关键词: 改进回溯搜索优化算法, 柯西种群生成策略, 组合变异策略, 越界处理策略, 函数优化

Abstract:

Aiming at the shortcomings of backtracking search optimization algorithm with slow convergence speed and easy to fall into local optimum, an improved backtracking search optimization algorithm based on combined mutation strategy is proposed. In order to improve the diversity of historical populations and expand the search space of the algorithm, the Cauchy population generation strategy is used in the iterative process of the algorithm to generate historical populations using Cauchy distribution scale coefficients. A combination based on chaotic map and Gamma distribution is introduced. The mutation strategy mutates the poor individuals to generate better quality individuals under certain probability. The out-of-bounds processing strategy is adopted for the cross-border individuals in the new population to ensure that the algorithm searches within the predetermined search space. In this paper, eleven standard test functions are selected, and numerical simulations are carried out in low-dimensional and high-dimensional states, and compared with three well-performing algorithms. The results show that the improved algorithm has great advantages in convergence speed and convergence precision.

Key words: improved backtracking search optimization algorithm, Cauchy population generation strategy, combined mutation strategy, out-of-bounds processing strategy, function optimization