Boosting Slime Mould Algorithm for High-Dimensional Gene Data Mining: Diversity Analysis and Feature Selection

Abstract

Slime mould algorithm (SMA) is a new metaheuristic algorithm, which simulates the behavior and morphology changes of slime mould during foraging. The slime mould algorithm has good performance; however, the basic version of SMA still has some problems. When faced with some complex problems, it may fall into local optimum and cannot find the optimal solution. Aiming at this problem, an improved SMA is proposed to alleviate the disadvantages of SMA. Based on the original SMA, Gaussian mutation and Levy flight are introduced to improve the global search performance of the SMA. Adding Gaussian mutation to SMA can improve the diversity of the population, and Levy flight can alleviate the local optimum of SMA, so that the algorithm can find the optimal solution as soon as possible. In order to verify the effectiveness of the proposed algorithm, a continuous version of the proposed algorithm, GLSMA, is tested on 33 classical continuous optimization problems. Then, on 14 high-dimensional gene datasets, the effectiveness of the proposed discrete version, namely, BGLSMA, is verified by comparing with other feature selection algorithm. Experimental results reveal that the performance of the continuous version of the algorithm is better than the original algorithm, and the defects of the original algorithm are alleviated. Besides, the discrete version of the algorithm has a higher classification accuracy when fewer features are selected. This proves that the improved algorithm has practical value in high-dimensional gene feature selection.

Advances in Slime Mould Algorithm: A Comprehensive Survey

“…Yang P et al [62] proposed a load identification method based on the improved slime mould algorithmgeneralized regression neural network (ISMA-GRNN) and the ISMA-GRNN achieved higher accuracy and precision values for load identifications obtained from the simulation results. Qiu F et al [69] developed a BGLSMA for the feature selection from 14 highdimensional gene datasets and the experiments verified that the discrete BGLSMA was a promising approach for features selection purposes. Zhou X et al [81] proposed a binary SMA (bLASMA) and evaluated it using 18 datasets of varying dimensions obtained from the UCI machine learning repository.…”

“…Qi A et al [63] presented an SDSMA combining adaptive Lévy diversity and directional crossover mechanisms. Qiu F et al [69] used Lévy flight to help the SMA jump out the local optimum position. Jui JJ et al [70] integrated the Lévy distribution into an SMA for solving the local optima problem.…”

“…Feng Qiu et al [56] converted an SMA into a binary version with a transfer function and used it for gene selection purposes. Feng Qiu et al [69] also mapped the proposed GLSMA to a binary space via the transformation function and applied for the feature selection. Yuanfei Wei et al [80] made the proposed EOSMA discrete with a sort-order-index (SOI)-based coding for a job shop scheduling problem (JSSP).…”