A Coral Reefs Optimization algorithm for optimal mobile network deployment with electromagnetic pollution control criterion

doi:10.1016/j.asoc.2014.07.007

Applied Soft Computing

Volume 24, November 2014, Pages 239-248

https://doi.org/10.1016/j.asoc.2014.07.007 Get rights and content

Highlights

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A Mobile Network Deployment Problem is tackled using the CRO.
•
The Coral Reefs Optimization is based on corals and corals reefs biology.
•
A full description of the new algorithm is carried out.
•
Experimental comparison with alternative soft-computing algorithms is done.

Abstract

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 Alcalá de Henares (Madrid, Spain), improving the results obtained by alternative algorithms such as Evolutionary, Particle Swarm Optimization or Harmony Search algorithms.

Graphical abstract

Introduction

Mobile telecommunication market is one of the most important factors in the current global economy. As an example, the contribution of the mobile market to the UK economy was 2.2% in 2003, and the forecast is that it will increase up to 7.5% in 2015 [1], [2]. This is a global trend in developed countries and it is even stronger in developing countries [3]. In parallel with this boom of mobile communications, a social concern on the adverse effects of electromagnetic emissions from base stations, antennas and terminals, has grown in the last few years [4]. There are quite different opinions on this topic, from those that relate the relationship between continuous exposition to electromagnetic fields with different kinds of pathologies [5], to those who deny these effects or claim for more definitive studies before making a complete decision [6]. Attending these studies, and for prevention issues, several governments have warned about possible health risks due to mobile phones and electromagnetic fields exposition [7].

In spite of this, electromagnetic emission is still not considered as a key factor in mobile networks deployment problems. Instead, companies and researchers have focussed in the development of efficient algorithms for mobile network deployment mainly based on coverage and cost [8], [9], [10], [11], [12], [13], [14], [15], [16]. On the other hand, the majority of recent network deployments problems in the literature have been successfully tackled by using bio-inspired meta-heuristic approaches: problems of WiFi network design [17], optical networks [19], [20], UMTS networks [21] and of course mobile cellular networks [22], [23] are some examples.

The objective of this study is twofold: first, we tackle a novelly defined Mobile Network Deployment Problem (MNDP), in which one of the key objectives is the minimization of the electromagnetic pollution from new base station controllers allocated. This objective is managed together with more classical deployment objectives such as the maximization of the network coverage and the minimization of the deployment cost. Second, we apply a recently proposed meta-heuristic approach to solve the MNDP, the Coral Reefs Optimization (CRO) algorithm [24]. The CRO is an evolutionary bio-inspired approach, based on the simulation of the processes in a coral reefs, such as coral reproduction, growing, fighting for space in the reef or depredation. The CRO can be classified into a family of bio-inspired algorithms which tries to artificially simulate the behavior of a specific natural ecosystem to tackle optimization problems. Other examples of this family of meta-heuristics are the ant colony optimization algorithm [25], the Particle Swarm Optimization algorithm [26], artificial bee colony approach (ABC) [27] or the weed colonization algorithm [28], etc. In this paper we describe in detail the CRO approach, and its adaptations to tackle the MNDP, such as specific reproduction operators and repairing procedures. We show the good performance of the CRO in a real MNDP in a Spanish city, Alcalá de Henares (Madrid), where an electromagnetic measure map is available after several studies carried out in the zone on the impact of mobile telephony over the population. Comparison against different state of the art meta-heuristics is carried out in order to show the excellent performance of the proposed CRO algorithm.

The rest of the paper has been structured as follows: next section states the problem definition, and details the different problem's objectives. Section “The Coral Reefs Optimization algorithm” presents the CRO approach proposed to tackle the problem, with full details on its characteristics, including encoding, operators, repairing procedures and adaptations to the MNDP. Section “Experimental part: a case study in Alcalá de Henares (Madrid)” describes the experimental part of the paper, with the results obtained in a real MNDP problem in a Spanish city. A comparison with alternative Evolutionary Algorithm, Particle Swarm Optimization and Harmony Search approach is carried out at this stage. Section “Conclusion” closes the paper by giving some final remarks and conclusions.

Section snippets

Problem statement and notation

In this section we present the mathematical definition of the network deployment problem tackled in this paper. Let us consider a metropolitan area A, where the existing electromagnetic field (volts per meter V/m) has been measured defining a set of points $O = (o_{i}), i \in {1, \dots, O}$ , described by coordinates $(x_{i}^{o}, y_{i}^{o}) \in A$ . The rest of the problem's inputs are the following:

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Set $P = (p_{i}), i \in {1, \dots, P}$ of possible points to locate the new BTSs (Base Transceiver Stations). Each point is defined by its coordinates (x_i

Basic CRO algorithm

The CRO is a novel meta-heuristic approach based on corals’ reproduction and coral reefs formation. Basically, the CRO is based on the artificial modeling of a coral reef, Λ, consisting of a N × M square grid. We assume that each square (i, j) of Λ is able to allocate a coral (or colony of corals) Ξ_i,j, representing a solution to a given optimization problem, which is encoded as a string of numbers in a given alphabet $I$ . The CRO algorithm is first initialized at random by assigning some squares

Experimental part: a case study in Alcalá de Henares (Madrid)

This section presents the experimental part of the paper, where we show the results obtained by the CRO in a real MNDP problem in Alcalá de Henares, Madrid, Spain, and the comparison with existing alternative meta-heuristics performance. Alcalá de Henares is a medium-size city on the north-east of Madrid (Fig. 8(a)). With the increasing of the population, its mobile network needed an update in order to cover the new mobile communication necessities in different parts of the city. The Alcalá

Conclusions

In this paper we have solved a Mobile Network Deployment Problem (MNDP) using a new meta-heuristic technique, the Coral Reefs Optimization (CRO) algorithm. The CRO is a novel optimization algorithm based on a coral reef formation, which simulates the main processes in the reef such as coral reproduction, growing and depredation. In this paper the CRO has been fully described and adapted to the considered MNDP, which, in this case takes into account the minimization of the electromagnetic

Acknowledgements

This work has been partially supported by Spanish Ministry of Science and Innovation, under project numbers ECO2010-22065-C03-02 and TEC2011-28250-C02-02.

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View full text

A Coral Reefs Optimization algorithm for optimal mobile network deployment with electromagnetic pollution control criterion

Highlights

Abstract

Graphical abstract

Introduction

Section snippets

Problem statement and notation

Basic CRO algorithm

Experimental part: a case study in Alcalá de Henares (Madrid)

Conclusions

Acknowledgements

Technol. Forecast. Soc. Change

Pathophysiology

Biomed. Environ. Sci.

Health Policy

Eur. J. Operat. Res.

Comput. Operat. Res.

Comput. Operat. Res.

Exp. Syst. Appl.

Comput. Commun.

Appl. Soft Comput.

Ecol. Inform.

Exp. Syst. Appl.

The Contribution of Mobile Phones to the UK Economy

The Changing Economic Impact of Mobile Telephones

Mobile Telecommunications and Health Research Program Report, Mobile Telecommunications and Health Research Program