Ensemble mutation slime mould algorithm with restart mechanism for feature selection

Jia, Heming; Zhang, Wanying; Zheng, Rong; Wang, Shuang; Leng, Xin; Cao, Ning

doi:10.1002/int.22776

Cited by 23 publications

(17 citation statements)

References 49 publications

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“…The dimensionality reduction approach, also known as the FS, needs data processing since it is necessary to increase classification accuracy using the technique. It is possible to make the most of the SMA's potential by combining two different strategies: the restart strategy (RS) and the composite mutation strategy (CMS) [ 76 ]. Its acronym, CMSRSSMA, also refers to the enhanced SMA.…”

Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

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Meta-heuristic algorithms have a high position among academic researchers in various fields, such as science and engineering, in solving optimization problems. These algorithms can provide the most optimal solutions for optimization problems. This paper investigates a new meta-heuristic algorithm called Slime Mould algorithm (SMA) from different optimization aspects. The SMA algorithm was invented due to the fluctuating behavior of slime mold in nature. It has several new features with a unique mathematical model that uses adaptive weights to simulate the biological wave. It provides an optimal pathway for connecting food with high exploration and exploitation ability. As of 2020, many types of research based on SMA have been published in various scientific databases, including IEEE, Elsevier, Springer, Wiley, Tandfonline, MDPI, etc. In this paper, based on SMA, four areas of hybridization, progress, changes, and optimization are covered. The rate of using SMA in the mentioned areas is 15, 36, 7, and 42%, respectively. According to the findings, it can be claimed that SMA has been repeatedly used in solving optimization problems. As a result, it is anticipated that this paper will be beneficial for engineers, professionals, and academic scientists.

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Section: Methods Of Smamentioning

confidence: 99%

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Gharehchopogh

Uçan

İbrikçi

et al. 2023

Arch Computat Methods Eng

View full text Add to dashboard Cite

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“…MOBIFS is an efective multiobjective BFO algorithm that handles FS issues using four information exchange mechanisms. Te slime mold algorithm (SMA) [49], binary manta ray foraging optimization (BMRFO) [50], and improved binary butterfy algorithm (IBFA) [51] are three other bioinspired algorithms that have good performance in FS. SMA imitates slime mold's foraging behavior and introduces the composite mutation strategy and restart strategy.…”

Section: Comparison Methodsmentioning

confidence: 99%

Semisupervised Bacterial Heuristic Feature Selection Algorithm for High-Dimensional Classification with Missing Labels

Wang

et al. 2023

International Journal of Intelligent Systems

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Feature selection is a crucial method for discovering relevant features in high-dimensional data. However, most studies primarily focus on completely labeled data, ignoring the frequent occurrence of missing labels in real-world problems. To address high-dimensional and label-missing problems in data classification simultaneously, we proposed a semisupervised bacterial heuristic feature selection algorithm. To track the label-missing problem, a k-nearest neighbor semisupervised learning strategy is designed to reconstruct missing labels. In addition, the bacterial heuristic algorithm is improved using hierarchical population initialization, dynamic learning, and elite population evolution strategies to enhance the search capacity for various feature combinations. To verify the effectiveness of the proposed algorithm, three groups of comparison experiments based on eight datasets are employed, including two traditional feature selection methods, four bacterial heuristic feature selection algorithms, and two swarm-based heuristic feature selection algorithms. Experimental results demonstrate that the proposed algorithm has obvious advantages in terms of classification accuracy and selected feature numbers.

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“…Abualigah et al [ 30 ] combine the arithmetic optimization algorithm (AOA) with SCA's operators for enhancing the local search ability of AOA and verify the effectiveness by some experiments. Jia et al [ 31 ] come up with an algorithm called CMSRSSMA with an improvement of SMA by embedding the composite mutation strategy (CMS) and restart strategy (RS). The CMS aims to enhance the population diversity, and the RS is utilized to avoid the local optima.…”

Section: Introductionmentioning

confidence: 99%

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

Song

Jia

Abualigah

et al. 2022

Computational Intelligence and Neuroscience

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One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.

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Ensemble mutation slime mould algorithm with restart mechanism for feature selection

Cited by 23 publications

References 49 publications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

Semisupervised Bacterial Heuristic Feature Selection Algorithm for High-Dimensional Classification with Missing Labels

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

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