计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 135-138.doi: 10.11896/JsJkx.190800112
杨凯中, 提梦桃, 谢英柏
YANG Kai-zhong, TI Meng-tao and XIE Ying-bai
摘要: 优化问题广泛存在于工程技术、经济管理等各个领域。实际问题的复杂性,导致传统的优化方法难以解决这些问题。随着迭代计算过程的推进,标准蝙蝠算法在进化后期容易陷入局部最优且种群多样性差。虽然目前已有大量工作针对蝙蝠算法的性能进行了改进,但难以同时满足收敛速度与寻优精度的要求。针对这些问题,提出了基于罗盘算子的改进蝙蝠算法,借鉴鸽群优化算法,引入了罗盘算子帮助蝙蝠种群快速找到质量高的个体,提高蝙蝠算法的开发和搜索能力。之后在MATLAB环境下,通过6种经典多维测试函数分别对该算法与遗传算法、标准蝙蝠算法进行仿真对比实验与双侧t检验。结果表明,改进算法的进化效率、优化深度和成功率均得到了较大程度的提升,对工程复杂函数有很大的价值。
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