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Dynamic niching particle swarm optimization with an external archive-guided mechanism for multimodal multi-objective optimization

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INFORMATION SCIENCES 2024年 653卷

作者： Sun, Yu Chang, Yuqing Yang, Shengxiang Wang, Fuli Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England

Multimodal multi-objective optimization problems (MMOPs) contain multiple equivalent Pareto optimal sets (PSs) corresponding to the same Pareto front (PF). However, simultaneously locating well-distributed and well-converged multiple equivalent global PSs and PF remains challenging. Therefore, this paper proposes dynamic niching particle swarm optimization (PSO) with an external archive-guided (AG) mechanism, termed DNPSO-AG, for solving MMOPs. In DNPSO-AG, a clustering-based dynamic niching technique is integrated with PSO to divide the population into multiple niches. In addition, a leader updating method controls the updating of the leaders. Furthermore, a novel external archive-guided mechanism guides the evolution of multiple niches and enhances the distribution of solutions, which comprises two strategies: the adaptive division of the external archive strategy, which adaptively divides the external archive into multiple sub-archives, and the distance-based sub-archive and niche matching strategy, which assigns sub-archives to multiple niches for maintenance. The experimental results demonstrate that the proposed DNPSO-AG outperforms seven other state-of-the-art competitors on the CEC 2019 MMOP test suite in terms of the inverted generational distance (IGD) and IGD in the decision space (IGDX) metrics, with improvements of 21.3% and 9.1% over the best-performing competitor, respectively.

关键词： Particle swarm optimization Niching affinity propagation clustering External archive Multimodal multi-objective optimization

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Efficient Indoor Fingerprinting Localization Technique Using Regional propagation Model

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IEICE TRANSACTIONS ON COMMUNICATIONS 2014年第8期E97B卷 1728-1741页

作者： Ding, Genming Tan, Zhenhui Wu, Jinsong Zhang, Jinbao Beijing Jiaotong Univ BJTU Inst Broadband Wireless Mobile Commun Beijing 100044 Peoples R China Beijing Jiaotong Univ BJTU State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Bell Labs Shanghai 201206 Peoples R China BJTU EMC Lab Beijing 100044 Peoples R China

The increasing demand of indoor location based service (LBS) has promoted the development of localization techniques. As an important alternative, fingerprinting localization technique can achieve higher localization accuracy than traditional trilateration and triangulation algorithms. However, it is computational expensive to construct the fingerprint database in the offline phase, which limits its applications. In this paper, we propose an efficient indoor positioning system that uses a new empirical propagation model, called regional propagation model (RPM), which is based on the cluster based propagation model theory. The system first collects the sparse fingerprints at some certain reference points (RPs) in the whole testing scenario. Then affinity propagation clustering algorithm operates on the sparse fingerprints to automatically divide the whole scenario into several clusters or sub-regions. The parameters of RPM are obtained in the next step and are further used to recover the entire fingerprint database. Finally, the location estimation is obtained through the weighted k-nearest neighbor algorithm (WkNN) in the online localization phase. We also theoretically analyze the localization accuracy of the proposed algorithm. The numerical results demonstrate that the proposed propagation model can predict the received signal strength (RSS) values more accurately than other models. Furthermore, experiments also show that the proposed positioning system achieves higher localization accuracy than other existing systems while cutting workload of fingerprint calibration by more than 50% in the offline phase.

关键词： fingerprinting localization indoor positioning regional propagation model affinity propagation clustering

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An AP-Based Shilling Attack Detector for Collaborative Filtering Recommendation Systems

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JOURNAL OF INTERNET TECHNOLOGY 2017年第5期18卷 985-993页

作者： Wang, Qingxian Wan, Meng Shang, Mingsheng Cai, Shimin Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Sichuan Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Beijing Peoples R China

Collaborative filtering is a technique widely used in online recommendation systems nowadays. However, it is vulnerable from manipulation by malicious users who often create some fake account (or shilling) profiles to influence the results of recommender systems. To identify the fake users, existing algorithms usually utilize certain characteristics of shilling profiles, of which the drawbacks are the low precision and the requirement of a large size of training set. In this paper, we develop a clustering based method to find the shilling attackers by incorporating the information of user ratings and the attribute of user profiles. The users are firstly self-organizedly clustered into several groups based on the integrated information of the rating features and the attributes of user profile, then the malicious user group is identified through the GRDMA (Group Rating Deviation from Mean Agreement) values of user group. Instead of identifying attacker one by one, the proposed algorithm finds the malicious users at the collective level, which provids a novel way to analyse and detect shilling attack. The experimental results performed on MovieLens dataset demonstrate that the proposed algorithm is effective and robust in three typical kinds of shilling attack models, especially when the attack size and the filler size are sufficiently high.

关键词： Collaborative filtering Shilling attack detection affinity propagation clustering Collective level

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Level of Service for Bicycle Through Movement at Signalized Intersections Operating Under Heterogeneous Traffic Flow Conditions

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TRANSPORTATION IN DEVELOPING ECONOMIES 2017年第2期3卷 1-16页

作者： Beura, Sambit Kumar Kumar, N. Kiran Bhuyan, Prasanta Kumar Natl Inst Technol Rourkela Dept Civil Engn Rourkela 769008 Odisha India

This study has taken an attempt to model the quality of services offered to bicycle through movement at urban signalized intersections carrying heterogeneous traffic. An extensive set of quantitative attributes (geometric, traffic and built-environmental) are collected from 35 intersection approaches of four Indian mid-sized cities and are thoroughly analyzed from a bicyclist's perspective. Perceived satisfactions of approximately 160 bicycle users on each intersection approach are assessed through an extensive perception survey. By using the survey outcomes as the dependent variable, six attributes of intersection approaches having significant influences on the bicycle service quality are identified with the help of Pearson's correlation analysis. Further, a step-wise regression analysis is carried out to establish a strong relationship between these influencing variables and perceived satisfactions of bicyclists. As observed, the resulting model is highly reliable in the present context and has a very high coefficient of determination (R-2) value of 0.86 with averaged observations. A sensitivity analysis is carried out to recognize the percentage contributions of modeled variables in service quality prediction. As per the results obtained, traffic volume (38%), width (17.6%), and pavement condition (15.9%) of an intersection approach have by far the most significant influences on its service quality. affinity propagation (AP) clustering is used to define the ranges of bicycle service categories (A-F). It is observed that approximately 69% of all studied intersection approaches are offering above average quality of services (A-C) and remaining are offering below average (D-F) kind of services at their present scenario.

关键词： Signalized intersection Heterogeneous traffic flow Bicycle level of service Step-wise regression Sensitivity analysis affinity propagation clustering

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An AP-Based Shilling Attack Detector for Collaborative Filtering Recommendation Systems

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网际网路技术学刊 2017年第5期18卷 985-993页

作者： Qingxian Wang Meng Wan Mingsheng Shang Shimin Cai

关键词： Collaborative filtering Shilling attack detection affinity propagation clustering Collective level

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An improved incomplete AP clustering algorithm based on K nearest neighbours

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2019年第3期11卷 269-277页

作者： Chen, Zhikui Leng, Yonglin Hu, Yueming Dalian Univ Technol Sch Software Technol Dalian 116620 Peoples R China Bohai Univ Coll Informat Sci & Technol Jinzhou 112100 Peoples R China South China Agr Univ Coll Nat Resources & Environm Guangzhou 510642 Guangdong Peoples R China

With the fast development of internet of things (IoT), a large amount of missing data is produced in the process of data collection and transmission. We call these data incomplete data. Many traditional methods use imputation or discarding strategy to cluster incomplete data. In this paper, we propose an improved incomplete affinity propagation (AP) clustering algorithm based on K nearest neighbours (IAPKNN). IAPKNN firstly partitions the dataset into complete and incomplete dataset, and then clusters the complete data set by AP clustering directly. Secondly, according to the similarity, IAPKNN extends the responsibility and availability matrices to the incomplete dataset. Finally, clustering algorithm is restarted based on the extended matrices. In addition, to address the clustering efficiency of large scale dataset, we give a distributed clustering algorithm scheme. Experiment results demonstrate that IAPKNN is effective in clustering incomplete data directly.

关键词： incomplete data affinity propagation clustering K nearest neighbours incomplete information system

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Identifying global zoogeographical regions: lessons from Wallace

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JOURNAL OF BIOGEOGRAPHY 2013年第12期40卷 2215-2225页

作者： Rueda, Marta Rodriguez, Miguel A. Hawkins, Bradford A. Univ Calif Irvine Dept Ecol & Evolutionary Biol Irvine CA 92697 USA Univ Alcala Dept Life Sci Forest Ecol & Restorat Grp Madrid Spain

AimWhen dividing the world into zoogeographical regions, Alfred Russel Wallace stipulated a set of criteria by which regions should be determined, foremost the use of generic rather than species distributions. Yet, recent updates of Wallace's scheme have not followed his reasoning, probably explaining in part the discrepancies found. Using a recently developed quantitative method, we evaluated the world's zoogeographical regions following his criteria as closely as possible. LocationGlobal. MethodsWe subjected presence-absence data from range maps of birds, mammals and amphibians to an innovative clustering algorithm, affinity propagation. We used genera as our taxonomic rank, although species and familial ranks were also assessed, to evaluate how divergence from Wallace's criteria influences the results. We also accepted Wallace's argument that bats and migratory birds should be excluded (although he was contradictory about the birds) and devised a procedure to determine the optimal number of regions to eliminate subjectivity in delimiting the number of regions. ResultsRegions attained using genera (eight for mammals and birds and six for amphibians) strongly coincided with the regions proposed by Wallace. The regions for amphibians were nearly identical to Wallace's scheme, whereas we obtained two new regions' for mammals and two for birds that largely coincide with Wallace's subregions. As argued by Wallace, there are strong reasons not to consider these as being equivalent to the six main regions. Species distributions generated many small regions related to contemporary climate and vegetation patterns, whereas at the familial rank regions were very broad. The differences between our generic maps and Wallace's all involve areas which he identified as being uncertain in his regionalization. Main conclusionsDespite more than 135years of additional knowledge of distributions, the shuffling of generic concepts, and the development of computers and complex analytical

关键词： affinity propagation clustering Alfred Russel Wallace amphibians biogeographical regions bioregionalization birds mammals

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FUSAIN: Combining Functional Dependencies and clustering for Missing Values Imputation

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ENGINEERING LETTERS 2022年第2期30卷 513-521页

作者： Wu, Huaiguang Li, Shuaichao Shi, Wenjun Du, Shaoqing Zhengzhou Univ Light Ind Fac Comp & Commun Engn Zhengzhou 450066 Henan Peoples R China Zhengzhou Univ Light Ind Zhengzhou 450066 Henan Peoples R China

Missing data is a common problem faced with real-world datasets. A large number of missing data will greatly affect the quality of the data and cause deviations in the results of data analysis. Therefore, missing values imputation (MVI) is a critical data processing process. Most imputation methods model the distribution of observed data to approximate the missing values. Such an approach usually models a single distribution for the entire dataset, which ignores the dependencies between data. In this paper, we propose a novel hybrid imputation algorithm, called combining Functional dependencies and clustering for miSsing vAlues ImputatioN (FUSAIN), which combines Functional Dependencies (FDs), K Nearest Neighbor (KNN), and affinity propagation (AP) clustering. This proposed algorithm not only considers the distribution of data but also uses the data dependency relationship represented by FDs to impute missing values. From the experimental results, the imputation performance of the proposed algorithm achieves superior performance compared to common and popular imputation algorithms.

关键词： Missing value imputation affinity propagation clustering Functional dependencies K nearest neighbor

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Constructing an Indoor Floor Plan Using Crowdsourcing Based on Magnetic Fingerprinting

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SENSORS 2017年第11期17卷 2678页

作者： Luo, Haiyong Zhao, Fang Jiang, Mengling Ma, Hao Zhang, Yuexia Chinese Acad Sci Inst Comp Technol Beijing Key Lab Mobile Comp & Pervas Device Beijing 100190 Peoples R China Beijing Univ Posts & Telecommun Sch Software Engn Beijing 100876 Peoples R China Beijing Informat Sci & Technol Univ Sch Informat & Telecommun Engn Beijing 100101 Peoples R China

A large number of indoor positioning systems have recently been developed to cater for various location-based services. Indoor maps are a prerequisite of such indoor positioning systems;however, indoor maps are currently non-existent for most indoor environments. Construction of an indoor map by external experts excludes quick deployment and prevents widespread utilization of indoor localization systems. Here, we propose an algorithm for the automatic construction of an indoor floor plan, together with a magnetic fingerprint map of unmapped buildings using crowdsourced smartphone data. For floor plan construction, our system combines the use of dead reckoning technology, an observation model with geomagnetic signals, and trajectory fusion based on an affinity propagation algorithm. To obtain the indoor paths, the magnetic trajectory data obtained through crowdsourcing were first clustered using dynamic time warping similarity criteria. The trajectories were inferred from odometry tracing, and those belonging to the same cluster in the magnetic trajectory domain were then fused. Fusing these data effectively eliminates the inherent tracking errors originating from noisy sensors;as a result, we obtained highly accurate indoor paths. One advantage of our system is that no additional hardware such as a laser rangefinder or wheel encoder is required. Experimental results demonstrate that our proposed algorithm successfully constructs indoor floor plans with 0.48 m accuracy, which could benefit location-based services which lack indoor maps.

关键词： indoor localization floor plan construction crowdsourcing affinity propagation clustering DTW

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Data-Driven Power Control of Ultra-Dense Femtocells: A clustering Based Approach 26

Data-Driven Power Control of Ultra-Dense Femtocells: A Clust...

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26th Wireless and Optical Communication Conference (WOCC)

作者： Wang, Li-Chun Cheng, Shao-Hung Tsai, Ang-Hsun Natl Chiao Tung Univ Dept Elect & Comp Engn Hsinchu Taiwan Natl Def Univ Dept Elect & Elect Engn Chung Cheng Inst Technol Taoyuan Taiwan

ISBN: (纸本)9781509049097

In this paper we present a data-driven power control (DDPC) approach to improve total cell throughput and energy efficiency of ultra-dense femtocells. Although femtocells can increase the capacity and coverage in an indoor environment, ultra-dense femtocells may consume a lot of energy and generate severe interference. We investigate a data-driven clustering approach to reduce co-tier interference among femtocells in a dense deployment scenario. The proposed DDPC approach periodically collects the operation data of dense femtocells, including reference signal received power (RSRP) from each user, the transmission power and the number of users per femtocell, and so on. The collected data are processed via the affinity propagation (AP) clustering algorithm to determine the cluster centers to perform power control. The AP clustering algorithm can automatically determine appropriate the number of clusters and the corresponding cluster centers for various femtocell densities. Simulation results show that the proposed DDPC approach can increase 41% higher total cell throughput and 64% higher energy efficiency respectively, compared to the approach without power control in the ultra-dense femtocells.

关键词： Data-driven affinity propagation clustering ultra-dense femtocells energy efficiency

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