A New Improved Beetle Swarm Optimization Algorithm

Aiming at the problem that the standard particle swarm optimization algorithm (PSO) is easy to fall into local convergence so quickly, and the convergence speed is slow, a new optimization algorithm combining PSO, Beetle Antennae Search Algorithm and Genetic Algorithm is proposed in this paper First, the improved sigmoid function is introduced to form a nonlinear function relation between the learning factor and the step size of the particle swarm optimization algorithm, which can effectively increase the convergence speed. Second, in order to maintain the diversity of the population, the crossover operation and mutation operation of genetic algorithm are introduced to improve the global search ability of the algorithm. In order to verify the performance of the new optimization algorithm, the average fitness values of the four standard test functions in different dimensions are compared to and the corresponding optimal values. The simulation results show that the new algorithm not only improves the premature convergence of the traditional particle swarm optimization algorithm, but also improves the optimization efficiency.