Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm

Xia, Jianfu; Yang, Daping; Zhou, Hong; Chen, Yuyan; Hong-liang, Zhang; Liu, Tong; Heidari, Ali Asghar; Chen, Huiling; Pan, Zhifang

doi:10.1016/j.compbiomed.2021.105137

Cited by 71 publications

(29 citation statements)

References 120 publications

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“…Table 3 records the combined results of IHGS at 10D, 30D, 50D, and 100D based on the Friedman test and Wilcoxon singed-rank test with a significance level of 0.05. From the perspective of symbols of "+/À/=", among the 30 examples in lower dimensions (10D), the IHGS algorithm has 20,8,17,20,7,29,22,1,21,9,18,4,17,29 examples outperforming OBSCA, EB_LSHADE, SCADE, CBA, EAGDE, CESCA, AMFOA, EBOW, RCBA, JSO, HGWO, LSHADESPACMA, BMWOA, MSFOA, and there are 7, 21, 7, 5, 21, 0, 8, 28, 5, 18, 10, 19, 9, 1 examples weaker than them, while there are 3, 1, 6, 5, 2, 1, 0, 1, 4, 3, 2, 7, 4 examples with the same performance as them. As the problem's dimensionality increases, IHGS is more advantageous in handling high-dimensional problems.…”

Section: Detailed Results For the Ieee Cec 2017 Function Setmentioning

confidence: 99%

Quantum Nelder‐Mead Hunger Games Search for optimizing photovoltaic solar cells

Heidari

Kuang

et al. 2022

Intl J of Energy Research

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Summary The inherent multimodal and nonlinear characteristics of solar photovoltaic (PV) systems make it challenging to accurately extract PV parameters. Therefore, this study proposes an efficient and accurate optimization technique, improved Hunger Games Search, to identify unknown parameters in PV systems named IHGS. IHGS makes full use of the population information generated by the differential vectors while incorporating the quantum rotation gate strategy and the Nelder‐Mead simplex method (NMs) are fused to further increase the population diversity while rotating the corresponding positions of the search agents, thus efficiently exploring the neighborhood of the optimal solution in the decision space and improving the stability of the algorithm in developing the global optimal solution. The first part of the experiments is to simply verify the effectiveness and the stable merit‐seeking ability of the IHGS to handle complex functions by putting the proposed IHGS to test on the IEEE CEC 2017 benchmark test suite. The efficiency of IHGS is verified based on Wilcoxon singed‐rank test and Friedman tests. Next, we apply IHGS to three different PV module models (single‐diode, double‐diode, and PV module) for PV parameter extraction experiments to verify the efficiency and accuracy of IHGS in extracting PV parameters. The root mean square error produced by the IHGS proposed in this study is 9.8602E‐04, 9.8248E‐04, and 2.4251E‐03 for SDM, DDM, and PV modules, respectively. Examples show that IHGS outperforms HGS and other advanced algorithms in terms of convergence speed, data fitting, consistency, and stability. Finally, we perform the PV module parameter extraction problem on three commercial PV models (thin‐film ST40, monocrystalline SM55, and multicrystalline KC200GT). We observe that IHGS can still maintain high accuracy when dealing with commercial PV models under complex environments such as different irradiation and temperature. The results show that IHGS is a valuable optimization technique for solar PV parameter estimation based on multi‐model prediction.

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Section: Detailed Results For the Ieee Cec 2017 Function Setmentioning

confidence: 99%

Quantum Nelder‐Mead Hunger Games Search for optimizing photovoltaic solar cells

Heidari

Kuang

et al. 2022

Intl J of Energy Research

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“…According to the study, the performance of the classifier is affected greatly by its inner parameters and the features in the data. Metaheuristics have great effectiveness in solving this type of problem as shown in many works [28][29][30][31][32], such as object tracking [33,34], the traveling salesman problem [35], gate resource allocation [36,37], multi-attribute decisionmaking [38,39], the design of the power electronic circuit [40,41], fractional-order controllers [42], medical diagnoses [43,44], big data optimization problems [45], green supplier selections [46], economic emission dispatch problems [47], scheduling problems [48,49], and combination optimization problems [50]. This study proposes an enhanced crow search algorithm (CSA) [51] to simultaneously optimize the hyperparameters of the kernel extreme learning machine (KELM) and the feature space for predicting the entrepreneurial intention of college students.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Specifically, average result (Avg) and standard deviation (Std) represent the average prediction result and standard deviation of each model under ten experiments, respectively. To find the best model, we evaluated each model using 10-fold cross-validation, which was adopted in many studies [43,44,113]. Moreover, the prediction results were analyzed by using the common metrics [114][115][116], including accuracy (ACC), sensitivity (Sens), specificity (Spec), and the Matthews correlation coefficient (MCC).…”

Section: Condition Configurationmentioning

confidence: 99%

Predicting Entrepreneurial Intention of Students: Kernel Extreme Learning Machine with Boosted Crow Search Algorithm

Zhang¹,

Yan

et al. 2022

Applied Sciences

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College students are the group with the most entrepreneurial vitality and potential. How to cultivate their entrepreneurial and innovative ability is one of the important and urgent issues facing this current social development. This paper proposes a reliable, intelligent prediction model of entrepreneurial intentions, providing theoretical support for guiding college students’ positive entrepreneurial intentions. The model mainly uses the improved crow search algorithm (CSA) to optimize the kernel extreme learning machine (KELM) model with feature selection (FS), namely CSA-KELM-FS, to study entrepreneurial intention. To obtain the best fitting model and key features, the gradient search rule, local escaping operator, and levy flight mutation (GLL) mechanism are introduced to enhance the CSA (GLLCSA), and FS is used to extract the key features. To verify the performance of the proposed GLLCSA, it is compared with eight other state-of-the-art methods. Further, the GLLCSA-KELM-FS model and five other machine learning methods have been used to predict the entrepreneurial intentions of 842 students from the Wenzhou Vocational College in Zhejiang, China, in the past five years. The results show that the proposed model can correctly predict the students’ entrepreneurial intention with an accuracy rate of 93.2% and excellent stability. According to the prediction results of the proposed model, the key factors affecting the student’s entrepreneurial intention are mainly the major studied, campus innovation, entrepreneurship practice experience, and positive personality. Therefore, the proposed GLLCSA-KELM-FS is expected to be an effective tool for predicting students’ entrepreneurial intentions.

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“…As a result, more and more researchers have started introducing swarm intelligence algorithm (SIOA) into the traditional MTIS to improve the segmentation efficiency instead of the traditional exhaustive method. These SIOAs has offered greater efficiency in optimization tasks such as expensive optimization problems [ 32 , 33 ], medical diagnosis [ [34] , [35] , [36] , [37] ], PID optimization control [ [38] , [39] , [40] ], plant disease recognition [ 41 ], feature selection [ [42] , [43] , [44] , [45] ], object tracking [ 46 , 47 ], economic emission dispatch problem [ 48 ], engineering design [ [49] , [50] , [51] ], parameter tuning for machine learning models [ [52] , [53] , [54] ], constrained optimization problems [ 55 , 56 ], combination optimization problems [ 57 ], traveling salesman problem [ 58 ], multi-objective or many optimization problems [ [59] , [60] , [61] ], and scheduling problems [ [62] , [63] , [64] ].…”

Section: Introductionmentioning

confidence: 99%