The radio network design (RND) is an NP-hard optimization problem which consists of the maximization of the coverage of a given area while minimizing the base station deployment. Solving RND problems efficiently is relevant to many fields of application and has a direct impact in the engineering, telecommunication, scientific, and industrial areas. Numerous works can be found in the literature dealing with the RND problem, although they all suffer from the same shortfall: a noncomparable efficiency. Therefore, the aim of this paper is twofold: first, to offer a reliable RND comparison base reference in order to cover a wide algorithmic spectrum, and, second, to offer a comprehensible insight into accurate comparisons of efficiency, reliability, and swiftness of the different techniques applied to solve the RND problem. In order to achieve the first aim we propose a canonical RND problem formulation driven by two main directives: technology independence and a normalized comparison criterion. Following this, we have included an exhaustive behavior comparison between 14 different techniques. Finally, this paper indicates algorithmic trends and different patterns that can be observed through this analysis.

In this paper we present a Differential Evolution based algorithm used to solve the Radio Network Design (RND) problem. This problem consists in determining the optimal locations for base station transmitters in order to get a maximum coverage area with a minimum number of transmitters. Because of the very high amount of possible solutions, this problem is suitable to be tackled with evolutionary techniques, so in our work it has been developed an algorithm inspired on the well-known Differential Evolution algorithm, obtaining good results.

The Radio Network Design (RND) problem for wide area networks consists of determining the optimal locations for base station transmitters in order to get a maximum coverage area with a minimum number of transmitters. Because of the grand amount of possible solutions, this problem is most suitable to be tackled with bio-inspired techniques instead of classical approaches. Our recent research work exploited a differential evolution based algorithm to deal with this problem. This paper describes an enhanced implementation of our Differential Evolution-based algorithm that is able to tackle the RND problem in a most effective and faster way that the one observed in our previous work. This approach includes the comparison of different operators and the usage of an alternate initialisation model.