基于欧氏距离的黑洞寻优算法

为了提高黑洞算法的寻优精度和算法的全局搜索能力,提出了一种基于欧氏距离的改进黑洞寻优算法.通过引入欧氏距离来初始化星体群位置,增强星体群的多样性,提高其全局搜索能力;设定黑洞半径最大值,避免由于黑洞面积过大跳过全局最优解,当有星体被黑洞吸收时,要求新的星体在距离黑洞一定欧氏距离以外的位置产生,提高星体的搜索区域;通过对3个基准测试函数进行寻优测试,并与PSO、ABC、DE、BH优化算法相比,验证了基于欧氏距离的黑洞寻优算法在寻优精度和全局寻优能力方面的优越性.结果表明,该算法不仅能够搜索到参数的全局最优解,而且与其他优化算法相比有一定优势.

Optimization algorithm of black hole based on Euclidean distance

To improve the optimization precision and global search capability of black hole algorithm, an improved optimization algorithm of black hole based on Euclidean distance was proposed. The Euclidean distance was applied to initialize the star swarm locations, increase the diversity of star swarm and improve the global searching capability. The maximum radius of black hole was set to avoid the escape of global optimization solutions due to too large black hole area. When a star was swallowed by the black hole, it was required that a new star generated at a position with a certain Euclidean distance from the black hole in order to enhance the star searching areas. Through the optimization tests for three benchmark functions and the comparison with PSO, ABC, DE, BH optimization algorithms, the superiority of optimization precision and global search capability for the optimization algorithm of black hole based on Euclidean distance was verified. The results show that the present algorithm can not only search the global optimal solution to the parameters, but also exhibit a certain advantage compared with other optimization algorithms. 