This paper proposes a new hybrid optimization algorithm, called “(HSSOGSA)” with the combination of “gravitational search algorithm (GSA)” and “sperm swarm optimization (SSO)”. The underlying concepts and ideas behind the proposed algorithm are to combine the capability of exploitation in SSO with the capability of exploration in GSA to synthesize both algorithms’ strength. To evaluate the efficiency of the proposed approach, different test bed problems of optimization are considered, called the “congress on evolutionary computation (CEC)” 2017 suite. The proposed HSSOGSA is compared against both the standard GSA and SSO algorithms. These algorithms are compared based on two mechanisms, including, qualitative and quantitative tests. For the quantitative test, we adopt best fitness, standard deviation, and average measures, while for the qualitative test, we compare between the convergence rates achieved by the proposed algorithm and the convergence rates achieved by SSO and GSA. The outcomes of the study present the hybrid method possesses a better capability and performance to escape from local extremes with faster rate of convergence than the standard SSO and GSA for the majority of benchmarks functions of wide and narrow search space domain.

本文提出了一种新的混合优化算法，称为“（HSSOGSA）”，它结合了“引力搜索算法（GSA）”和“精子群优化（SSO）”。该算法背后的基本概念和思想是将SSO中的开发能力与GSA中的探索能力相结合，以综合两种算法的强度。为了评估所提出方法的效率，考虑了优化的不同测试平台问题，称为“进化计算大会（CEC）” 2017套件。将拟议的HSSOGSA与标准GSA和SSO算法进行了比较。这些算法是基于包括定性和定量测试在内的两种机制进行比较的。对于定量测试，我们采用最佳适用性，标准差和平均测度，对于定性测试，我们比较了所提算法的收敛速度与SSO和GSA的收敛速度。研究结果表明，对于宽和窄搜索空间域的大多数基准功能，混合方法具有比标准SSO和GSA更好的能力和性能，能够以更快的收敛速度摆脱局限性。