School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, China.
Anhui Science and Technology University, Chuzhou, China.
PLoS One. 2021 Dec 6;16(12):e0260512. doi: 10.1371/journal.pone.0260512. eCollection 2021.
This research proposes a new multi-membrane search algorithm (MSA) based on cell biological behavior. Cell secretion protein behavior and cell division and fusion strategy are the main inspirations for the algorithm. In order to verify the performance of the algorithm, we used 19 benchmark functions to compare the MSA test results with MVO, GWO, MFO and ALO. The number of iterations of each algorithm on each benchmark function is 100, the population number is 10, and the running is repeated 50 times, and the average and standard deviation of the results are recorded. Tests show that the MSA is competitive in unimodal benchmark functions and multi-modal benchmark functions, and the results in composite benchmark functions are all superior to MVO, MFO, ALO, and GWO algorithms. This paper also uses MSA to solve two classic engineering problems: welded beam design and pressure vessel design. The result of welded beam design is 1.7252, and the result of pressure vessel design is 5887.7052, which is better than other comparison algorithms. Statistical experiments show that MSA is a high-performance algorithm that is competitive in unimodal and multimodal functions, and its performance in compound functions is significantly better than MVO, MFO, ALO, and GWO algorithms.
本研究提出了一种基于细胞生物学行为的新型多膜搜索算法（MSA）。算法的主要灵感来源于细胞分泌蛋白行为和细胞分裂融合策略。为了验证算法的性能，我们使用了 19 个基准函数，将 MSA 的测试结果与 MVO、GWO、MFO 和 ALO 进行了比较。每个基准函数中每种算法的迭代次数为 100，种群数量为 10，重复运行 50 次，并记录结果的平均值和标准差。测试表明，MSA 在单峰基准函数和多峰基准函数中具有竞争力，在组合基准函数中的结果均优于 MVO、MFO、ALO 和 GWO 算法。本文还使用 MSA 解决了两个经典的工程问题：焊接梁设计和压力容器设计。焊接梁设计的结果为 1.7252，压力容器设计的结果为 5887.7052，优于其他比较算法。统计实验表明，MSA 是一种高性能算法，在单峰和多峰函数中具有竞争力，在复合函数中的性能明显优于 MVO、MFO、ALO 和 GWO 算法。
