Search the repository

A+ | A- | | SLO | ENG

Keywords: hypergroups, Euler's function, fuzzy grades, commutativity degrees
Published in RUNG: 05.07.2023; Views: 2318; Downloads: 4
Link to file
This document has many files! More...

Abstract: 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.
Keywords: 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)
Published in RUNG: 08.03.2023; Views: 2863; Downloads: 0
This document has many files! More...

Abstract: Abstract. The subalgebra of BCK/BCI-algebra using Łukasiewicz anti fuzzy set introduced by Jun is studied in this article. The concept of Łukasiewicz anti fuzzy subalgebra of a BCK/BCI-algebra is introduced, and several properties are investigated. The relationship between anti fuzzy subalgebra and Łukasiewicz anti fuzzy subalgebra is given, and the characterization of a Łukasiewicz anti fuzzy subalgebra is discussed. Conditions are found in which a Lukasiewicz anti fuzzy set is a Lukasiewicz anti fuzzy subalgebra Finally, conditions under which ⋖-subset, Υ- subset, and anti-subset become subalgebra are explored.
Keywords: Anti fuzzy subalgebra, Łukasiewicz anti fuzzy set, Łukasiewicz anti fuzzy subalgebra, ⋖-subset, Υ-subset, anti subset
Published in RUNG: 20.02.2023; Views: 1732; Downloads: 0
This document has many files! More...

Keywords: dependence relation, degree of influence, degree of impact, fuzzy set, hypercompositional algebra
Published in RUNG: 10.08.2021; Views: 2644; Downloads: 52
Full text (216,43 KB)

Abstract: 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.
Keywords: intrusion detection, fuzzy C-means clustering, particle swarm optimization, detection rate, e-governance
Published in RUNG: 01.04.2021; Views: 2797; Downloads: 0
This document has many files! More...