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Sharma R, Mahanti GK, Panda G, Rath A, Dash S, Mallik S, Zhao Z. Comparative performance analysis of binary variants of FOX optimization algorithm with half-quadratic ensemble ranking method for thyroid cancer detection. Sci Rep 2023; 13:19598. [PMID: 37950041 PMCID: PMC10638362 DOI: 10.1038/s41598-023-46865-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/06/2023] [Indexed: 11/12/2023] Open
Abstract
Thyroid cancer is a life-threatening condition that arises from the cells of the thyroid gland located in the neck's frontal region just below the adam's apple. While it is not as prevalent as other types of cancer, it ranks prominently among the commonly observed cancers affecting the endocrine system. Machine learning has emerged as a valuable medical diagnostics tool specifically for detecting thyroid abnormalities. Feature selection is of vital importance in the field of machine learning as it serves to decrease the data dimensionality and concentrate on the most pertinent features. This process improves model performance, reduces training time, and enhances interpretability. This study examined binary variants of FOX-optimization algorithms for feature selection. The study employed eight transfer functions (S and V shape) to convert the FOX-optimization algorithms into their binary versions. The vision transformer-based pre-trained models (DeiT and Swin Transformer) are used for feature extraction. The extracted features are transformed using locally linear embedding, and binary FOX-optimization algorithms are applied for feature selection in conjunction with the Naïve Bayes classifier. The study utilized two datasets (ultrasound and histopathological) related to thyroid cancer images. The benchmarking is performed using the half-quadratic theory-based ensemble ranking technique. Two TOPSIS-based methods (H-TOPSIS and A-TOPSIS) are employed for initial model ranking, followed by an ensemble technique for final ranking. The problem is treated as multi-objective optimization task with accuracy, F2-score, AUC-ROC and feature space size as optimization goals. The binary FOX-optimization algorithm based on the [Formula: see text] transfer function achieved superior performance compared to other variants using both datasets as well as feature extraction techniques. The proposed framework comprised a Swin transformer to extract features, a Fox optimization algorithm with a V1 transfer function for feature selection, and a Naïve Bayes classifier and obtained the best performance for both datasets. The best model achieved an accuracy of 94.75%, an AUC-ROC value of 0.9848, an F2-Score of 0.9365, an inference time of 0.0353 seconds, and selected 5 features for the ultrasound dataset. For the histopathological dataset, the diagnosis model achieved an overall accuracy of 89.71%, an AUC-ROC score of 0.9329, an F2-Score of 0.8760, an inference time of 0.05141 seconds, and selected 12 features. The proposed model achieved results comparable to existing research with small features space.
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Affiliation(s)
- Rohit Sharma
- Department of Electronics and Communication Engineering, NIT, Durgapur, 713209, India
| | - Gautam Kumar Mahanti
- Department of Electronics and Communication Engineering, NIT, Durgapur, 713209, India
| | - Ganapati Panda
- Department of Electronics and Communication Engineering, C.V. Raman Global University, Bhubaneswar, 752054, India
| | - Adyasha Rath
- Department of Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, 752054, India
| | - Sujata Dash
- Department of Information Technology, Nagaland University, Dimapur, India
| | - Saurav Mallik
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.
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Alharthi AM, Kadir DH, Al-Fakih AM, Algamal ZY, Al-Thanoon NA, Qasim MK. Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:831-846. [PMID: 37885432 DOI: 10.1080/1062936x.2023.2261855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 09/17/2023] [Indexed: 10/28/2023]
Abstract
The horse herd optimization algorithm (HOA), one of the more contemporary metaheuristic algorithms, has demonstrated superior performance in a number of challenging optimization tasks. In the present work, the descriptor selection issue is resolved by classifying different essential oil retention indices using the binary form, BHOA. Based on internal and external prediction criteria, Z-shape transfer functions (ZTF) were tested to verify their efficiency in improving BHOA performance in QSPR modelling for predicting retention indices of essential oils. The evaluation criteria involved the mean-squared error of the training and testing datasets (MSE), and leave-one-out internal and external validation (Q2). The degree of convergence of the proposed Z-shaped transfer functions was compared. In addition, K-fold cross validation with k = 5 was applied. The results show that ZTF, especially ZTF1, greatly improves the performance of the original BHOA. Comparatively speaking, ZTF, especially ZTF1, exhibits the fastest convergence behaviour of the binary algorithms. It chooses the fewest descriptors and requires the fewest iterations to achieve excellent prediction performance.
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Affiliation(s)
- A M Alharthi
- Department of Mathematics, Turabah University College, Taif University, Taif, Saudi Arabia
| | - D H Kadir
- Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Erbil, F.R. Iraq
- Department of Business Administration, Cihan University-Erbil, Erbil, Iraq
| | - A M Al-Fakih
- Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia
- Department of Chemistry, Faculty of Science, Sana'a University, Sana'a, Yemen
| | - Z Y Algamal
- Department of Statistics and Informatics, University of Mosul, Mosul, Iraq
| | - N A Al-Thanoon
- Department of Operations Research and Intelligent Techniques, University of Mosul, Mosul, Iraq
| | - M K Qasim
- Department of General Science, University of Mosul, Mosul, Iraq
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Lemus-Romani J, Crawford B, Cisternas-Caneo F, Soto R, Becerra-Rozas M. Binarization of Metaheuristics: Is the Transfer Function Really Important? Biomimetics (Basel) 2023; 8:400. [PMID: 37754151 PMCID: PMC10526273 DOI: 10.3390/biomimetics8050400] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 08/24/2023] [Accepted: 08/25/2023] [Indexed: 09/28/2023] Open
Abstract
In this work, an approach is proposed to solve binary combinatorial problems using continuous metaheuristics. It focuses on the importance of binarization in the optimization process, as it can have a significant impact on the performance of the algorithm. Different binarization schemes are presented and a set of actions, which combine different transfer functions and binarization rules, under a selector based on reinforcement learning is proposed. The experimental results show that the binarization rules have a greater impact than transfer functions on the performance of the algorithms and that some sets of actions are statistically better than others. In particular, it was found that sets that incorporate the elite or elite roulette binarization rule are the best. Furthermore, exploration and exploitation were analyzed through percentage graphs and a statistical test was performed to determine the best set of actions. Overall, this work provides a practical approach for the selection of binarization schemes in binary combinatorial problems and offers guidance for future research in this field.
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Affiliation(s)
- José Lemus-Romani
- Escuela de Construcción Civil, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna 4860, Macul, Santiago 7820436, Chile
| | - Broderick Crawford
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (F.C.-C.); (R.S.); (M.B.-R.)
| | - Felipe Cisternas-Caneo
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (F.C.-C.); (R.S.); (M.B.-R.)
| | - Ricardo Soto
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (F.C.-C.); (R.S.); (M.B.-R.)
| | - Marcelo Becerra-Rozas
- Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Avenida Brasil 2241, Valparaíso 2362807, Chile; (F.C.-C.); (R.S.); (M.B.-R.)
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Zhu J, Liu J, Chen Y, Xue X, Sun S. Binary Restructuring Particle Swarm Optimization and Its Application. Biomimetics (Basel) 2023; 8:266. [PMID: 37366861 DOI: 10.3390/biomimetics8020266] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 06/28/2023] Open
Abstract
Restructuring Particle Swarm Optimization (RPSO) algorithm has been developed as an intelligent approach based on the linear system theory of particle swarm optimization (PSO). It streamlines the flow of the PSO algorithm, specifically targeting continuous optimization problems. In order to adapt RPSO for solving discrete optimization problems, this paper proposes the binary Restructuring Particle Swarm Optimization (BRPSO) algorithm. Unlike other binary metaheuristic algorithms, BRPSO does not utilize the transfer function. The particle updating process in BRPSO relies solely on comparison results between values derived from the position updating formula and a random number. Additionally, a novel perturbation term is incorporated into the position updating formula of BRPSO. Notably, BRPSO requires fewer parameters and exhibits high exploration capability during the early stages. To evaluate the efficacy of BRPSO, comprehensive experiments are conducted by comparing it against four peer algorithms in the context of feature selection problems. The experimental results highlight the competitive nature of BRPSO in terms of both classification accuracy and the number of selected features.
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Affiliation(s)
- Jian Zhu
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China
| | - Jianhua Liu
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China
| | - Yuxiang Chen
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China
| | - Xingsi Xue
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China
| | - Shuihua Sun
- School of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
- Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fuzhou 350118, China
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Al-Fakih AM, Qasim MK, Algamal ZY, Alharthi AM, Zainal-Abidin MH. QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2023; 34:285-298. [PMID: 37157994 DOI: 10.1080/1062936x.2023.2208374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
One of the recently developed metaheuristic algorithms, the coyote optimization algorithm (COA), has shown to perform better in a number of difficult optimization tasks. The binary form, BCOA, is used in this study as a solution to the descriptor selection issue in classifying diverse antifungal series. Z-shape transfer functions (ZTF) are evaluated to verify their efficiency in improving BCOA performance in QSAR classification based on classification accuracy (CA), the geometric mean of sensitivity and specificity (G-mean), and the area under the curve (AUC). The Kruskal-Wallis test is also applied to show the statistical differences between the functions. The efficacy of the best suggested transfer function, ZTF4, is further assessed by comparing it to the most recent binary algorithms. The results prove that ZTF, especially ZTF4, significantly improves the performance of the original BCOA. The ZTF4 function yields the best CA and G-mean of 99.03% and 0.992%, respectively. It shows the fastest convergence behaviour compared to other binary algorithms. It takes the fewest iterations to reach high classification performance and selects the fewest descriptors. In conclusion, the obtained results indicate the ability of the ZTF4-based BCOA to find the smallest subset of descriptors while maintaining the best classification accuracy performance.
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Affiliation(s)
- A M Al-Fakih
- Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia
- Department of Chemistry, Faculty of Science, Sana'a University, Sana'a, Yemen
| | - M K Qasim
- Department of General Science, University of Mosul, Mosul, Iraq
| | - Z Y Algamal
- Department of Statistics and Informatics, University of Mosul, Mosul, Iraq
- College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq
| | - A M Alharthi
- Department of Mathematics, Turabah University College, Taif University, Taif, Saudi Arabia
| | - M H Zainal-Abidin
- Department of Chemistry, Faculty of Science, Universiti Teknologi Malaysia, Johor, Malaysia
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6
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Mohd Yusof N, Muda AK, Pratama SF, Abraham A. A novel nonlinear time-varying sigmoid transfer function in binary whale optimization algorithm for descriptors selection in drug classification. Mol Divers 2023; 27:71-80. [PMID: 35254585 DOI: 10.1007/s11030-022-10410-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 02/15/2022] [Indexed: 02/08/2023]
Abstract
In computational chemistry, the high-dimensional molecular descriptors contribute to the curse of dimensionality issue. Binary whale optimization algorithm (BWOA) is a recently proposed metaheuristic optimization algorithm that has been efficiently applied in feature selection. The main contribution of this paper is a new version of the nonlinear time-varying Sigmoid transfer function to improve the exploitation and exploration activities in the standard whale optimization algorithm (WOA). A new BWOA algorithm, namely BWOA-3, is introduced to solve the descriptors selection problem, which becomes the second contribution. To validate BWOA-3 performance, a high-dimensional drug dataset is employed. The proficiency of the proposed BWOA-3 and the comparative optimization algorithms are measured based on convergence speed, the length of the selected feature subset, and classification performance (accuracy, specificity, sensitivity, and f-measure). In addition, statistical significance tests are also conducted using the Friedman test and Wilcoxon signed-rank test. The comparative optimization algorithms include two BWOA variants, binary bat algorithm (BBA), binary gray wolf algorithm (BGWOA), and binary manta-ray foraging algorithm (BMRFO). As the final contribution, from all experiments, this study has successfully revealed the superiority of BWOA-3 in solving the descriptors selection problem and improving the Amphetamine-type Stimulants (ATS) drug classification performance.
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Affiliation(s)
- Norfadzlia Mohd Yusof
- Fakulti Teknologi Kejuruteraan Elektrik dan Elektronik, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia.
| | - Azah Kamilah Muda
- Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
| | - Satrya Fajri Pratama
- Fakulti Teknologi Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100, Durian Tunggal, Melaka, Malaysia
| | - Ajith Abraham
- Machine Intelligence Research Labs (MIR Labs) Scientific Network for Innovation and Research Excellence, Auburn, WA, USA
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7
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An improved binary particle swarm optimization combing V-shaped and U-shaped transfer function. EVOLUTIONARY INTELLIGENCE 2023. [DOI: 10.1007/s12065-023-00819-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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8
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Beheshti Z. BMPA-TVSinV: A Binary Marine Predators Algorithm using time-varying sine and V-shaped transfer functions for wrapper-based feature selection. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Wang GL, Chu SC, Tian AQ, Liu T, Pan JS. Improved Binary Grasshopper Optimization Algorithm for Feature Selection Problem. ENTROPY 2022; 24:e24060777. [PMID: 35741497 PMCID: PMC9223162 DOI: 10.3390/e24060777] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/24/2022] [Accepted: 05/29/2022] [Indexed: 11/16/2022]
Abstract
The migration and predation of grasshoppers inspire the grasshopper optimization algorithm (GOA). It can be applied to practical problems. The binary grasshopper optimization algorithm (BGOA) is used for binary problems. To improve the algorithm’s exploration capability and the solution’s quality, this paper modifies the step size in BGOA. The step size is expanded and three new transfer functions are proposed based on the improvement. To demonstrate the availability of the algorithm, a comparative experiment with BGOA, particle swarm optimization (PSO), and binary gray wolf optimizer (BGWO) is conducted. The improved algorithm is tested on 23 benchmark test functions. Wilcoxon rank-sum and Friedman tests are used to verify the algorithm’s validity. The results indicate that the optimized algorithm is significantly more excellent than others in most functions. In the aspect of the application, this paper selects 23 datasets of UCI for feature selection implementation. The improved algorithm yields higher accuracy and fewer features.
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Affiliation(s)
- Gui-Ling Wang
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (G.-L.W.); (S.-C.C.); (A.-Q.T.); (T.L.)
| | - Shu-Chuan Chu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (G.-L.W.); (S.-C.C.); (A.-Q.T.); (T.L.)
- College of Science and Engineering, Flinders University, Adelaide 5042, Australia
| | - Ai-Qing Tian
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (G.-L.W.); (S.-C.C.); (A.-Q.T.); (T.L.)
| | - Tao Liu
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (G.-L.W.); (S.-C.C.); (A.-Q.T.); (T.L.)
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; (G.-L.W.); (S.-C.C.); (A.-Q.T.); (T.L.)
- Department of Information Management, Chaoyang University of Technology, Taichung 413, China
- Correspondence:
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Zhang P, Du J, Wang L, Fei M, Yang T, Pardalos PM. A human learning optimization algorithm with reasoning learning. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108816] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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11
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Development of a Hybrid Artificial Neural Network-Particle Swarm Optimization Model for the Modelling of Traffic Flow of Vehicles at Signalized Road Intersections. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11188387] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The tremendous increase in vehicular navigation often witnessed daily has elicited constant and continuous traffic congestion at signalized road intersections. This study focuses on applying an artificial neural network trained by particle swarm optimization (ANN-PSO) to unravel the problem of traffic congestion. Traffic flow variables, such as the speed of vehicles on the road, number of different categories of vehicles, traffic density, time, and traffic volumes, were considered input and output variables for modelling traffic flow of non-autonomous vehicles at a signalized road intersection. Four hundred and thirty-four (434) traffic datasets, divided into thirteen (13) inputs and one (1) output, were obtained from seven roadsites connecting to the N1 Allandale interchange identified as the busiest road in Southern Africa. The results obtained from this research have shown a training and testing performance of 0.98356 and 0.98220. These results are indications of a significant positive correlation between the inputs and output variables. Optimal performance of the ANN-PSO model was achieved by tuning the number of neurons, accelerating factors, and swarm population sizes concurrently. The evidence from this research study suggests that the ANN-PSO model is an appropriate predictive model for the swift optimization of vehicular traffic flow at signalized road intersections. This research extends our knowledge of traffic flow modelling at a signalized road intersection using metaheuristics algorithms. The ANN-PSO model developed in this research will assist traffic engineers in designing traffic lights and creation of traffic rules at signalized road intersections.
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BHHO-TVS: A Binary Harris Hawks Optimizer with Time-Varying Scheme for Solving Data Classification Problems. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11146516] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Data classification is a challenging problem. Data classification is very sensitive to the noise and high dimensionality of the data. Being able to reduce the model complexity can help to improve the accuracy of the classification model performance. Therefore, in this research, we propose a novel feature selection technique based on Binary Harris Hawks Optimizer with Time-Varying Scheme (BHHO-TVS). The proposed BHHO-TVS adopts a time-varying transfer function that is applied to leverage the influence of the location vector to balance the exploration and exploitation power of the HHO. Eighteen well-known datasets provided by the UCI repository were utilized to show the significance of the proposed approach. The reported results show that BHHO-TVS outperforms BHHO with traditional binarization schemes as well as other binary feature selection methods such as binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), binary bat algorithm (BBA), binary whale optimization algorithm (BWOA), and binary salp swarm algorithm (BSSA). Compared with other similar feature selection approaches introduced in previous studies, the proposed method achieves the best accuracy rates on 67% of datasets.
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14
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Aygun B, Gunel Kilic B, Arici N, Cosar A, Tuncsiper B. Application of binary PSO for public cloud resources allocation system of video on demand (VoD) services. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2020.106870] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Nguyen BH, Xue B, Andreae P, Zhang M. A New Binary Particle Swarm Optimization Approach: Momentum and Dynamic Balance Between Exploration and Exploitation. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:589-603. [PMID: 31613790 DOI: 10.1109/tcyb.2019.2944141] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Particle swarm optimization (PSO) is a heuristic optimization algorithm generally applied to continuous domains. Binary PSO is a form of PSO applied to binary domains but uses the concepts of velocity and momentum from continuous PSO, which leads to its limited performance. In our previous work, we reformulated momentum as a stickiness property and velocity as a flipping probability to develop sticky binary PSO. The initial design provides a good base, but many key factors need to be investigated. In this article, we propose a new algorithm called dynamic sticky binary PSO by developing a dynamic parameter control strategy based on an investigation of exploration and exploitation in the binary search spaces. The proposed algorithm is compared with four state-of-the-art dynamic binary algorithms on two types of binary problems: 1) knapsack and 2) feature selection. The experimental results on the knapsack datasets show that the new velocity and momentum assist sticky binary PSO in evolving better solutions than the benchmark algorithms. On feature selection, the dynamic strategy takes the advantages of these two newly defined movement concepts to help the proposed algorithm to produce smaller feature subsets with higher classification performance. This is the first time in the binary PSO, the four important concepts, that is, velocity, momentum, exploration, and exploitation, are investigated systematically to capture the properties of the binary search spaces to evolve better solutions for binary problems.
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Ni Z, Xia P, Zhu X, Ding Y, Ni L. A novel ensemble pruning approach based on information exchange glowworm swarm optimization and complementarity measure. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020. [DOI: 10.3233/jifs-189149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Ensemble pruning has been widely used for enhancing classification ability employing a smaller number of classifiers. Ensemble pruning extracts a part of classifiers with good overall performance to form the final ensemble. Diversity and accuracy of classifiers are of vital importance for a successful ensemble. It is hard for the members in one ensemble to achieve both good diversity and high accuracy, simultaneously, because there is a tradeoff between them. Existing works usually search for the tradeoff in terms of diversity measures, or find it utilizing heuristic algorithms, which cannot gain the exact solution without exhaustive search. To address the above issue, a novel ensemble pruning method based on information exchange glowworm swarm optimization and complementarity measure, abbreviated EPIECM, is proposed using the combination of information exchange glowworm swarm optimization (IEGSO) and complementarity measure (COM). Firstly, multiple generated classifiers are utilized to construct a pool of learners who perform diversely. Secondly, COM is employed to pre-prune the classifiers with poor comprehensive performance, and the pre-pruned ensemble is formed utilizing the retaining classifiers. Finally, the optimal subset of classifiers is combined from the remaining constituents after pre-pruning with IEGSO. Empirical results on 27 UCI datasets indicate that EPIECM significantly outperforms other techniques.
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Affiliation(s)
- Zhiwei Ni
- School of Management, Hefei University of Technology, Hefei, China
- Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, China
| | - Pingfan Xia
- School of Management, Hefei University of Technology, Hefei, China
- Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, China
| | - Xuhui Zhu
- School of Management, Hefei University of Technology, Hefei, China
- Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, China
| | - Yufei Ding
- School of Computer Science, University of California Santa Barbara, California, USA
| | - Liping Ni
- School of Management, Hefei University of Technology, Hefei, China
- Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, China
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18
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19
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Ensemble pruning of ELM via migratory binary glowworm swarm optimization and margin distance minimization. Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10336-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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20
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Beheshti Z. A time-varying mirrored S-shaped transfer function for binary particle swarm optimization. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2019.10.029] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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21
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Abstract
This paper concerns several important topics of the Symmetry journal, namely, pattern recognition, computer-aided design, diversity and similarity. We also take advantage of the symmetric structure of a membership function. Searching for the (sub) optimal subset of features is an NP-hard problem. In this paper, a binary swallow swarm optimization (BSSO) algorithm for feature selection is proposed. To solve the classification problem, we use a fuzzy rule-based classifier. To evaluate the feature selection performance of our method, BSSO is compared to induction without feature selection and some similar algorithms on well-known benchmark datasets. Experimental results show the promising behavior of the proposed method in the optimal selection of features.
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Fast Multi-Objective Antenna Optimization Based on RBF Neural Network Surrogate Model Optimized by Improved PSO Algorithm. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9132589] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
In this paper, a radial basis function neural network (RBFNN) surrogate model optimized by an improved particle swarm optimization (PSO) algorithm is developed to reduce the computation cost of traditional antenna design methods which rely on high-fidelity electromagnetic (EM) simulations. Considering parameters adjustment and update mechanism simultaneously, two modifications are proposed in this improved PSO. First, time-varying learning factors are designed to balance exploration and exploitation ability of particles in the search space. Second, the local best information is added to the updating process of particles except for personal and global best information for better population diversity. The improved PSO is applied to train RBFNN for determining optimal network parameters. As a result, the constructed improved PSO-RBFNN model can be used as a surrogate model for antenna performance prediction with better network generalization capability. By integrating the improved PSO-RBFNN surrogate model with multi-objective evolutionary algorithms (MOEAs), a fast multi-objective antenna optimization framework for multi-parameter antenna structures is then established. Finally, a Pareto-optimal planar miniaturized multiband antenna design is presented, demonstrating that the proposed model provides better prediction performance and considerable computational savings compared to those previously published approaches.
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PSO-based novel resource scheduling technique to improve QoS parameters in cloud computing. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04266-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Al-Fakih AM, Algamal ZY, Lee MH, Aziz M, Ali HTM. A QSAR model for predicting antidiabetic activity of dipeptidyl peptidase-IV inhibitors by enhanced binary gravitational search algorithm. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2019; 30:403-416. [PMID: 31122062 DOI: 10.1080/1062936x.2019.1607899] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 04/11/2019] [Indexed: 06/09/2023]
Abstract
Time-varying binary gravitational search algorithm (TVBGSA) is proposed for predicting antidiabetic activity of 134 dipeptidyl peptidase-IV (DPP-IV) inhibitors. To improve the performance of the binary gravitational search algorithm (BGSA) method, we propose a dynamic time-varying transfer function. A new control parameter, μ , is added in the original transfer function as a time-varying variable. The TVBGSA-based model was internally and externally validated based on Qint2 , QLGO2 , QBoot2 , MSEtrain , Qext2 , MSEtest , Y-randomization test, and applicability domain evaluation. The validation results indicate that the proposed TVBGSA model is robust and not due to chance correlation. The descriptor selection and prediction performance of TVBGSA outperform BGSA method. TVBGSA shows higher Qint2 of 0.957, QLGO2 of 0.951, QBoot2 of 0.954, Qext2 of 0.938, and lower MSEtrain and MSEtest compared to obtained results by BGSA, indicating the best prediction performance of the proposed TVBGSA model. The results clearly reveal that the proposed TVBGSA method is useful for constructing reliable and robust QSARs for predicting antidiabetic activity of DPP-IV inhibitors prior to designing and experimental synthesizing of new DPP-IV inhibitors.
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Affiliation(s)
- A M Al-Fakih
- a Department of Chemistry, Faculty of Science , Universiti Teknologi Malaysia , Johor , Malaysia
- b Department of Chemistry, Faculty of Science , Sana'a University , Sana'a , Yemen
| | - Z Y Algamal
- c Department of Statistics and Informatics , University of Mosul , Mosul , Iraq
| | - M H Lee
- d Department of Mathematical Sciences, Faculty of Science , Universiti Teknologi Malaysia , Johor , Malaysia
| | - M Aziz
- a Department of Chemistry, Faculty of Science , Universiti Teknologi Malaysia , Johor , Malaysia
- e Advanced Membrane Technology Centre , Universiti Teknologi Malaysia , Johor , Malaysia
| | - H T M Ali
- f College of Computers and Information Technology , Nawroz University , Kurdistan region , Iraq
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Mafarja M, Aljarah I, Heidari AA, Faris H, Fournier-Viger P, Li X, Mirjalili S. Binary dragonfly optimization for feature selection using time-varying transfer functions. Knowl Based Syst 2018. [DOI: 10.1016/j.knosys.2018.08.003] [Citation(s) in RCA: 246] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Shukla UP, Nanda SJ. Dynamic clustering with binary social spider algorithm for streaming dataset. Soft comput 2018. [DOI: 10.1007/s00500-018-3627-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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