Gaussian bare-bone slime mould algorithm: performance optimization and case studies on truss structures

Abstract

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, the differential evolution (DE) update strategy in the Gaussian barebone, using rand as the guiding vector. It also enhances the algorithm's global search performance to a certain extent. Also, the greedy selection is introduced on this basis, which prevents individuals from performing invalid position updates. In the IEEE CEC2017 test function, the proposed GBSMA is compared with a variety of meta-heuristic algorithms to verify the performance of GBSMA. Besides, GBSMA is applied to solve truss structure optimization problems. Experimental results show that the convergence speed and solution accuracy of the proposed GBSMA are significantly better than the original SMA and other similar products.

Advances in Slime Mould Algorithm: A Comprehensive Survey

“…The greedy selection strategy was employed for building the information exchange model to provide a better solution for the subsequent iteration. Shubiao Wu et al [75] adopted a greedy selection strategy to increase the convergence rate of the proposed GBSMA. Yin S et al [76] updated the individual and global historical optimal values with the greedy strategy, resulting in the acceleration of the convergence.…”

“…In addition to the strategies previously mentioned in this study, there are numerous additional strategies that researchers usually add to SMAs, including the Nelder-Mead simplex search [33], bee-foraging learning operator [39], dispersed foraging strategy [40],orthogonal learning [82], dynamic random search [55], sigmoid function [67,83], and Gaussian strategy [75,84]. These strategies have improved the performance of SMAs and are important directions for researchers.…”

Slime Mould Algorithm Based on a Gaussian Mutation for Solving Constrained Optimization Problems

“…To balance exploration and exploitation, Luo et al [21] developed an enhanced multi-strategy grasshopper optimization algorithm (GOA) with GM, levy flight, and opposition-based learning strategies. An enhanced Gaussian barebone was proposed by S. Wu et al [22] as the DE mutation extension to improve the update approach of SMA.…”

Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design

“… 56 effectively minimized operating costs in microgrids by utilizing the SMA algorithm, while Wu et al. 57 achieved significant improvements in convergence speed and solution accuracy for optimizing truss structures through an improved version of the SMA algorithm compared to similar products.…”

“…Wu et al. 57 proposed a Gaussian barebone mutation enhanced SMA, in which the incorporation of a Gaussian function not only accelerated the convergence speed but also expanded the search space. Additionally, the introduction of the DE update strategy enhances global search performance to some extent.…”