Search

Sparrow search algorithm (SSA) is easy to fall into local convergence and convergence stagnation. In order to solve these problems, this paper introduced Circle chaos map into the original SSA to improve its global search ability at the beginning of iteration. Meanwhile, it introduced T-distribution variation to affect the sparrow population position update rules in different iteration periods. Finally, we constructed the “similarity function” to measure the “dispersion” of the sparrow population, and formulated the search rules of the sparrow population under different “dispersion”. In order to test the specific optimization performance of the proposed algorithm, the test results of 54 test functions are compared with those of 9 other algorithms which are widely used, and then the test results are analyzed using non-parametric tests in statistics. At the same time, this paper introduces this algorithm into three concrete engineering test problems for testing. The results of these tests all prove that the proposed algorithm has stronger global optimization ability and higher convergence precision compared with other algorithms.

Aiming at the shortages of basic flower pollination algorithm (FPA) with slow convergence speed, low search precision, and easy to fall into local optimum, a new adaptive FPA based on opposition-based learning and t-distribution (OTAFPA) was proposed and be applied to the social networks. First, the opposition-based learning strategy is utilized to increase the diversity and quality of the initial population. Then, the adaptive dynamic switching probability is introduced, which can effectively balance the global and local search according to the current number of iterations. Finally, the t-distribution variation is used to increase the population diversity and to help the algorithm jump out of the local optimum. The simulation experiments on eight classical test functions show that OTAFPA has better global optimization ability, which improves the convergence speed and the solution accuracy of the algorithm. The OTAFPA also shows superior performance in practical applications of user identification across social networks.