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1.
A new variant of the p-hub location problem with a ring backbone network for content placement in VoD services
Soumen Atta, Goutam Sen, 2021, izvirni znanstveni članek

Opis: In this article, the single allocation p-hub location problem (SApHLP) with a ring backbone network for content placement in VoD services is proposed. In VoD services, a large volume of digital data is kept as data segments in spatially distributed hubs. In SApHLP, each user is restricted to be allocated only to a single hub, and here hubs form a ring backbone network. SApHLP jointly addresses (i) the locations of hubs, (ii) the placement of segments to hubs, (iii) the allocation of users to hubs as per their demands, and (iv) the optimal paths to route the demands from users to hubs. We have introduced network flow-based 3-subscripted and path-based 4-subscripted MILP formulations of SApHLP. This article presents a novel discrete particle swarm optimization (PSO)-based approach where factoradic numbers are used to encode solution. It also incorporates three problem-specific solution refinement methods for faster convergence. In this article, SApHLP instances are generated from a real-world database of video files obtained from a movie recommender system. The benchmark solutions are generated using IBM’s CPLEX optimizer with default settings and Benders decomposition strategy. The performance of the proposed PSO is compared with the benchmark results produced by CPLEX.
Ključne besede: Single allocation p-hub location problem, Ring backbone network, VoD services, Particle Swarm Optimization (PSO), Factoradics, CPLEX
Objavljeno v RUNG: 17.04.2023; Ogledov: 1066; Prenosov: 0
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2.
Solving a new variant of the capacitated maximal covering location problem with fuzzy coverage area using metaheuristic approaches
Soumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2022, izvirni znanstveni članek

Opis: The Maximal Covering Location Problem (MCLP) is concerned with the optimal placement of a fixed number of facilities to cover the maximum number of customers. This article considers a new variant of MCLP where both the coverage radii of facilities and the distance between customer and facility are fuzzy. Moreover, the finite capacity of each facility is considered. We call this problem the capacitated MCLP with fuzzy coverage area (FCMCLP), and it is formulated as a 0–1 linear programming problem. In this article, two classical metaheuristics: particle swarm optimization, differential evolution, and two new-generation metaheuristics: artificial bee colony algorithm, firefly algorithm, are proposed for solving FCMCLP. Each of the customized metaheuristics utilizes a greedy deterministic heuristic to generate their initial populations. They also incorporate a local neighborhood search to improve their convergence rates. New instances of FCMCLP are generated from the traditional MCLP instances available in the literature, and IBM’s CPLEX solver is used to generate benchmark solutions. An experimental comparative study among the four customized metaheuristics is described in this article. The performances of the proposed metaheuristics are also compared with the benchmark solutions obtained from CPLEX.
Ključne besede: Facility Location Problem (FLP), Fuzzy Capacitated Maximal Covering Location Problem (FCMCLP), Particle Swarm Optimization (PSO), Differential Evolution (DE), Artificial Bee Colony (ABC), Firefly Algorithm (FA)
Objavljeno v RUNG: 08.03.2023; Ogledov: 1819; Prenosov: 0
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3.
Design & analysis of clustering based intrusion detection schemes for e-governance
Rajan Gupta, Sunil K. Muttoo, Saibal K. Pal, 2016, objavljeni znanstveni prispevek na konferenci

Opis: The problem of attacks on various networks and information systems is increasing. And with systems working in public domain like those involved under E-Governance are facing more problems than others. So there is a need to work on either designing an altogether different intrusion detection system or improvement of the existing schemes with better optimization techniques and easy experimental setup. The current study discusses the design of an Intrusion Detection Scheme based on traditional clustering schemes like K-Means and Fuzzy C-Means along with Meta-heuristic scheme like Particle Swarm Optimization. The experimental setup includes comparative analysis of these schemes based on a different metric called Classification Ratio and traditional metric like Detection Rate. The experiment is conducted on a regular Kyoto Data Set used by many researchers in past, however the features extracted from this data are selected based on their relevance to the E-Governance system. The results shows a better and higher classification ratio for the Fuzzy based clustering in conjunction with meta-heuristic schemes. The development and simulations are carried out using MATLAB.
Ključne besede: particle swarm optimization, intrusion detection, anomaly detection, intrusion detection system, network intrusion detection
Objavljeno v RUNG: 02.04.2021; Ogledov: 2319; Prenosov: 9
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4.
Binary division fuzzy C-means clustering and particle swarm optimization based efficient intrusion detection for e-governance systems
Sunil K. Muttoo, Saibal K. Pal, 2016, izvirni znanstveni članek

Opis: With the rapid rise of technology, many unusual and unwanted patterns have been observed in the communication network andrespective systems. This may be attributed to the increase of external threats that cause many security concerns. Such anomalies and unusual behavior lead to a strong need of studying and designing the Intrusion Detection Systems and Clustering. Currently,a variety of clustering methods and their combinations are used to develop an efficient intrusion detection system, but some metrics like low detection rate and high false alarm rate make these models unsatisfactory. The problem of local minima for clustering technique makes their search ability less efficient. An evolutionary technique called particle swarm optimization algorithm, that is based on swarm intelligence, shows a high global maxima search capability. In this paper, these two techniques have been combined to present a novel approach called fuzzy based particle swarm algorithm for the implementation of intrusion detection system. The experiment was conducted on a new data set called Kyoto data set with more number of anomalies. The obtained results were compared with two traditional clustering techniques based on K-Means and Fuzzy C-Means. It was observed that the proposed algorithm outperformed the other two traditional methods on the basis of the Detection Rate and False Alarm rate. In past some researchers have presented the combination of Fuzzy Based Particle Swarm Optimization algorithm to improve the intrusion detection rate,but this rate has been further improved because the algorithm performance depends on the termination condition and the fitness function value which are new in the proposed algorithm. Moreover, cluster numbers have been considered differently in the past, whereas the proposed algorithm works only on binary clustering.
Ključne besede: intrusion detection, fuzzy C-means clustering, particle swarm optimization, detection rate, e-governance
Objavljeno v RUNG: 01.04.2021; Ogledov: 2199; Prenosov: 0
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5.
Meta-heuristic algorithms to improve fuzzy C-means and K-means clustering for location allocation of telecenters under e-governance in developing nations
Rajan Gupta, Sunil K. Muttoo, Saibal K. Pal, 2019, izvirni znanstveni članek

Opis: The telecenter, popularly known as the rural kiosk or common service center, is an important building block for the improvement of e-governance in developing nations as they help in better citizen engagement. Setting up of these centers at appropriate locations is a challenging task; inappropriate locations can lead to a huge loss to the government and allied stakeholders. This study proposes the use of various meta-heuristic algorithms (particle swarm optimization, bat algorithm, and ant colony optimization) for the improvement of traditional clustering approaches (K-means and fuzzy C-means) used in the facility location allocation problem and maps them for the betterment of telecenter location allocation. A dataset from the Indian region was considered for the purpose of this experiment. The performance of the algorithms when applied to traditional facility location allocation problems such as set-cover, P-median, and the P-center problem was investigated, and it was found that their efficiency improved by 20%–25% over that of existing algorithms.
Ključne besede: ant colony optimization, bat algorithm, common service center, e-governance, fuzzy clustering, meta-heuristic algorithm, particle swarm optimization
Objavljeno v RUNG: 01.04.2021; Ogledov: 2235; Prenosov: 10
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6.
Fuzzy C-means clustering and particle swarm optimization based scheme for common service center location allocation
Rajan Gupta, Sunil K. Muttoo, Saibal K. Pal, 2017, izvirni znanstveni članek

Opis: Common Service Centers (CSCs), which are also known as Tele-centers and Rural Kiosks, are important infrastructural options for any country aiming to provide E-Governance services in rural regions. Their main objective is to provide adequate information and services to a country’s rural areas, thereby increasing government-citizen connectivity. Within developing nations, such as India, many CSC allocations are being planned. This study proposes a solution for allocating a CSC for villages in a country according to their E-Governance plan. The Fuzzy C-Means (FCM) algorithm was used for clustering the village dataset and finding a cluster center for CSC allocation, and the Particle Swarm Optimization (PSO) algorithm was used for further optimizing the results obtained from the FCM algorithm based on population. In the context of other studies addressing similar issues, this study highlights the practical implementation of location modeling and analysis. An extensive analysis of the results obtained using a village dataset from India including four prominent states shows that the proposed solution reduces the average traveling costs of villagers by an average of 33 % compared with those of allocating these CSCs randomly in a sorted order and by an average of 11 % relative to centroid allocation using the FCM-based approach only. As compared to traditional approaches like P-Center and P-Median, the proposed scheme is better by 31 % and 14 %, respectively. Therefore, the proposed algorithm yields better results than classical FCM and other types of computing techniques, such as random search & linear programming. This scheme could be useful for government departments managing the allocation of CSCs in various regions. This work should also be useful for researchers optimizing the location allocation schemes used for various applications worldwide.
Ključne besede: common service centers, tele-centers, e-governance, location allocation, fuzzy C-means clustering, particle swarm optimization
Objavljeno v RUNG: 17.02.2021; Ogledov: 2700; Prenosov: 0
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