Combinatorial optimization algorithms for radio network planning

Abstract

This paper uses a realistic problem taken from the telecommunication world as the basis for comparing di erent combinatorial optimization algorithms. The problem recalls the minimum hitting set problem, and is solved with greedy-like, Darwinism and genetic algorithms. These three paradigms are described and analyzed with emphasis on the Darwinism approach, which is based on the computation of -nets.

Comparison of Radio Wave Propagation Models for Mobile Networks

“…Each service area, called a cell, is a set of points at which the intensity of the signal received from the BS under consideration is higher than that received from the other BSs. Cells can have different shapes and sizes depending on the BSs location, configuration parameters, and propagation properties [7].…”

Evolutionary algorithms for solving the automatic cell planning problem: a survey

“…Rather specific evolutionary techniques such as CHC (Eshelman (1991)), Differential Evolution (DE, Storn and Price (1995)), PBIL (Baluja (1994)), or Artificial Immune Systems (AIS, de Melo Carvalho Filho and de Alencar (2008)) are also found. It can be seen that not only sequential approaches exist, but also parallel models deployed on standard parallel platforms such as clusters of computers (dGAs, Alba and Chicano (2005), Calégari et al (2001)) and even grid computing systems (Talbi et al (2007)). If multiobjective approaches are considered, NSGA-II (Deb et al (2002)) and SPEA2 (Zitzler et al (2002)), the two best known algorithms in the evolutionary multiobjective research community have been applied in eight of the analyzed works.…”

“…From the point of view of the formulation, the first proposals have adopted a single objective approach in which the different network aspects to be optimized are weighted into a single (aggregative) function (Calégari et al (1997), Chamaret and Condevaux-Lanloy (1998), Lieska et al (1998), Reininger et al (1999)). However, the recent advances in multi- Calégari et al (1996Calégari et al ( , 1997Calégari et al ( , 2001) dGA …”

Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem

“…Despite some resemblances to Calégari's [9], [10] work on genetic algorithmic (GA) approaches for radio network optimization for mobile systems, developed in the mid-1990s, this field of research actually focuses on the principle of minimization of resources rather than on achieving the total coverage of an area, since in most real-world-problem cases, these latter scenarios are uncommon. Calégari GAs adopted the graph-maximum independent-set search method which attempts to find the largest independent-set in a graph.…”