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

Gharehchopogh, Farhad Soleimanian; Uçan, Alaettin; İbrikçi, Turgay; Arasteh, Bahman; Işık, Gültekin

doi:10.1007/s11831-023-09883-3

Cited by 71 publications

(12 citation statements)

References 167 publications

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“…S F Gharehchopogh et al proposed an emerging metaheuristic algorithm called "slime mold algorithm" (SMA) for research and analysis. According to the results statistics, by 2020, research based on SMA has been published in major scientific and technological databases, covering four fields: hybridization, development, deformation, and optimization of SMA algorithms, with the most widespread application in optimization problem solving [ 21 ]. M Ayar et al proposed a novel feature selection method based on chaos partitioning for rapid automatic identification of arrhythmias.…”

Section: Related Workmentioning

confidence: 99%

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing

Feng

2024

PLoS ONE

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High-dimensional data is widely used in many fields, but selecting key features from it is challenging. Feature selection can reduce data dimensionality and weaken noise interference, thereby improving model efficiency and enhancing model interpretability. In order to improve the efficiency and accuracy of high-dimensional data processing, a feature selection method based on optimized genetic algorithm is proposed in this study. The algorithm simulates the process of natural selection, searches for possible subsets of feature, and finds the subsets of feature that optimizes the performance of the model. The results show that when the value of K is less than 4 or more than 8, the recognition rate is very low. After adaptive bias filtering, 724 features are filtered to 372, and the accuracy is improved from 0.9352 to 0.9815. From 714 features to 406 Gaussian codes, the accuracy is improved from 0.9625 to 0.9754. Among all tests, the colon has the highest average accuracy, followed by small round blue cell tumor(SRBCT), lymphoma, central nervous system(CNS) and ovaries. The green curve is the best, with stable performance and a time range of 0–300. While maintaining the efficiency, it can reach 4.48 as soon as possible. The feature selection method has practical significance for high-dimensional data processing, improves the efficiency and accuracy of data processing, and provides an effective new method for high-dimensional data processing.

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Section: Related Workmentioning

confidence: 99%

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing

Feng

2024

PLoS ONE

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“…The strategy of the tunicates and their search mechanism in the process of finding food sources and foraging have been the main inspirations in the design of the Tunicate Swarm Algorithm (TSA) 17 . Some other swarm-based methods are White Shark Optimizer (WSO) 18 , Reptile Search Algorithm (RSA) 19 , Raccoon Optimization Algorithm (ROA) 20 , African Vultures Optimization Algorithm (AVOA) 21 , Farmland Fertility Algorithm (FFA) 22 , Slime Mould algorithm (SMA) 23 , Mountain Gazelle Optimizer (MGO) 24 , Sparrow Search Algorithm (SSA) 25 , Whale Optimization Algorithm (WOA) 26 , Artificial Gorilla Troops Optimizer (GTO) 27 , and Pelican Optimization Algorithm (POA) 28 .…”

Section: Literature Reviewmentioning

confidence: 99%

A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior

Trojovský

Dehghani

2023

Sci Rep

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This paper introduces a new bio-inspired metaheuristic algorithm called Walrus Optimization Algorithm (WaOA), which mimics walrus behaviors in nature. The fundamental inspirations employed in WaOA design are the process of feeding, migrating, escaping, and fighting predators. The WaOA implementation steps are mathematically modeled in three phases exploration, migration, and exploitation. Sixty-eight standard benchmark functions consisting of unimodal, high-dimensional multimodal, fixed-dimensional multimodal, CEC 2015 test suite, and CEC 2017 test suite are employed to evaluate WaOA performance in optimization applications. The optimization results of unimodal functions indicate the exploitation ability of WaOA, the optimization results of multimodal functions indicate the exploration ability of WaOA, and the optimization results of CEC 2015 and CEC 2017 test suites indicate the high ability of WaOA in balancing exploration and exploitation during the search process. The performance of WaOA is compared with the results of ten well-known metaheuristic algorithms. The results of the simulations demonstrate that WaOA, due to its excellent ability to balance exploration and exploitation, and its capacity to deliver superior results for most of the benchmark functions, has exhibited a remarkably competitive and superior performance in contrast to other comparable algorithms. In addition, the use of WaOA in addressing four design engineering issues and twenty-two real-world optimization problems from the CEC 2011 test suite demonstrates the apparent effectiveness of WaOA in real-world applications. The MATLAB codes of WaOA are available in https://uk.mathworks.com/matlabcentral/profile/authors/13903104.

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“…The algorithmic-level approach aims to enhance classification results and reduce data imbalance through optimization techniques. Several studies have investigated algorithm optimization, with notable examples including the improved harris hawk optimization and opposition-based learning (IHHOOBL) algorithm, which is specifically designed to detect communities in social networks [11]; Several algorithms have been developed to address optimization problems, including the quantum-learning, gaussian, cauchy, and tunicate swarm (QLGCTSA) algorithm, which is a generalpurpose algorithm [12]; the slime mould algorithm (SMA), designed to simulate biological wave optimization [13]; the sparrow search algorithm (SSA), developed specifically for optimization problems [14]; the tree seed algorithm (TSA), which identifies tree and seed relationships for optimization [15]; and QC-inspired metaheuristic algorithms, which aim to solve numerical optimization problems [16]. Although previous studies have shown that optimizing these algorithms, either through feature selection [17] or for improved accuracy, does not adequately address the challenge of imbalanced data arising from oversampling or undersampling.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

2023

IJIES

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This research presents a novel hybrid sampling technique, implemented at the data level, to effectively address imbalanced and noisy data in classification processes. The proposed technique expertly combines two established methods, namely, the random over sampling (ROS) and neighbourhood cleaning rule (NCL) approaches, to tackle imbalance and noise issues, respectively. The study carried out an empirical evaluation of the proposed approach using crowdsourced text data that primarily emphasized the triple bottom line (TBL) dimension of a smart social, economic, and environmental city. The study used the long short-term memory (LSTM), convolutional neural networks (CNN), and CNN-LSTM classification models to validate the efficacy of the proposed hybrid sampling technique and compare its performance with other existing approaches, including ROS oversampling, NCL undersampling, synthetic minority over sampling & tomek links (SMOTE-Tomek), and synthetic minority oversampling and edited nearest neighbours (SMOTE-ENN) hybrid sampling. The results are impressive, with the ROS-NCL hybrid sampling technique achieving high accuracy rates across all three classification models, at 97.71%, 98.01%, and 98.11%, respectively. This approach provides a robust and effective solution for handling impure data and holds great promise in identifying complex data patterns in real-world classification problems.

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

Cited by 71 publications

References 167 publications

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing

Feature selection algorithm based on optimized genetic algorithm and the application in high-dimensional data processing

A new bio-inspired metaheuristic algorithm for solving optimization problems based on walruses behavior

A Hybrid Sampling Approach for Improving the Classification of Imbalanced Data Using ROS and NCL Methods

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