In this paper we apply a novel meta-heuristic approach, the Coral Reefs optimization (CRO) algorithm, to solve a Mobile network Deployment Problem (MNDP), in which the control of the electromagnetic pollution plays an...
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In this paper we apply a novel meta-heuristic approach, the Coral Reefs optimization (CRO) algorithm, to solve a Mobile network Deployment Problem (MNDP), in which the control of the electromagnetic pollution plays an important role. The CRO is a new bio-inspired meta-heuristic algorithm based on the growing and evolution of coral reefs. The aim of this paper is therefore twofold: first of all, we study the performance of the CRO approach in a real hard optimization problem, and second, we solve an important problem in the field of telecommunications, including the minimization of electromagnetic pollution as a key concept in the problem. We show that the CRO is able to obtain excellent solutions to the MNDP in a real instance in Alcala de Henares (Madrid, Spain), improving the results obtained by alternative algorithms such as Evolutionary, Particle Swarm optimization or Harmony Search algorithms. (C) 2014 Elsevier B.V. All rights reserved.
Electromagnetic pollution due to mobile telephony is one of the most concerning problems arising since the spreading of this technology. Different studies have shown the relationship between continuous exposition to e...
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Electromagnetic pollution due to mobile telephony is one of the most concerning problems arising since the spreading of this technology. Different studies have shown the relationship between continuous exposition to electromagnetic fields and different kinds of pathologies. Despite this, the electromagnetic danger for exposition is not taken into account in recent mobile network deployments. In this paper we propose a novel evolutionary algorithm for mobile networks deployment, which takes into account the control of the electromagnetic emission from the base stations as one of the key design parameters. The proposed evolutionary approach is a variable-length algorithm, able to produce solutions with different number of base stations. We detail the encoding, operators and a repairing procedure applied to obtain good solutions in terms of coverage, cost and electromagnetic pollution. The algorithm has been tested in a real problem of mobile network deployment in Alcala de Henares, Madrid, Spain, and compare with a greedy (constructive) approach and a meta-heuristic algorithm (Harmony Search), obtaining very good results. (C) 2012 Elsevier Ltd. All rights reserved.
This paper presents the application of a Hybrid Grouping Genetic Algorithm (HGGA) to solve the problem of deploying metropolitan wireless networks. In particular, the exploitation of the existing broadband infrastruct...
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This paper presents the application of a Hybrid Grouping Genetic Algorithm (HGGA) to solve the problem of deploying metropolitan wireless networks. In particular, the exploitation of the existing broadband infrastructure (e.g.. ADSL networks) by "opening up" WiFi-enabled routers to third party users, is considered to produce a complex problem, henceforth call WiFi network Design Problem or WiFiDP. The application of a HGGA to this problem produces cost-effective network deployment plans, considering real life aspects such as budget (the total cost of deployment - i.e. the cost of opening all selected DSL routers for public use - should not exceed the allocated budget) and DSL router characteristics (coverage, DSL capacity at a specific location, unit price, etc.) The hybrid grouping genetic algorithm proposed incorporates a particular encoding to tackle the WiFiDP, in which the group part also includes the type of router to be installed. Also, a modification of this encoding to consider the working frequencies of routers is presented in this paper. Moreover, a repairing and local search procedures are added to the algorithm to obtain better performance and always find viable solutions. The performance and effectiveness of the proposed HGGA is evaluated using two randomly generated WiFiDP instances (considering 1000 and 2000 users), used to perform several experiments. The comparison of the proposed HGGA results against those of a greedy optimization algorithm (previously proposed to solve the WiFiDP) shows the better performance of this approach. Finally, the application of the HGGA to real datasets in the cities of Berlin (Germany) and Torrejon de Ardoz (Spain) is also reported in the experimental part. In real conditions, the HGGA keeps performing better than previous methods. (C) 2011 Elsevier Ltd. All rights reserved.
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