Soumen Atta

Genetic Algorithm Based Approaches to Install Different Types of Facilities

Given a set P of n-points (customers) on the plane and a positive integer k (1 ≤ k ≤ n), the objective is to find a placement of k circles (facilities) such that the union of k circles contains all the points of P and the sum of the radii of the circles is minimized. We have proposed a Genetic Algorithm (GA) to solve this problem. In this context, we have also proposed two different algorithms for k=1 and 2. Finally, we have proposed a GA to solve another optimization problem to compute a placement of fixed number of facilities where the facilities are hazardous in nature and the range of each such facility is circular.

Solving maximal covering location problem using genetic algorithm with local refinement

The maximal covering location problem (MCLP) deals with the problem of finding an optimal placement of a given number of facilities within a set of customers. Each customer has a specific demand and the facilities are to be placed in such a way that the total demand of the customers served by the facilities is maximized. In this article an improved genetic algorithm (GA)-based approach, which utilizes a local refinement strategy for faster convergence, is proposed to solve MCLP. The proposed algorithm is applied on several MCLP instances from literature and it is demonstrated that the proposed GA with local refinement gives better results in terms of percentage of coverage and computation time to find the solutions in almost all the cases. The proposed GA-based approach with local refinement is also found to outperform the other existing methods for most of the small as well as large instances of MCLP.

Power-aware traffic grooming in WDM optical mesh networks for bandwidth wastage minimization: A genetic algorithm-based approach

The cost of optical backbone network has increased nowadays. So we need to reduce this cost. One of the major contributory costs is the power consumed by the underlying network. Power may also be consumed by different network equipments viz. add-drop multiplexers (ADM), Network Interface Device (NID), Optical Network Terminal (ONT), electrical-to-optical-to-electrical (EOE) conversion etc. In this article we have only considered the power consumption by EOE conversion in a mesh network. We have proposed a genetic algorithm to minimize the EOE conversions needed for a mesh network to satisfy all the traffic requests for a given physical topology. We have also considered the amount of wavelength wastages for our solution and we have minimized these wastages below a user given value. The results have been demonstrated on two optical mesh networks.

Solving Uncapacitated Facility Location Problem Using Monkey Algorithm

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.

Deterministic and Randomized Heuristic Algorithms for Uncapacitated Facility Location Problem

A well-known combinatorial optimization problem, known as the Uncapacitated Facility Location Problem (UFLP) is considered in this paper. Given a set of customers and a set of potential facilities, the objective of UFLP is to open a subset of the potential facilities such that sum of the opening cost for opened facilities and the service cost of customers is minimized. In this paper, deterministic and randomized heuristic algorithms are presented to solve UFLP. The effectivenesses of the proposed algorithms are tested on UFLP instances taken from the OR-Library. Although the proposed deterministic algorithm gives optimal results for most of the instances, the randomized algorithm achieves optimal results for all the instances of UFLP considered in this paper including those for which the deterministic algorithm fails to achieve the optimal solutions.

An efficient algorithm for PMFAP

Perturbation-Minimizing Frequency Assignment Problem (PMFAP) is a frequency assignment problem in which newly generated demands are satisfied with minimum change 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. The proposed algorithm also works for the well-known Frequency Assignment Problem (FAP) and its performance is compared with the existing results for the standard benchmark data sets.

Multi-objective k-Center Sum Clustering Problem

Given a set P of n objects in two dimensional plane and a positive integer k ( ≤ n), we have considered the problem of partitioning P into k clusters of circular shape so as to minimize the following two objectives: (i) the sum of radii of these k circular clusters and (ii) the number of points of P covered by more than one circular cluster. The NSGA-II based multi-objective genetic algorithm (MOGA) has been proposed to solve this problem.

L(4, 3, 2, 1)-Labeling for Simple Graphs

An L(4, 3, 2, 1)-labeling of a graph is a function which assigns label to each vertex of the graph such that if two vertices are one, two, three and four distance apart then assigned labels must have a difference of at least 4, 3, 2 and 1 respectively between them. This paper presents L(4, 3, 2, 1)-labeling number for simple graphs such as complete graphs, complete bipartite graphs, stars, paths and cycles. This paper also presents an L(4, 3, 2, 1)-labeling algorithm for paths which is optimal for paths on \( n \ge 7 \) vertices.