基于变异交叉方程与进化选择机制的回溯优化改进算法

针对回溯搜索优化算法存在的收敛速度慢,容易陷入局部最优等问题,提出了一种改进算法。首先利用t分布产生变异尺度系数,加快了算法收敛速度；接着完善交叉方程结构,引入最优个体控制种群搜索方向,有效提高了算法开发能力；最后提出进化选择机制,引入差分进化算法变异因子,一定概率下以较差解替换较优解,避免算法陷入局部最优。在数值实验中,选取了15个测试函数进行仿真测试,并与5种表现良好的算法进行了比较,结果表明,该算法在收敛速度及搜索精度方面有明显优势。

Backtracking Search Optimization Algorithm Based on Mutation and Crossing Equations and Evolutionary Selection Mechanism

According to the slow convergence and easiness to trap in local optimum of backtracking search optimization algorithm, an improved algorithm was presented. The mutation scale factor was generated by t distribution was proposed firstly to speed up the convergence rate; then the method improved the structure of crossover equation and introduced the optimal individual to control the direction of population search, which effectively improved the development capability. Finally, the algorithm proposed the evolutionary selection mechanism, introduced the mutation factor of differential evolution algorithm and replaced the optimal solution with worse solution under a certain probability, which can avoid algorithm to fall into the local optimum. In the numerical experiments, 15 test functions were selected for simulation and compared with 5 well-behaved algorithms. The results show that the proposed algorithm has obvious advantages in terms of convergence rate and search accuracy.