Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis

Wang, Mingjing; Chen, Huiling

doi:10.1016/j.asoc.2019.105946

Cited by 384 publications

(153 citation statements)

References 38 publications

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“…WOA recently emerged as a beneficial algorithm, which is applied in many practical problems such as forecasting water resources demand [40], flow shop scheduling problem [41], opinion leader detection in online social network [42], medical diagnosis [43]. However, it was first proposed in [44], which mimicked the unusual social behaviors and the engaging hunting activities of humpback whales.…”

Section: Whale Optimization Algorithm (Woa)mentioning

confidence: 99%

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

et al. 2020

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The works presented in this paper addresses the robust population-based global optimization that is influenced by the simplicity and efficiency principles introduced in two new generation optimization algorithms. Galactic Swarm Optimization is inspired by the motion of stars, galaxies, and superclusters of galaxies under the influence of gravity. It acts well as a global controller of the whole optimization process by employing multiple flexible cycles of exploration and exploitation phases to find new, better solutions. However, the optimization process still suffers poverty in the exploitation phase, which is improved in this work by its hybridization with our evolution version of the Whale Optimization Algorithm. Concretely, the exploitation phase of Galactic Swarm Optimization is replaced by our Evolution Whale Optimization Algorithm to avoid early convergence. The Whale Optimization Algorithm mimics the unusual social behaviors and the hunting activities of humpback whales. However, it is not optimized for global optimization when the number of dimensions is increased. Hence its is evolved in our works by Levy-Flight trajectory for faster local search with adaptive step lengths and two-point crossover operator to reduce bias in the offspring creation procedure. The achieved results through extensive and careful experiments showed that our hybridization and evolution enhancements bring outstanding performance in terms of accuracy, convergence speed, and stability.

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Section: Whale Optimization Algorithm (Woa)mentioning

confidence: 99%

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

et al. 2020

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“…Also, the total ranking obtained for models is featured in Table 4. The same process is provided in other machine learning-based studies (e.g., [30][31][32][33], [36][37][38][39], [41], [42], [75], [76]). A graphical exhibition of the results is also presented, based on a colour intensity model.…”

Section: Model Assessment In the Percentage Of Drag Reduction Predictionmentioning

confidence: 99%

“…Most recently developed prediction techniques employed the computer science advances and intended to find a reliable solution in solving engineering and medical related problems. In this sense, the technique of extreme machine learning approaches [30][31][32]33], Harris hawks optimization [34,35], spatial adjacent histogram [36], fruit fly optimization [37], chaotic moth-flame optimization [38,39,40], multi-swarm whale optimizer [41], grey wolf optimization [42] can be mentioned. Such multi-disciplinary techniques are widely used as analysis tools in most complex engineering projects such as building information modelling [43,44], sustainable sediment management in hydropower [45], contractors' dynamic price competition in mega projects [46], emotion recognition and image sharing [47], wireless sensor networks [48,49], big data application [50,51], landslide prediction over a large region [52], Digital Neuromorphic Architecture [53].…”

Section: Introductionmentioning

confidence: 99%

Soft computing method for predicting pressure drop reduction in crude oil pipelines based on machine learning methods

Moayedi

Foong

Nguyen

2020

J Braz. Soc. Mech. Sci. Eng.

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In the oil industry, the drag-reducing agent has been used to reduce turbulent friction of fluids. The main effort of this study is to examine the feasibility of four novel machine learning models, namely multilayer perceptron, M5Rules, decision table (DT), and trees M5P to estimate the percentage of drag reduction. Then, the mentioned methods are utilized to identify a relationship between the input and output parameters of the crude oil pipeline system. The parameter percentage of drag reduction was taken as the essential output. In contrast, the input parameters selected the flow rate of oil, polymer concentration, kind of polymer, temperature, as well as pipe diameter and roughness. The predicted results obtained by the tools mentioned above were evaluated according to several known statistical indices, namely coefficient of determination (R 2), mean absolute error (MAE), root mean squared error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) as well as novel ranking systems of color intensity rating and total ranking method. The training and testing results of the DT learning method for the

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“…As a part of support vector machine (SVM) [1]- [3], support vector regression (SVR) has produced gratifying achievements in the field of prediction. However, poor choices of penalty factor, kernel type and others can markedly reduce the SVR's capability.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm

Chen

Liu

2020

IEEE Access

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Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high-and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field. INDEX TERMS Machine learning, forecasting problem, K-means clustering method, chaotic slime mould algorithm, support vector regression.

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Chaotic multi-swarm whale optimizer boosted support vector machine for medical diagnosis

Cited by 384 publications

References 38 publications

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

Hybridization of Galactic Swarm and Evolution Whale Optimization for Global Search Problem

Soft computing method for predicting pressure drop reduction in crude oil pipelines based on machine learning methods

An Efficient Parameter Adaptive Support Vector Regression Using K-Means Clustering and Chaotic Slime Mould Algorithm

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