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Perturbation-minimising frequency assignment to address short term demand fluctuation in cellular network
Soumen Atta, Priya Ranjan Sinha Mahapatra, 2018, izvirni znanstveni članek

Opis: In cellular network short term demand fluctuation is a very common phenomenon. The demand of any cell may increase or decrease slightly or the system may expand by adding additional cells or the system may shrink if the demands of certain number of cells become zero. In this paper, the perturbation-minimising frequency assignment problem (PMFAP) is considered to address the short term fluctuation in demand vector. PMFAP is a frequency assignment problem in which newly generated demands are satisfied with minimum changes in the already existing frequency assignment keeping all the interference constraints. In this paper, an efficient heuristic algorithm for PMFAP is presented. The efficiency of this algorithm is compared with the existing results from literature. With a slight modification to the proposed algorithm, it can solve the well-known frequency assignment problem (FAP) and its performance is also compared with the existing results using the standard benchmark data sets for FAP.
Ključne besede: short term demand fluctuation, frequency assignment problem, FAP, PMFAP, cellular network, perturbation, heuristic algorithm
Objavljeno v RUNG: 17.04.2023; Ogledov: 1115; Prenosov: 0
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Solving uncapacitated facility location problem using monkey algorithm
Soumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2018, objavljeni znanstveni prispevek na konferenci

Opis: The Uncapacitated Facility Location Problem (UFLP) is considered in this paper. Given a set of customers and a set of potential facility locations, the objective of UFLP is to open a subset of facilities to satisfy the demands of all the customers such that the sum of the opening cost for the opened facilities and the service cost is minimized. UFLP is a well-known combinatorial optimization problem which is also NP-hard. So, a metaheuristic algorithm for solving this problem is natural choice. In this paper, a relatively new swarm intelligence-based algorithm known as the Monkey Algorithm (MA) is applied to solve UFLP. To validate the efficiency of the proposed binary MA-based algorithm, experiments are carried out with various data instances of UFLP taken from the OR-Library and the results are compared with those of the Firefly Algorithm (FA) and the Artificial Bee Colony (ABC) algorithm.
Ključne besede: Uncapacitated Facility Location Problem (UFLP), Simple Plant Location Problem (SPLP), Warehouse Location Problem (WLP), Monkey Algorithm
Objavljeno v RUNG: 17.04.2023; Ogledov: 1231; Prenosov: 0
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A multi-objective formulation of maximal covering location problem with customers’ preferences: Exploring Pareto optimality-based solutions
Soumen Atta, Priya Ranjan Sinha Mahapatra, Anirban Mukhopadhyay, 2021, izvirni znanstveni članek

Opis: The maximal covering location problem (MCLP) is a well-known combinatorial optimization problem with several applications in emergency and military services as well as in public services. Traditionally, MCLP is a single objective problem where the objective is to maximize the sum of the demands of customers which are served by a fixed number of open facilities. In this article, a multi-objective MCLP is proposed where each customer has a preference for each facility. The multi-objective MCLP with customers’ preferences (MOMCLPCP) deals with the opening of a fixed number of facilities from a given set of potential facility locations and then customers are assigned to these opened facilities such that both (i) the sum of the demands of customers and (ii) the sum of the preferences of the customers covered by these opened facilities are maximized. A Pareto-based multi-objective harmony search algorithm (MOHSA), which utilizes a harmony refinement strategy for faster convergence, is proposed to solve MOMCLPCP. The proposed MOHSA is terminated based on the stabilization of the density of non-dominated solutions. For experimental purposes, 82 new test instances of MOMCLPCP are generated from the existing single objective MCLP benchmark data sets. The performance of the proposed MOHSA is compared with the well-known non-dominated sorting genetic algorithm II (NSGA-II), and it has been observed that the proposed MOHSA always outperforms NSGA-II in terms of computation time. Moreover, statistical tests show that the objective values obtained from both algorithms are comparable.
Ključne besede: Maximal covering location problem (MCLP), Multi-objective MCLP, Customers’ preferences, Multi-objective harmony search algorithm (MOHSA), NSGA II, CPLEX
Objavljeno v RUNG: 17.04.2023; Ogledov: 1056; Prenosov: 0
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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: 1808; Prenosov: 0
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Least-square fit : written report
Aleksander Đorđević, 2021, raz. nal. na višji ali visoki šoli

Ključne besede: least-square fit, non-linear regression, Levenberg-Marquardt algorithm
Objavljeno v RUNG: 28.06.2021; Ogledov: 2496; Prenosov: 0
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BAT algorithm for improving fuzzy C-means clustering for location allocation of rural kiosks in developing countries under e-governance
Rajan Gupta, Sunil K. Muttoo, Saibal K. Pal, 2016, izvirni znanstveni članek

Opis: Rural Kiosks are important infrastructural pillar in rural regions for internet and basic technology facility all around the world. They are also known as Tele-centers or Common Service Centers and are majorly used by government to promote Electronic Governance. The major characteristic of setting up of Rural Kiosk is their appropriate location so that people from rural region can avail the services at minimum travel cost and time. There are lot of traditional schemes used by researchers in past for location allocation but this paper proposes the usage of Fuzzy C-Means clustering and BAT algorithm to optimize the location of Rural Kiosk. The meta-heuristic approach has produced better results as compared to normal graph theories in past. The experiment has been conducted on a random data set of 72 village locations from India and their clusters are formed. It is found that using only Fuzzy C-Means clustering to allocate the center and by using it in combination with BAT algorithm produced up to 25% of efficient results. This can drastically help the key stakeholders in allocation of these Rural Kiosks at right places so as to maximize their utility.
Ključne besede: BAT algorithm, location allocation, rural kiosks, fuzzy C-means, e-governance, tele-centers, common service centers
Objavljeno v RUNG: 01.04.2021; Ogledov: 2191; Prenosov: 0
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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: 2230; Prenosov: 10
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