With the increase of the dimension of the problem to be optimized, Many-objective Optimization Problem (MAOP) will form a huge target space, resulting in a sharp increase of the proportion of non-dominant solutions. And the selection pressure of evolutionary algorithms is weakened and the efficiency of evolutionary algorithms for solving MAOP is reduced. To solve this problem, a Particle Swarm Optimization (PSO) algorithm using hyper-spherical dominance relationship to reduce the number of non-dominant solutions was proposed. The fuzzy dominance strategy was used to maintain the selection pressure of the population to MAOP. And the distribution of individuals in the target space was maintained by the selection of global extremum and the maintenance of external files. The simulation results on standard test sets DTLZ and WFG show that the proposed algorithm has better convergence and distribution when solving MAOP.

When traditional binary-coded evolutionary algorithms are applied to optimize functions, the mutual interference between different dimensions would prevent effective restructuring of some low-order modes. A new evolutionary algorithm, called Dynamic Chromosomal Translocation-based Evolutionary Algorithm (CTDEA), was proposed based on cytological findings. This algorithm simulated the structuralized process of organic chromosome inside cells by constructing gene matrixes, and realized modular translocations of homogeneous chromosomes on the basis of gene matrix, in order to maintain the diversity of populations. Moreover, the individual fitness-based population-dividing method was adopted to safeguard elite populations, ensure competitions among individuals and improve the optimization speed of the algorithm. Experimental results show that compared with existing Genetic Algorithm (GA) and distribution estimation algorithms, this evolutionary algorithm greatly improves the population diversity, keeping the diversity of populations around 0.25. In addition, this algorithm shows obvious advantages in accuracy, stability and speed of optimization.

Considering the non-ideal factors of the actual impedance matching network, and in order to achieve low Standing Wave Ratio (SWR) and high transmission efficiency, an impedance matching method for radio frequency antenna was proposed based on Dual-aimed Chaotic Particle Swarm Optimization (DCPSO) algorithm. The experimental results of single-frequency impedance matching obtained by DCPSO proves that SWR and output power performance are improved compared with Particle Swarm Optimization (PSO). Combined with real frequency method, broadband impedance matching experiments were done with working band of 2G, 3G and 4G mobile technologies, results show that the entire band can get good results with impedance matching transmission efficiency.

Random sequence as the basis for information security, the quality depends on its use of random source, how to get high-quality random source research in the field of information security is one of the difficulties. Portrait facial features through the use of the difference and get (shoot) random process, a new image as a random source of portraits of random sequence generation methods; through the image acquisition process and the human biological characteristics of random noise a combination of random source. Simulation tests show that this method does not have a random sequence generated by linear correlation and nonlinear correlation, with excellent uniformity and FIPS PUB 140-2 and NIST 800-22 test pass rates to meet the needs of information security, and the method is simple and easy to implement.