柯西自适应回溯搜索与最小二乘支持向量机的集成预测模型

针对在最小二乘支持向量机（LSSVM）的核函数参数和正则化参数优化中回溯搜索优化算法（BSA）易早熟、局部开采能力弱等问题，提出了一种集成预测模型CABSA-LSSVM。首先采用柯西种群生成策略增加历史种群的多样性使算法不易陷入局部最优解，然后利用自适应变异因子策略调节变异尺度系数以平衡算法的全局勘探和局部开采能力，最后运用改进后的柯西自适应回溯搜索算法（CABSA）优化LSSVM以形成新的集成预测模型。选取10个UCI数据集进行数值实验，结果表明所提模型CABSA-LSSVM在种群规模为80时回归预测性能最优，且与标准BSA、粒子群优化（PSO）算法、人工蜂群（ABC）算法、灰狼优化（GWO）算法优化的LSSVM相比，该模型的决定系数提升了1.21%~15.28%，预测误差降低了6.36%~29.00%，运行时间降低了5.88%~94.16%，可见该模型具有较高的预测精度和较快的计算速度。

Integrated prediction model of Cauchy adaptive backtracking search and least square support vector machine

Aiming at the problem that Backtracking Search optimization Algorithm （BSA） is easy to premature and has weak local development ability in the optimization of kernel function parameters and regularization parameters of Least Square Support Vector Machine （LSSVM）， an integrated prediction model named CABSA-LSSVM was proposed. Firstly， the Cauchy population generation strategy was used to improve the diversity of historical populations， so that the algorithm was not easy to fall into the local optimal solution. Then， the adaptive mutation factor strategy was used to balance the global exploration and local development abilities of the algorithm by adjusting the mutation scale coefficient. Finally， the improved Cauchy Adaptive Backtracking Search Algorithm （CABSA） was used to optimize the LSSVM to form a new integrated prediction model. Ten UCI datasets were selected for numerical experiments. The results show that the proposed model CABSA-LSSVM has the best regression prediction performance when the population size is 80. Compared with the LSSVMs optimized by the standard BSA， Particle Swarm Optimization （PSO） algorithm， Artificial Bee Colony （ABC） algorithm and Grey Wolf Optimization （GWO） algorithm， the proposed model has the coefficient of determination increased by 1.21%-15.28%， the prediction error reduced by 6.36%-29.00%， and the running time reduced by 5.88%-94.16%. In conclusion， the proposed model has high prediction accuracy and fast computation speed.