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ISSN (Online): 2212-392X

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Research Article

Enhanced Moth-flame Optimizer with Quasi-Reflection and Refraction Learning with Application to Image Segmentation and Medical Diagnosis

Author(s): Jianfu Xia, Zhennao Cai, Ali Asghar Heidari, Yinghai Ye*, Huiling Chen* and Zhifang Pan*

Volume 18, Issue 2, 2023

Published on: 04 November, 2022

Page: [109 - 142] Pages: 34

DOI: 10.2174/1574893617666220920102401

Price: $65

Abstract

Background: Moth-flame optimization will meet the premature and stagnation phenomenon when encountering difficult optimization tasks.

Objective: This paper presented a quasi-reflection moth-flame optimization algorithm with refraction learning called QRMFO to strengthen the property of ordinary MFO and apply it in various application fields to overcome shortcomings.

Methods: In the proposed QRMFO, quasi-reflection-based learning increases the diversity of the population and expands the search space on the iteration jump phase; refraction learning improves the accuracy of the potential optimal solution.

Results: Several experiments are conducted to evaluate the superiority of the proposed QRMFO in the paper; first of all, the CEC2017 benchmark suite is utilized to estimate the capability of QRMFO when dealing with the standard test sets compared with the state-of-the-art algorithms; afterward, QRMFO is adopted to deal with multilevel thresholding image segmentation problems and real medical diagnosis case.

Conclusion: Simulation results and discussions show that the proposed optimizer is superior to the basic MFO and other advanced methods in terms of convergence rate and solution accuracy.

Keywords: Moth-flame optimization, global optimization, multilevel thresholding image segmentation, medical diagnosis, particle swarm optimization, ACO.

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[1]
Cao B, Li M, Liu X, Zhao J, Cao W, Lv Z. Many-objective deployment optimization for a drone-assisted camera network. IEEE Trans Netw Sci Eng 2021; 8(4): 2756-64.
[http://dx.doi.org/10.1109/TNSE.2021.3057915]
[2]
Lu C, Liu Q, Zhang B, Yin L. A Pareto-based hybrid iterated greedy algorithm for energy-efficient scheduling of distributed hybrid flowshop. Expert Syst Appl 2022; 204: 117555.
[http://dx.doi.org/10.1016/j.eswa.2022.117555]
[3]
Xie Y, Sheng Y, Qiu M, Gui F. An adaptive decoding biased random key genetic algorithm for cloud workflow scheduling. Eng Appl Artif Intell 2022; 112: 104879.
[http://dx.doi.org/10.1016/j.engappai.2022.104879]
[4]
Mirjalili S, Dong JS, Lewis A. Nature-inspired optimizers: Theories, literature reviews and applications. Springer 2019; 811.
[5]
Shehab M, Abualigah L, Al Hamad H, Alabool H, Alshinwan M, Khasawneh AM. Moth–flame optimization algorithm: Variants and applications. Neural Comput Appl 2020; 32(14): 9859-84.
[http://dx.doi.org/10.1007/s00521-019-04570-6]
[6]
Mirjalili S. Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowl Base Syst 2015; 89: 228-49.
[http://dx.doi.org/10.1016/j.knosys.2015.07.006]
[7]
Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H. Harris hawks optimization: Algorithm and applications. Future Gener Comput Syst 2019; 97: 849-72.
[http://dx.doi.org/10.1016/j.future.2019.02.028]
[8]
Li S, Chen H, Wang M, Heidari AA, Mirjalili S. Slime mould algorithm: A new method for stochastic optimization. Future Gener Comput Syst 2020; 111: 300-23.
[http://dx.doi.org/10.1016/j.future.2020.03.055]
[9]
Tu J, Chen H, Wang M, Gandomi AH. The colony predation algorithm. J Bionics Eng 2021; 18(3): 674-710.
[http://dx.doi.org/10.1007/s42235-021-0050-y]
[10]
Ahmadianfar I, Heidari AA, Gandomi AH, Chu X, Chen H. RUN beyond the metaphor: An efficient optimization algorithm based on Runge Kutta method. Expert Syst Appl 2021; 181: 115079.
[http://dx.doi.org/10.1016/j.eswa.2021.115079]
[11]
Yang Y, Chen H, Heidari AA, Gandomi AH. Hunger games search: Visions, conception, implementation, deep analysis, perspectives, and towards performance shifts. Expert Syst Appl 2021; 177: 114864.
[http://dx.doi.org/10.1016/j.eswa.2021.114864]
[12]
Ahmadianfar I, Heidari AA, Noshadian S, Chen H, Gandomi AH. INFO: An efficient optimization algorithm based on weighted mean of vectors. Expert Syst Appl 2022; 195: 116516.
[http://dx.doi.org/10.1016/j.eswa.2022.116516]
[13]
Hussien AG, Heidari AA, Ye X, Liang G, Chen H, Pan Z. Boosting whale optimization with evolution strategy and Gaussian random walks: An image segmentation method. Eng Comput 2022.
[http://dx.doi.org/10.1007/s00366-021-01542-0]
[14]
Yu H, Song J, Chen C, et al. Image segmentation of Leaf Spot Diseases on Maize using multi-stage Cauchy-enabled grey wolf algorithm. Eng Appl Artif Intell 2022; 109: 104653.
[http://dx.doi.org/10.1016/j.engappai.2021.104653]
[15]
Cai Z, Gu J, Luo J, et al. Evolving an optimal kernel extreme learning machine by using an enhanced grey wolf optimization strategy. Expert Syst Appl 2019; 138: 112814.
[http://dx.doi.org/10.1016/j.eswa.2019.07.031]
[16]
Dong R, Chen H, Heidari AA, Turabieh H, Mafarja M, Wang S. Boosted kernel search: Framework, analysis and case studies on the economic emission dispatch problem. Knowl Base Syst 2021; 233: 107529.
[http://dx.doi.org/10.1016/j.knosys.2021.107529]
[17]
Yu H, Cheng X, Chen C, et al. Apple leaf disease recognition method with improved residual network. Multimedia Tools Appl 2022; 81(6): 7759-82.
[http://dx.doi.org/10.1007/s11042-022-11915-2]
[18]
Han X, Han Y, Chen Q, et al. Distributed flow shop scheduling with sequence-dependent setup times using an improved iterated greedy algorithm. Complex System Modeling and Simulation 2021; 1(3): 198-217.
[http://dx.doi.org/10.23919/CSMS.2021.0018]
[19]
Gao D, Wang GG, Pedrycz W. Solving fuzzy job-shop scheduling problem using DE algorithm improved by a selection mechanism. IEEE Trans Fuzzy Syst 2020; 28(12): 3265-75.
[http://dx.doi.org/10.1109/TFUZZ.2020.3003506]
[20]
Wang GG, Gao D, Pedrycz W. Solving multi-objective fuzzy job-shop scheduling problem by a hybrid adaptive differential evolution algorithm. IEEE Trans Industr Inform 2022; 1: 8516-28.
[http://dx.doi.org/10.1109/TII.2022.3165636]
[21]
Xia J, Yang D, Zhou H, et al. Evolving kernel extreme learning machine for medical diagnosis via a disperse foraging sine cosine algorithm. Comput Biol Med 2022; 141: 105137.
[http://dx.doi.org/10.1016/j.compbiomed.2021.105137] [PMID: 34953358]
[22]
Zhao F, Di S, Cao J, Tang J. Jonrinaldi. A novel cooperative multi-stage hyper-heuristic for combination optimization problems. Complex Syst Model Simulat 2021; 1(2): 91-108.
[http://dx.doi.org/10.23919/CSMS.2021.0010]
[23]
Deng W, Zhang X, Zhou Y, et al. An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems. Inf Sci 2022; 585: 441-53.
[http://dx.doi.org/10.1016/j.ins.2021.11.052]
[24]
Hua Y. A survey of evolutionary algorithms for multi-objective optimization problems with irregular pareto fronts. IEEE/CAA J Autom Sin 2021; 8(2): 303-18.
[http://dx.doi.org/10.1109/JAS.2021.1003817]
[25]
Hu J, Gui W, Heidari AA, et al. Dispersed foraging slime mould algorithm: Continuous and binary variants for global optimization and wrapper-based feature selection. Knowl Base Syst 2022; 237: 107761.
[http://dx.doi.org/10.1016/j.knosys.2021.107761]
[26]
He Z, Yen GG, Ding J. Knee-based decision making and visualization in many-objective optimization. IEEE Trans Evol Comput 2021; 25(2): 292-306.
[http://dx.doi.org/10.1109/TEVC.2020.3027620]
[27]
He Z, Yen GG, Lv J. Evolutionary multiobjective optimization with robustness enhancement. IEEE Trans Evol Comput 2020; 24(3): 494-507.
[http://dx.doi.org/10.1109/TEVC.2019.2933444]
[28]
Wang G, Gui W, Liang G, et al. Spiral motion enhanced elite whale optimizer for global tasks. Complexity 2021; 2021: 1-33.
[http://dx.doi.org/10.1155/2021/8130378]
[29]
Ling Chen H. Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy. Appl Math Comput 2014; 239: 180-97.
[30]
Yu H, Yuan K, Li W, et al. Improved butterfly optimizer-configured extreme learning machine for fault diagnosis. Complexity 2021; 2021: 1-17.
[http://dx.doi.org/10.1155/2021/6315010]
[31]
Ye X, Liu W, Li H, et al. Modified whale optimization algorithm for solar cell and PV module parameter identification. Complexity 2021; 2021: 1-23.
[http://dx.doi.org/10.1155/2021/8878686]
[32]
Apinantanakon W, Sunat K. OMFO: A new opposition-based moth-flame optimization algorithm for solving unconstrained optimization problems. In: Recent Advances in Information and Communication Technology 2017. Cham: Springer International Publishing 2018.
[http://dx.doi.org/10.1007/978-3-319-60663-7_3]
[33]
Emary E, Zawbaa HM. Impact of chaos functions on modern swarm optimizers. PLoS One 2016; 11(7): e0158738.
[http://dx.doi.org/10.1371/journal.pone.0158738] [PMID: 27410691]
[34]
Wang M, Chen H, Yang B, et al. Toward an optimal kernel extreme learning machine using a chaotic moth-flame optimization strategy with applications in medical diagnoses. Neurocomputing 2017; 267: 69-84.
[http://dx.doi.org/10.1016/j.neucom.2017.04.060]
[35]
Guvenc U, Duman S,. Hınıslıoglu Y. Chaotic moth swarm algorithm. In. IEEE International Conference on Innovations in Intelligent SysTems and Applications (INISTA). 03-05 July 2017; Gdynia, Poland: IEEE.
[36]
Xu Y, Chen H, Heidari AA, et al. An efficient chaotic mutative moth-flame-inspired optimizer for global optimization tasks. Expert Syst Appl 2019; 129: 135-55.
[http://dx.doi.org/10.1016/j.eswa.2019.03.043]
[37]
Li Z, Zhou Y, Zhang S, Song J. Lévy-Flight moth-flame algorithm for function optimization and engineering design problems. Math Probl Eng 2016; 2016: 1-22.
[http://dx.doi.org/10.1155/2016/1423930]
[38]
Xu Y, Chen H, Luo J, Zhang Q, Jiao S, Zhang X. Enhanced Moth-flame optimizer with mutation strategy for global optimization. Inf Sci 2019; 492: 181-203.
[http://dx.doi.org/10.1016/j.ins.2019.04.022]
[39]
Elsakaan AA, El-Sehiemy RA, Kaddah SS, Elsaid MI. An enhanced moth-flame optimizer for solving non-smooth economic dispatch problems with emissions. Energy 2018; 157: 1063-78.
[http://dx.doi.org/10.1016/j.energy.2018.06.088]
[40]
Li C, Niu Z, Song Z, Li B, Fan J, Liu PX. A double evolutionary learning moth-flame optimization for real-parameter global optimization problems. IEEE Access 2018; 6: 76700-27.
[http://dx.doi.org/10.1109/ACCESS.2018.2884130]
[41]
Sayed GI, Hassanien AE. A hybrid SA-MFO algorithm for function optimization and engineering design problems. Complex & Intelligent Systems 2018; 4(3): 195-212.
[http://dx.doi.org/10.1007/s40747-018-0066-z]
[42]
Bhesdadiya RH. A novel hybrid approach particle swarm optimizer with moth-flame optimizer algorithm. In: Advances in Computer and Computational Sciences. Singapore: Springer Singapore 2017.
[http://dx.doi.org/10.1007/978-981-10-3770-2_53]
[43]
Khelifi A, Bentouati B, Saliha C. Optimal power flow using hybrid particle swarm optimization and moth flame optimizer approach. Revue des sciences et sciences de l’ingénieur 2018; 7(2): 33-41.
[44]
Khalilpourazari S, Khalilpourazary S. An efficient hybrid algorithm based on Water Cycle and Moth-Flame Optimization algorithms for solving numerical and constrained engineering optimization problems. Soft Comput 2019; 23(5): 1699-722.
[http://dx.doi.org/10.1007/s00500-017-2894-y]
[45]
Khalilpourazari S, Pasandideh SHR. Modeling and optimization of multi-item multi-constrained EOQ model for growing items. Knowl Base Syst 2019; 164: 150-62.
[http://dx.doi.org/10.1016/j.knosys.2018.10.032]
[46]
Sarma A, Bhutani A, Goel L. Hybridization of moth flame optimization and gravitational search algorithm and its application to detection of food quality In: 2017 Intelligent Systems Conference. Intelli Sys 2017.
[47]
Zhang L, Mistry K, Neoh SC, Lim CP. Intelligent facial emotion recognition using moth-firefly optimization. Knowl Base Syst 2016; 111: 248-67.
[http://dx.doi.org/10.1016/j.knosys.2016.08.018]
[48]
Zhao Xd. An ameliorated moth-flame optimization algorithm. In: 2018 37th Chinese Control Conference (CCC). Wuhan, China: IEEE 2018.
[http://dx.doi.org/10.23919/ChiCC.2018.8482799]
[49]
K SR. Solution to unit commitment in power system operation planning using binary coded modified moth flame optimization algorithm (BMMFOA): A flame selection based computational technique. J Comput Sci 2018; 25: 298-317.
[50]
Punnathanam V, Kommadath R, Kotecha P. Extension and performance evaluation of recent optimization techniques on mixed integer optimization problems. In: 2016 IEEE Congress on Evolutionary Computation, CEC 2016. Vancouver, BC, Canada: IEEE. 2016.
[http://dx.doi.org/10.1109/CEC.2016.7744348]
[51]
Savsani V, Tawhid MA. Non-dominated sorting moth flame optimization (NS-MFO) for multi-objective problems. Eng Appl Artif Intell 2017; 63: 20-32.
[http://dx.doi.org/10.1016/j.engappai.2017.04.018]
[52]
Vikas, Nanda SJ. Multi-objective moth flame optimization. In: 2016 International Conference on Advances in Computing, Communications and Informatics. Jaipur, India: ICACCI 2016.
[53]
Dubey HM, Pandit M, Panigrahi BK. An overview and comparative analysis of recent bio-inspired optimization techniques for wind integrated multi-objective power dispatch. Swarm Evol Comput 2018; 38: 12-34.
[http://dx.doi.org/10.1016/j.swevo.2017.07.012]
[54]
Allam D, Yousri DA, Eteiba MB. Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame Optimization Algorithm. Energy Convers Manage 2016; 123: 535-48.
[http://dx.doi.org/10.1016/j.enconman.2016.06.052]
[55]
Sulaiman MH. An application of Moth-Flame Optimization algorithm for solving optimal reactive power dispatch problem. In. 4th IET Clean Energy and Technology Conference (CEAT 2016); 14-15 Nov. 2016: Kuala Lumpur, Malaysia. 2016.
[http://dx.doi.org/10.1049/cp.2016.1273]
[56]
Aziz MAE, Ewees AA, Hassanien AE. Whale optimization algorithm and moth-flame optimization for multilevel thresholding image segmentation. Expert Syst Appl 2017; 83: 242-56.
[http://dx.doi.org/10.1016/j.eswa.2017.04.023]
[57]
Sayed GI, Hassanien AE. Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images. Appl Intell 2017; 47(2): 397-408.
[http://dx.doi.org/10.1007/s10489-017-0897-0]
[58]
Gandomi AH, Kashani AR. Construction cost minimization of shallow foundation using recent swarm intelligence techniques. IEEE Trans Industr Inform 2018; 14(3): 1099-106.
[http://dx.doi.org/10.1109/TII.2017.2776132]
[59]
Li J, Niu D, Wu M, Wang Y, Li F, Dong H. Research on battery energy storage as backup power in the operation optimization of a regional integrated energy system. Energies 2018; 11(11): 2990-3009.
[http://dx.doi.org/10.3390/en11112990]
[60]
Shah YA, Habib HA, Aadil F, Khan MF, Maqsood M, Nawaz T. CAMONET: Moth-Flame Optimization (MFO) based clustering algorithm for VANETs. IEEE Access 2018; 6: 48611-24.
[http://dx.doi.org/10.1109/ACCESS.2018.2868118]
[61]
Tolba M, Rezk H, Tulsky V, Diab A, Abdelaziz A, Vanin A. Impact of optimum allocation of renewable distributed generations on distribution networks based on different optimization algorithms. Energies 2018; 11(1): 245-77.
[http://dx.doi.org/10.3390/en11010245]
[62]
Trivedi IN, Jangir P, Parmar SA, Jangir N. Optimal power flow with voltage stability improvement and loss reduction in power system using Moth-Flame Optimizer. Neural Comput Appl 2018; 30(6): 1889-904.
[http://dx.doi.org/10.1007/s00521-016-2794-6]
[63]
Das M, Singh MAK, Biswas A. Techno-economic optimization of an off-grid hybrid renewable energy system using metaheuristic optimization approaches-Case of a radio transmitter station in India. Energy Convers Manage 2019; 185: 339-52.
[http://dx.doi.org/10.1016/j.enconman.2019.01.107]
[64]
Goel L, Raman S, Dora SS, Bhutani A, Aditya AS, Mehta A. Hybrid computational intelligence algorithms and their applications to detect food quality. Artif Intell Rev 2020; 53(2): 1415-40.
[http://dx.doi.org/10.1007/s10462-019-09705-8]
[65]
Jalili A, Keshtgari M, Akbari R. A new framework for reliable control placement in software-defined networks based on multi-criteria clustering approach. Soft Comput 2020; 24(4): 2897-916.
[http://dx.doi.org/10.1007/s00500-019-04070-8]
[66]
Lei X, Fang M, Fujita H. Moth–flame optimization-based algorithm with synthetic dynamic PPI networks for discovering protein complexes. Knowl Base Syst 2019; 172: 76-85.
[http://dx.doi.org/10.1016/j.knosys.2019.02.011]
[67]
Mahata S, Saha SK, Kar R, Mandal D. A metaheuristic optimization approach to discretize the fractional order Laplacian operator without employing a discretization operator. Swarm Evol Comput 2019; 44: 534-45.
[http://dx.doi.org/10.1016/j.swevo.2018.06.007]
[68]
Li C, Li S, Liu Y. A least squares support vector machine model optimized by moth-flame optimization algorithm for annual power load forecasting. Appl Intell 2016; 45(4): 1166-78.
[http://dx.doi.org/10.1007/s10489-016-0810-2]
[69]
Jiang P, Li R, Lu H, Zhang X. Modeling of electricity demand forecast for power system. Neural Comput Appl 2020; 32(11): 6857-75.
[http://dx.doi.org/10.1007/s00521-019-04153-5]
[70]
Cao Z, Wang Y, Zheng W, et al. The algorithm of stereo vision and shape from shading based on endoscope imaging. Biomed Signal Process Control 2022; 76: 103658.
[http://dx.doi.org/10.1016/j.bspc.2022.103658]
[71]
Liu Y, Tian J, Hu R, et al. Improved feature point pair purification algorithm based on SIFT during endoscope image stitching. Front Neurorobot 2022; 16: 840594.
[http://dx.doi.org/10.3389/fnbot.2022.840594] [PMID: 35242022]
[72]
Zhang Z, Wang L, Zheng W, Yin L, Hu R, Yang B. Endoscope image mosaic based on pyramid ORB. Biomed Signal Process Control 2022; 71: 103261.
[http://dx.doi.org/10.1016/j.bspc.2021.103261]
[73]
Ergezer M, Simon D, Du D. Oppositional biogeography-based optimization. In. IEEE International Conference on Systems, Man and Cybernetics; 04 December 2009: San Antonio, TX, USA; IEEE 2009.
[http://dx.doi.org/10.1109/ICSMC.2009.5346043]
[74]
Ergezer M, Simon D. Mathematical and experimental analyses of oppositional algorithms. IEEE Trans Cybern 2014; 44(11): 2178-89.
[http://dx.doi.org/10.1109/TCYB.2014.2303117] [PMID: 25330478]
[75]
Ergezer M, Simon D. Probabilistic properties of fitness-based quasi-reflection in evolutionary algorithms. Comput Oper Res 2015; 63: 114-24.
[http://dx.doi.org/10.1016/j.cor.2015.03.013]
[76]
Yu F. The application of a novel OBL based on lens imaging principle in PSO. ACTA Electonica Sinica 2014; 42(2): 230.
[77]
Shao P, Wu ZJ, Zhou XY, Deng CS. Improved particle swarm optimization algorithm based on opposite learning of refraction. ACTA Electonica Sinica 2015; 43: 2137-44.
[78]
Long W, Wu T, Cai S, Liang X, Jiao J, Xu M. A novel grey wolf optimizer algorithm with refraction learning. IEEE Access 2019; 7: 57805-19.
[http://dx.doi.org/10.1109/ACCESS.2019.2910813]
[79]
Long W, Wu T, Jiao J, Tang M, Xu M. Refraction-learning-based whale optimization algorithm for high-dimensional problems and parameter estimation of PV model. Eng Appl Artif Intell 2020; 89: 103457.
[http://dx.doi.org/10.1016/j.engappai.2019.103457]
[80]
Remli MA, Deris S, Mohamad MS, Omatu S, Corchado JM. An enhanced scatter search with combined opposition-based learning for parameter estimation in large-scale kinetic models of biochemical systems. Eng Appl Artif Intell 2017; 62: 164-80.
[http://dx.doi.org/10.1016/j.engappai.2017.04.004]
[81]
Wu Z, Li G, Shen S, Lian X, Chen E, Xu G. Constructing dummy query sequences to protect location privacy and query privacy in location-based services. World Wide Web (Bussum) 2021; 24(1): 25-49.
[http://dx.doi.org/10.1007/s11280-020-00830-x]
[82]
Wu Z, Wang R, Li Q, et al. A location privacy-preserving system based on query range cover-up for location-based services. IEEE Trans Vehicular Technol 2020; 69(5): 5244-54.
[http://dx.doi.org/10.1109/TVT.2020.2981633]
[83]
Guan Q, Chen Y, Wei Z, et al. Medical image augmentation for lesion detection using a texture-constrained multichannel progressive GAN. Comput Biol Med 2022; 145: 105444.
[http://dx.doi.org/10.1016/j.compbiomed.2022.105444] [PMID: 35421795]
[84]
Chen Y, Yang XH, Wei Z, et al. Generative adversarial networks in medical image augmentation: A review. Comput Biol Med 2022; 144: 105382.
[http://dx.doi.org/10.1016/j.compbiomed.2022.105382] [PMID: 35276550]
[85]
Qiu S, Hongkai Z, Nan J, et al. Sensor network oriented human motion capture via wearable intelligent system. Int J Intell Syst 2021; 37(2): 1646-73.
[http://dx.doi.org/10.1002/int.22689]
[86]
Tian Y, Su X, Su Y, Zhang X. EMODMI: A multi-objective optimization based method to identify disease modules. IEEE Trans Emerg Top Comput Intell 2021; 5(4): 570-82.
[http://dx.doi.org/10.1109/TETCI.2020.3014923]
[87]
Su Y, Li S, Zheng C, Zhang X. A heuristic algorithm for identifying molecular signatures in cancer. IEEE Trans Nanobiosci 2020; 19(1): 132-41.
[http://dx.doi.org/10.1109/TNB.2019.2930647] [PMID: 31352348]
[88]
Yang Z, Ma J, Chen H, Zhang J, Chang Y. Context-aware attentive multilevel feature fusion for named entity recognition. IEEE Trans Neural Netw Learn Syst. 2022; 8: pp. 1-12.
[http://dx.doi.org/10.1109/TNNLS.2022.3178522] [PMID: 35675246]
[89]
Wu Z, Li R, Zhou Z, Guo J, Jiang J, Su X. A user sensitive subject protection approach for book search service. J Assoc Inf Sci Technol 2020; 71(2): 183-95.
[http://dx.doi.org/10.1002/asi.24227]
[90]
Wu Z, Shen S, Lian X, Su X, Chen E. A dummy-based user privacy protection approach for text information retrieval. Knowl Base Syst 2020; 195: 105679.
[http://dx.doi.org/10.1016/j.knosys.2020.105679]
[91]
Wu Z, Shen S, Zhou H, Li H, Lu C, Zou D. An effective approach for the protection of user commodity viewing privacy in e-commerce website. Knowl Base Syst 2021; 220: 106952.
[http://dx.doi.org/10.1016/j.knosys.2021.106952]
[92]
Gao X, Xiaoke M, Wensheng Z, et al. Multi-view clustering with self-representation and structural constraint. IEEE Trans Big Data 2022; 8(4): 882-93.
[http://dx.doi.org/10.1109/TBDATA.2021.3128906]
[93]
Wu W, Ma X. Network-based structural learning nonnegative matrix factorization algorithm for clustering of scRNA-seq data. IEEE/ACM Trans Comput Biol Bioinformatics 2022; 20(2): 566-75.
[http://dx.doi.org/10.1109/TCBB.2022.3161131]
[94]
Huang L, Yang Y, Chen H, Zhang Y, Wang Z, He L. Context-aware road travel time estimation by coupled tensor decomposition based on trajectory data. Knowl Base Syst 2022; 245: 108596.
[http://dx.doi.org/10.1016/j.knosys.2022.108596]
[95]
Zhang X, Hu W, Xie N, Bao H, Maybank S. A robust tracking system for low frame rate video. Int J Comput Vis 2015; 115(3): 279-304.
[http://dx.doi.org/10.1007/s11263-015-0819-8]
[96]
Zhang X. Hierarchical feature fusion with mixed convolution attention for single image dehazing. IEEE Transactions on Circuits and Systems for Video Technology. 2021.
[http://dx.doi.org/10.1109/TCSVT.2021.3067062]
[97]
Li D, Zhang S, Ma X. Dynamic module detection in temporal attributed networks of cancers. IEEE/ACM Trans Comput Biol Bioinformatics 2021.
[http://dx.doi.org/10.1109/TCBB.2021.3069441]
[98]
Ma X, Sun PG, Gong M. An integrative framework of heterogeneous genomic data for cancer dynamic modules based on matrix decomposition. IEEE/ACM Trans Comput Biol Bioinformatics 2020; 19(1): 305-16.
[http://dx.doi.org/10.1109/TCBB.2020.3004808]
[99]
Wang D, Liang Y, Xu D, Feng X, Guan R. A content-based recommender system for computer science publications. Knowl Base Syst 2018; 157: 1-9.
[http://dx.doi.org/10.1016/j.knosys.2018.05.001]
[100]
Li J, Chen C, Chen H, Tong C. Towards context-aware social recommendation via individual trust. Knowl Base Syst 2017; 127: 58-66.
[http://dx.doi.org/10.1016/j.knosys.2017.02.032]
[101]
Li J, Lin J. A probability distribution detection based hybrid ensemble QoS prediction approach. Inf Sci 2020; 519: 289-305.
[http://dx.doi.org/10.1016/j.ins.2020.01.046]
[102]
Li J, Zheng XL, Chen ST, Song WW, Chen D. An efficient and reliable approach for quality-of-service-aware service composition. Inf Sci 2014; 269: 238-54.
[http://dx.doi.org/10.1016/j.ins.2013.12.015]
[103]
Zhou D, Xue X, Zhou Z. SLE2: The improved social learning evolution model of cloud manufacturing service ecosystem. IEEE Trans Industr Inform 2022; 18(12): 9017-26.
[http://dx.doi.org/10.1109/TII.2022.3173053]
[104]
Xue X, Chen F, Zhou D, Wang X, Lu M, Wang FY. Computational experiments for complex social systems--Part I: The customization of computational model. IEEE Trans Comput Soc Syst 2021; 1-15.
[http://dx.doi.org/10.1109/TCSS.2021.3125287]
[105]
Li YH, Li XX, Hong JJ, et al. Clinical trials, progression-speed differentiating features and swiftness rule of the innovative targets of first-in-class drugs. Brief Bioinform 2020; 21(2): 649-62.
[http://dx.doi.org/10.1093/bib/bby130] [PMID: 30689717]
[106]
Zhu F, Li XX, Yang SY, Chen YZ. Clinical success of drug targets prospectively predicted by in silico study. Trends Pharmacol Sci 2018; 39(3): 229-31.
[http://dx.doi.org/10.1016/j.tips.2017.12.002] [PMID: 29295742]
[107]
Zhang X. Random reconstructed unpaired image-to-image translation. IEEE Trans Industr Inform 2022.
[http://dx.doi.org/10.1109/TII.2022.3160705]
[108]
Derrac J. García S, Molina D, Herrera F. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evol Comput 2011; 1(1): 3-18.
[http://dx.doi.org/10.1016/j.swevo.2011.02.002]
[109]
He S. MRMD2.0: A python tool for machine learning with feature ranking and reduction. Curr Bioinform 2020; 15(10): 1213-21.
[http://dx.doi.org/10.2174/2212392XMTA2bMjko1]
[110]
Wu X, Zheng W, Chen X, Zhao Y, Yu T, Mu D. Improving high-impact bug report prediction with combination of interactive machine learning and active learning. Inf Softw Technol 2021; 133: 106530.
[http://dx.doi.org/10.1016/j.infsof.2021.106530]
[111]
Liu K, Ke F, Huang X, et al. DeepBAN: A temporal convolution-based communication framework for dynamic WBANs. IEEE Trans Commun 2021; 69(10): 6675-90.
[http://dx.doi.org/10.1109/TCOMM.2021.3094581]
[112]
Liu R, Wang X, Lu H, et al. SCCGAN: Style and characters inpainting based on CGAN. Mob Netw Appl 2021; 26(1): 3-12.
[http://dx.doi.org/10.1007/s11036-020-01717-x]
[113]
Li J, Xu K, Chaudhuri S, Yumer E, Zhang H, Guibas L. Grass: Generative recursive autoencoders for shape structures. ACM Trans Graph 2017; 36(4): 1-14.
[http://dx.doi.org/10.1145/3072959.3073637]
[114]
Wang S, Guo H, Zhang S, Barton D, Brooks P. Analysis and prediction of double-carriage train wheel wear based on SIMPACK and neural networks. Adv Mech Eng 2022; 14(3): 1-12.
[http://dx.doi.org/10.1177/16878132221078491]
[115]
Daihong J, Sai Z, Lei D, Yueming D. Multi-scale generative adversarial network for image super-resolution. Soft Comput 2022; 26(8): 3631-41.
[http://dx.doi.org/10.1007/s00500-022-06822-5]
[116]
Awad NH, M.Z. Ali, J.J. Liang, B.Y. Qu. Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective bound constrained real-parameter numerical optimization. Singapore: Nanyang Technological University 2016.
[117]
Heidari AA, Ali Abbaspour R, Chen H. Efficient boosted grey wolf optimizers for global search and kernel extreme learning machine training. Appl Soft Comput 2019; 81: 105521.
[http://dx.doi.org/10.1016/j.asoc.2019.105521]
[118]
Tubishat M, Abushariah MAM, Idris N, Aljarah I. Improved whale optimization algorithm for feature selection in Arabic sentiment analysis. Appl Intell 2019; 49(5): 1688-707.
[http://dx.doi.org/10.1007/s10489-018-1334-8]
[119]
Ling Y, Zhou Y, Luo Q. Lévy flight trajectory-based whale optimization algorithm for global optimization. IEEE Access 2017; 5: 6168-86.
[http://dx.doi.org/10.1109/ACCESS.2017.2695498]
[120]
Li H, Liu J, Chen L, Bai J, Sun Y, Lu K. Chaos-enhanced moth-flame optimization algorithm for global optimization. J Syst Eng Electron 2019; 30(6): 1144-59.
[http://dx.doi.org/10.21629/JSEE.2019.06.10]
[121]
Mirjalili S, Mirjalili SM, Lewis A. Grey wolf optimizer. Adv Eng Softw 2014; 69: 46-61.
[http://dx.doi.org/10.1016/j.advengsoft.2013.12.007]
[122]
Mirjalili S, Lewis A. The whale optimization algorithm. Adv Eng Softw 2016; 95: 51-67.
[http://dx.doi.org/10.1016/j.advengsoft.2016.01.008]
[123]
Morales-Castañeda B, Zaldívar D, Cuevas E, Fausto F, Rodríguez A. A better balance in metaheuristic algorithms: Does it exist? Swarm Evol Comput 2020; 54: 100671.
[http://dx.doi.org/10.1016/j.swevo.2020.100671]
[124]
Xu Q, Zeng Y, Tang W, et al. Multi-task joint learning model for segmenting and classifying tongue images using a deep neural network. IEEE J Biomed Health Inform 2020; 24(9): 2481-9.
[http://dx.doi.org/10.1109/JBHI.2020.2986376] [PMID: 32310809]
[125]
Bhandari AK, Kumar A, Singh GK. Modified artificial bee colony based computationally efficient multilevel thresholding for satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Syst Appl 2015; 42(3): 1573-601.
[http://dx.doi.org/10.1016/j.eswa.2014.09.049]
[126]
Wu B, Zhou J, Ji X, Yin Y, Shen X. An ameliorated teaching–learning-based optimization algorithm based study of image segmentation for multilevel thresholding using Kapur’s entropy and Otsu’s between class variance. Inf Sci 2020; 533: 72-107.
[http://dx.doi.org/10.1016/j.ins.2020.05.033]
[127]
Zhao D, Liu L, Yu F, et al. Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy. Knowl Base Syst 2021; 216: 106510.
[http://dx.doi.org/10.1016/j.knosys.2020.106510]
[128]
Zhao C, Zhu Y, Du Y, Liao F, Chan CY. A novel direct trajectory planning approach based on generative adversarial networks and rapidly-exploring random tree. IEEE Trans Intell Transp Syst 2022; 1-12.
[http://dx.doi.org/10.1109/TITS.2022.3164391]
[129]
Yang XS. A new metaheuristic bat-inspired algorithm. In: González JR, Ed. Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Berlin: Heidelberg: Springer Berlin Heidelberg 2010; pp. 65-74.
[http://dx.doi.org/10.1007/978-3-642-12538-6_6]
[130]
Kennedy J, Eberhart R. Particle swarm optimization. Perth, WA, Australia 1995.
[131]
Liang JJ, Qin AK, Suganthan PN, Baskar S. Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 2006; 10(3): 281-95.
[http://dx.doi.org/10.1109/TEVC.2005.857610]
[132]
Xu C. Biogeography-based learning particle swarm optimization. Soft Comput 2016; 21(24): 1-23.
[133]
Liu H, Liu J, Hou S, Tao T, Han J. Perception consistency ultrasound image super-resolution via self-supervised CycleGAN. Neural Comput Appl 2021; 1-11.
[http://dx.doi.org/10.1007/s00521-020-05687-9]
[134]
Zhou G, Yang F, Xiao J. Study on pixel entanglement theory for imagery classification. IEEE Trans Geosci Remote Sens 2022; 60: 1-18.
[http://dx.doi.org/10.1109/TGRS.2022.3167569]
[135]
Zhang M, Chen Y, Lin J. A privacy-preserving optimization of neighborhood-based recommendation for medical-aided diagnosis and treatment. IEEE Internet Things J 2021; 8(13): 10830-42.
[http://dx.doi.org/10.1109/JIOT.2021.3051060]
[136]
Zhang M, Chen Y, Susilo W. PPO-CPQ: A privacy-preserving optimization of clinical pathway query for e-healthcare systems. IEEE Internet Things J 2020; 7(10): 10660-72.
[http://dx.doi.org/10.1109/JIOT.2020.3007518]
[137]
Guang-Bin Huang, Hongming Zhou, Xiaojian Ding, Rui Zhang. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern B Cybern 2012; 42(2): 513-29.
[http://dx.doi.org/10.1109/TSMCB.2011.2168604] [PMID: 21984515]
[138]
Chang CC, Lin CJ. LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2011; 2(3): 1-27.
[http://dx.doi.org/10.1145/1961189.1961199]
[139]
Chen H, Yang B, Liu D, et al. Using blood indexes to predict overweight statuses: An extreme learning machine-based approach. PLoS One 2015; 10(11): e0143003.
[http://dx.doi.org/10.1371/journal.pone.0143003] [PMID: 26600199]
[140]
Kadry S, Rajinikanth V. Grey scale image multi-thresholding using moth-flame algorithm and tsallis entropy. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 2020; 6(2): 79-89.
[141]
Kadry S, Rajinikanth V, Raja NSM, Jude Hemanth D, Hannon NMS, Raj ANJ. Evaluation of brain tumor using brain MRI with modified-moth-flame algorithm and Kapur’s thresholding: A study. Evol Intell 2021; 14(2): 1053-63.
[http://dx.doi.org/10.1007/s12065-020-00539-w]
[142]
Rajinikanth V, Kadry SC, Rubén G. Verdú E. A study on RGB image multi-thresholding using kapur/tsallis entropy and moth-flame algorithm. Inter J Interact Multi Artif Intell 2021; 7(2): 163-71.

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