In recent years, with the increasing demand for social production, engineering design problems have gradually become more and more complex. Many novel and well-performing meta-heuristic algorithms have been studied and developed to cope with this problem. Among them, the Spherical Evolutionary Algorithm (SE) is one of the classical representative methods that proposed in recent years with admirable optimization performance. However, it tends to stagnate prematurely to local optima in solving some specific problems. Therefore, this paper proposes an SE variant integrating the Cross-search Mutation (CSM) and Gaussian Backbone Strategy (GBS), called CGSE. In this study, the CSM can enhance its social learning ability, which strengthens the utilization rate of SE on effective information; the GBS cooperates with the original rules of SE to further improve the convergence effect of SE. To objectively demonstrate the core advantages of CGSE, this paper designs a series of global optimization experiments based on IEEE CEC2017, and CGSE is used to solve six engineering design problems with constraints. The final experimental results fully showcase that, compared with the existing well-known methods, CGSE has a very significant competitive advantage in global tasks and has certain practical value in real applications. Therefore, the proposed CGSE is a promising and first-rate algorithm with good potential strength in the field of engineering design.

近年来，随着社会生产需求的不断增加，工程设计问题逐渐变得越来越复杂。人们已经研究和开发了许多新颖且性能良好的元启发式算法来解决这个问题。其中，球形进化算法（SE）是近年来提出的经典代表方法之一，具有令人赞叹的优化性能。然而，在解决一些具体问题时，它往往会过早地停滞于局部最优。因此，本文提出了一种集成交叉搜索变异（CSM）和高斯骨干策略（GBS）的SE变体，称为CGSE。本研究中，CSM可以增强其社会学习能力，从而加强SE对有效信息的利用率；GBS与SE原有规则配合，进一步提高SE的收敛效果。为了客观地展示CGSE的核心优势，本文基于IEEE CEC2017设计了一系列全局优化实验，并利用CGSE解决了6个带约束的工程设计问题。最终的实验结果充分表明，与现有的知名方法相比，CGSE在全局任务中具有非常显着的竞争优势，在实际应用中具有一定的实用价值。因此，所提出的CGSE是一种有前途、一流的算法，在工程设计领域具有良好的潜力。