Application Of The Co-located Base Station Optimization Problems

Today, with the development of science and technology, the study of multiobjective optimization problem has become an urgent need. But traditional optimization methods could not solve the existing problems of complex society. In an era of mobile communication scale expands rapidly, reasonable solution of base station location selection problem in the construction of network has become an important way of reducing construction cost. Base station site belongs to a typical multi-objective optimization problem, it is the focus of our study that reduce the cost to meet the needs by the base station location selection optimization. In recent years, the development of intelligent algorithm show the excellent performance in various fields and applications, so the study of its effective algorithm has important scientific significance and application value. In this paper, the essence of the multi-objective optimization problem is discussed, and the advantages and disadvantages of many kinds of optimization algorithm are analyzed, then the artificial immune algorithm could be better used to solve the problem of base station location because of the good properties.In view of the base station location selection optimization problem, an improved immune algorithm is proposed and it is applied in the four cta with the base station location problem. The mainly work is shown as follows: 1) In order to reduce the cost of base station site, multi-objective affinity function based on loss of cost, cover function and 2G/3G/4G colocation cost is designed; 2) In order to improve the algorithm, the bargaining game theory is introduced into the immune multi-objective optimization algorithm and a bargaining game algorithm for solving multi-objective optimization problems is proposed; 3) Give the simulation experiments of the above two algorithms by the matlab software, and compared performance to other algorithms which are not improved.By the comparison, it is showed that two algorithms could satisfy the demands of network coverage as a relatively low cost of website, and efficiency of the algorithm is improved obviously.