Applications Of Optimization Algorithms In Communication Signal Processing

Optimization problems exist in many subjects and fields and solving them properly is important to the development of our country's economy. In current years, there are many kinds of optimization algorithms which can be classified into several groups, such as classical optimization algorithms, artificial intelligent optimization algorithms, and hybrid optimization algorithms.Since many practical engineering optimization problems have the attributes of non-linearity, restriction nature, multi-mode and difficult to construct mathematics model, searching and designing new optimization algorithms that fit to solve engineering problems has been an important research interest in optimization fields. There is no one method that can deal with all of the encounted problems in practical applications, like that every coine has two sides. In this paper, many optimization theories and methods are combined effectively after detailed study of them and the properties of these methods in practicle applications are also investigated and revealed through appling them to engineering problems. These researches will obtain huge of application values under the background of fast development of our country.The main idea of this paper is to research the theory and interior mechanism, algorithm innovation of the artificial intelligence, convex optimization and game theory, and to apply them to practicle problems in wireless communications and signal processing. In subject selection, it begins with the genetic algorithm, particle swarm optimization and artificial fish swarm algorithm, and merges them with some important concepts from game theory. In theory discussion, it demonstrates the main details of the algorithm theory, structure, and algorithm implementation. In applications, it applies the improved algorithms to the problems appeared in wireless communication fields. In algorithms innovations, this paper introduces the concepts of Nash equilibrium, maximin strategy and non-dominated sorting algorithm to artificial intelligence algorithms, in which the complicated multi-objective optimization problems are solved effectively. In simulation results, it pays more attention to the analysis and comparison between different methods. The results show that the improved algorithms achieved better global search ability and fast convergence speed.Mainly based on the applications of different methods, this paper starts with algorithm analysis and simulation test, coupled with engineering applications. The importance of optimization theory research and innovation is underlined more than only utilizing them. The contents of each chapter have some inter-associations with each other, so the paper has more systematicness. Finally, the paper concludes the performance and characteristic of the improved algorithms in practical applications, and gives the development tendency of the optimization algorithms.