biostudies-literatureUnknownWu SThe slime mould algorithm (SMA) is a new meta-heuristic algorithm recently proposed. The algorithm is inspired by the foraging behavior of polycephalus slime moulds. It simulates the behavior and morphological changes of slime moulds during foraging through adaptive weights. Although the original SMA's performance is better than most swarm intelligence algorithms, it still has shortcomings, such as quickly falling into local optimal values and insufficient exploitation. This paper proposes a Gaussian barebone mutation enhanced SMA (GBSMA) to alleviate the original SMA's shortcomings. First of all, the Gaussian function in the Gaussian barebone accelerates the convergence while also expanding the search space, which improves the algorithm exploration and exploitation capabilities. Secondly,Artificial intelligence review1-37https://www.ebi.ac.uk/biostudies/studies/S-EPMC9853503biostudies-literatureGaussian bare-bone slime mould algorithm: performance optimization and case studies on truss structures.PMC9853503Kuang FWu SChen HZhang SHeidari AAfalseGaussian bare-bone slime mould algorithm: performance optimization and case studies on truss structures.The slime mould algorithm (SMA) is a new meta-heuristic algorithm recently proposed. The algorithm is inspired by the foraging behavior of polycephalus slime moulds. It simulates the behavior and morphological changes of slime moulds during foraging through adaptive weights. Although the original SMA's performance is better than most swarm intelligence algorithms, it still has shortcomings, such as quickly falling into local optimal values and insufficient exploitation. This paper proposes a Gaussian barebone mutation enhanced SMA (GBSMA) to alleviate the original SMA's shortcomings. First of all, the Gaussian function in the Gaussian barebone accelerates the convergence while also expanding the search space, which improves the algorithm exploration and exploitation capabilities. Secondly,2023-01-01T00:00:00Z2023 Jan2025-04-26T07:51:33.476Z2025-04-06T12:25:52.374ZS-EPMC98535033669461510.1007/s10462-022-10370-7