Journal of Bionic Engineering (2024) 21:1055–1091 https://doi.org/10.1007/s42235-023-00476-1 Gaussian Backbone?Based Spherical Evolutionary Algorithm with Cross?search for Engineering Problems

Yupeng Li1  · Dong Zhao1  · Ali Asghar Heidari2  · Shuihua Wang3,4 · Huiling Chen5  · Yudong Zhang3,6 

1 College of Computer Science and Technology, Changchun Normal University, Changchun 130032, Jilin, China 2 School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran 3 School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK 4 Department of Biological Sciences, Xi’an Jiaotong-Liverpool University, Suzhou 215123, Jiangsu, China 5 Key Laboratory of Intelligent Informatics for Safety and Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, China 6 School of Computer Science and Engineering, Southeast University, Nanjing 210096, China

AbstractIn 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 specifc 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 efective information; the GBS cooperates with the original rules of SE to further improve the convergence efect 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 fnal experimental results fully showcase that, compared with the existing well-known methods, CGSE has a very signifcant competitive advantage in global tasks and has certain practical value in real applications. Therefore, the proposed CGSE is a promising and frst-rate algorithm with good potential strength in the feld of engineering design.

Keywords Meta-heuristic algorithms · Engineering optimization · Spherical evolution algorithm · Global optimization