Utility Itemsets Using bio-inspired algorithms: A Diverse Optimal Value Framework INDEX

引用

IEEE ACCESS 2018年 6卷 19568-19582页

作者： Song, Wei Huang, Chaomin North China Univ Technol Coll Comp Sci & Technol Beijing 100144 Peoples R China North China Univ Technol Beijing Key Lab Integrat & Anal Large Scale Strea Beijing 100144 Peoples R China

Mining high utility itemsets (HUI) is an interesting research problem in the field of data mining and knowledge discovery. Recently, bio-inspired computing has attracted considerable attention, leading to the development of new algorithms for mining HUIs. These algorithms have shown good performance in terms of efficiency, but are not guaranteed to find all HUIs in a database. That is, the quality is comparatively poor in terms of the number of discovered HUIs. To solve this problem, a new framework based on bioinspired algorithms is proposed. This approach adjusts the standard roadmap of bio-inspired algorithms by proportionally selecting discovered HUIs as the target values of the next population, rather than maintaining the current optimal values in the next population. Thus, the diversity within populations can be improved. Three new algorithms based on the bio-HUI framework are developed using the genetic algorithm, particle swarm optimization, and the bat algorithm, respectively. Extensive tests conducted on publicly available datasets show that the proposed algorithms outperform existing state-of-the-art algorithms in terms of efficiency, quality of results, and convergence speed.

关键词： Data mining high utility itemset mining bio-inspired algorithm genetic algorithm particle swarm optimization bat algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：