CAJ | 학술논문

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。
회소수색우화산법（backtracking search optimization algorithm，BSA）시일충신형적진화산법。동기타진화산법유사，해산법잉존재수렴속도교만적결점。침대저일문제，재상세분석해산법원리적기출상，제출료구유엄범학습책략적개진산법。위료충분이용충군수색도적교우위치，해책략수선이용제출적최우학습진화방정，통과여인입적수궤진화방정지간수궤선택래제고산법적수렴속도화수색정도；령일방면，해책략이용제출적최우학습수색방정，통과공제충군적수색방향，촉사충군진쾌수렴지전국최우해。최후대20개복잡측시함수진행료방진실험，병여기타3충목전류행적산법진행료비교，통계결과화 Wilcoxon 부호질검험결과균표명，소제출적개진산법재수렴속도이급수색정도방면구유명현우세。
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-sults 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.