Dynamic evolutionary data and text document clustering approach using improved Aquila optimizer based arithmetic optimization algorithm and differential evolution

Abualigah, Laith; Almotairi, Khaled H.

doi:10.1007/s00521-022-07571-0

Cited by 10 publications

(4 citation statements)

References 54 publications

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“…The comparison was made between GA and PSO Gravitational search algorithm 30 2011 Inspired by the gravity law and mass interactions The comparison was made between PSO and GA only. Detailed analysis is not available DE 2 2013 Inspired by Darwin's theory of evolution The comparison is carried out between different variants of DE PSO hybridized with magnetic charge system search 27 2015 Hybrid PSO with magnetic charge system searches and is inspired by electromagnetic theory Validation is carried out for very few benchmark datasets Glowworm optimization algorithm 25 2017 The swarm's movement of glowworms is determined by their distance from one another and by a luminous quantity Detailed analysis is not carried out for the clustering problems Symbiotic organism search algorithm 85 2019 Inspired by the symbiotic interaction implemented to survive and propagate Ten datasets are used to validate the algorithm and compared with PSO and GA GWO 39 2020 Inspired by the social hierarchy and hunting behaviour of the grey wolves Limited datasets are used for validation MFO 86 2021 Inspired by the moth's intelligence, i.e., transverse orientation to navigate in nature Original MFO is applied, and it gets trapped by local optima Aquila optimizer 87 2022 Hybridized with the arithmetic optimization algorithm. The Aquila optimizer has inspired the behaviours during the finding of the prey It has been applied for text and data clustering problems.…”

Section: Introductionmentioning

confidence: 99%

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Premkumar,

Sinha,

Ramasamy

et al. 2024

Sci Rep

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This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in order to address the problem of data clustering. The process that groups similar items within a dataset into non-overlapping groups. Grey wolf hunting behaviour served as the model for grey wolf optimizer, however, it frequently lacks the exploration and exploitation capabilities that are essential for efficient data clustering. This work mainly focuses on enhancing the grey wolf optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and avoid premature convergence. Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated on ten numerical functions and multiple real-world datasets with varying levels of complexity and dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial solutions and adding a weight factor to increase the diversity of solutions during the optimization phase. The results show that the K-means clustering-based grey wolf optimizer performs much better than the standard grey wolf optimizer in discovering optimal clustering solutions, indicating a higher capacity for effective exploration and exploitation of the solution space. The study found that the K-means clustering-based grey wolf optimizer was able to produce high-quality cluster centres in fewer iterations, demonstrating its efficacy and efficiency on various datasets. Finally, the study demonstrates the robustness and dependability of the K-means clustering-based grey wolf optimizer in resolving data clustering issues, which represents a significant advancement over conventional techniques. In addition to addressing the shortcomings of the initial algorithm, the incorporation of K-means and the innovative weight factor into the grey wolf optimizer establishes a new standard for further study in metaheuristic clustering algorithms. The performance of the K-means clustering-based grey wolf optimizer is around 34% better than the original grey wolf optimizer algorithm for both numerical test problems and data clustering problems.

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Section: Introductionmentioning

confidence: 99%

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Premkumar,

Sinha,

Ramasamy

et al. 2024

Sci Rep

Self Cite

View full text Add to dashboard Cite

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“…The number of individuals in the population was set as 50, the number of iterations was 50, the mutation probability was 0.01, the hybridization probability was 0.8, and the encoding method was binary. In order to reduce the influence of human factors, according to the specific conditions of the experimental samples, the parameter ranges of the LSTM are set as follows: the value range of the time-window step is [1,60], the batch size is [10,500], the number of hidden layers is [10,200], and the rejection rate is [0.05, 0.35].…”

Section: Parameter Selectionmentioning

confidence: 99%

“…Recently, the developmental environment of the chemical industry has been complex and changeable, the market scale is expanding, and the competition among the main bodies is increasingly fierce [1][2][3]. It is the key to winning sales initiatives, grasping the coordination of quantity and price, and realizing the optimal economic benefit to accurately understand and properly evaluate the price trends of domestic chemical products.…”

Section: Introductionmentioning

confidence: 99%

Prediction Model for the Chemical Futures Price Using Improved Genetic Algorithm Based Long Short-Term Memory

Teng²,

Zhang

et al. 2023

Processes

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In this paper, a new prediction model for accurately recognizing and appropriately evaluating the trends of domestic chemical products and for improving the forecasting accuracy of the chemical products’ prices is proposed. The proposed model uses the minimum forecasting error as the evaluation objective to forecast the settlement price. Active contracts for polyethylene and polypropylene futures on the Dalian Commodity Futures Exchange for the next five days were used, the data were divided into a training set and test set through normalization, and the time window, batch processing size, number of hidden layers, and rejection rate of a long short-term memory (LSTM) network were optimized by an improved genetic algorithm (IGA). In the experiments, with respect to the shortcomings of the genetic algorithm, the crossover location determination and some gene exchange methods in the crossover strategy were improved, and the predicted results of the IGA–LSTM model were compared with those of other models. The results showed that the IGA–LSTM model could effectively capture the characteristics and trends of time-series changes. The results showed that the proposed model obtained the minimum values (MSE = 0.00107, RMSE = 0.03268, and MAPE = 0.0691) in the forecasting of futures prices for two types of chemical products, showing excellent forecasting performance.

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“…Besides the DE algorithm, researchers used other swarm algorithms to solve DOPs effectively [20,25]. Following the stance of incorporating EAs to solve DOPs, many attempts have been initiated to solve DOPs in real-time [26][27][28] using DE as a base algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

Raghul,

Jeyakumar

2023

IEEE Access

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In conventional optimization problems, it is assumed that all relevant parametric constraints remain stationary. In contrast, optimization problems encountered in practical applications are dynamic and supervened by uncertainties. The research community has evinced a keen interest in multi-population approaches combined with nature-inspired algorithms to manage dynamic optimization problems efficiently. Applying multi-population approaches to solve dynamic optimization problems engenders specific vital issues, such as reproducing sub-populations in new environments influenced by archival information. Moreover, overpartitioning the population may lead to aberrant utilization of computational resources among the subpopulations. These impediments are addressed using the proposed hybrid multi-population reinitialization strategy, which is a combination of distributed differential evolution algorithmic framework and reinitialization strategy. This scheme relies on simple reinitialization to surmount the dynamism. This framework was assessed on different instances in a moving peak benchmark problem, a proven benchmarking function in the domain of dynamic optimization. Furthermore, this study encompasses a comparative and statistical analysis to validate the effectiveness of the proposed approach in comparison to cutting-edge algorithms in solving dynamic optimization problems efficiently. The experimental results consistently show that the hybrid multi-population reinitialization strategy outperforms conventional Differential Evolution algorithms across various parameter configurations. This hybrid multi-population reinitialization strategy showcases its effectiveness in the successful handling of increased shift lengths and number of peaks, which are pivotal parameters in solving moving peak benchmark function.

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Dynamic evolutionary data and text document clustering approach using improved Aquila optimizer based arithmetic optimization algorithm and differential evolution

Cited by 10 publications

References 54 publications

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Augmented weighted K-means grey wolf optimizer: An enhanced metaheuristic algorithm for data clustering problems

Prediction Model for the Chemical Futures Price Using Improved Genetic Algorithm Based Long Short-Term Memory

A Hybrid Multi-Population Reinitialization Strategy to Tackle Dynamic Optimization Problems

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