具有广泛学习策略的回溯搜索优化算法

Backtracking search optimization algorithm with comprehensive learning strategy

回溯搜索优化算法(backtracking search optimization algorithm,BSA)是一种新型的进化算法。同其他进化算法类似,该算法仍存在收敛速度较慢的缺点。针对这一问题,在详细分析该算法原理的基础上,提出了具有广泛学习策略的改进算法。为了充分利用种群搜索到的较优位置,该策略首先利用提出的最优学习进化方程,通过与引入的随机进化方程之间随机选择来提高算法的收敛速度和搜索精度;另一方面,该策略利用提出的最优学习搜索方程,通过控制种群的搜索方向,促使种群尽快收敛至全局最优解。最后对20个复杂测试函数进行了仿真实验,并与其他3种目前流行的算法进行了比较,统计结果和Wilcoxon符号秩检验结果均表明,所提出的改进算法在收敛速度以及搜索精度方面具有明显优势。

The backtracking search optimization algorithm （BSA） is a novel evolution algorithm. However, the BSA has the problem of low convergence speed as the same as the other evolution algorithms. Aiming at this problem, an improved BSA with the comprehensive learning strategy is proposed based on detailed analysis of BSA. The strategy is used for making full use of the better solutions that the population obtains. Firstly, the global best learning equation is proposed and the random evolution equation is introduced in the strategy. They are chosen randomly so as to improve the convergence speed and precision of the improved algorithm. Secondly, in order to control the search direction, the global best search equation is proposed in the strategy so as to reach the global best solution as fast as possible. Finally, 20 complex benchmarks and other three popular algorithms are compared to illustrate the superiority of BSA with comprehensive learning strategy. The experimental re- suits and the Wilcoxon signed ranks test results show that the BSA with comprehensive learning strategy out- performed the other three algorithms in terms of convergence speed and precision.