Spatial Feature Aware Genetic Algorithm of Network Base Station Configuration for Internet of Things
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Network configurations, which maximize the accessed number of the sensor devices in IoT subjected to limited active base stations is an important topic. The weakness of traditional genetic algorithms mainly lies in that the spatial feature, i.e., the geometry distribution of base stations, is not considered. A novel genetic algorithm, in which the spatial feature of base stations is taken into account, to obtain the optimal subset of base stations in IoT is proposed. The crossover operation and the mutation operation are designated based on the spatial characteristic. Experiments have been conducted to prove the proposed algorithm for the network configuration.Keywords:
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To overcome the shortcomings of classical genetic algorithm for solving flight optimization problem, improved genetic algorithm is proposed. Structure of genetic algorithm is improved first. The mutation operation is separated from the crossover operation. The second, realization of genetic operation is modified. In crossover operation, the principal of best to best is used in the individual match for the crossover operation. The selection of the crossover point is made out by chaotic series. Crossover of one point is carried out to ensure precision of the algorithm, to weaken and avoid the oscillation in the process of optimization. In the mutation, a few genes are varied by chaotic operator to avoid prematurity of the algorithm. In the end, the improved genetic algorithm is compared with two usual genetic algorithms in solving flight optimization problem.
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The problem of automatic selection and configuration of base station sites in the dynamic scenario is investigated. We propose a intelligent approach based on Genetic Algorithm used to adjust the level of the antenna power and select the base stations’ site from the candidate sites. According to the users’ density the base stations adjust their antenna radiated power automatically. Results are given that most of base stations are powered off when the density becomes very low, and show that it will save lots of energy resources. Since this algorithm can adjust the status of base stations intelligently and automatically, it can be used in SON.
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How to select and combine many services with similar functions reasonably and efficiently to provide users with better service is the main challenge in the service composition problem. This is thorny when the number of the candidate Services is huge. Recently, researches transform the service compositions problem as a multi-objective optimizing task, and then the genetic algorithm is commonly used to tackle this issue. However, the fixed crossover probability and mutation probability settings in genetic algorithm usually result to it falls into a local optimal. To improve the performance of the genetic algorithm in the service composition task, this paper proposes an adaptive parameter adjust strategy, which can adjust the crossover probability and mutation probability automatically. The experiment result shows our method has greatly improved the maximum fitness of the final solutions of traditional genetic algorithm.
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