{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T13:01:02Z","timestamp":1772283662565,"version":"3.50.1"},"reference-count":94,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T00:00:00Z","timestamp":1772236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Project of Education Department of Yunnan Province","award":["2025J0385"],"award-info":[{"award-number":["2025J0385"]}]},{"name":"Princess Nourah bint Abdulrahman University Researchers Supporting Project","award":["PNURSP2025R746"],"award-info":[{"award-number":["PNURSP2025R746"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s13042-025-02931-3","type":"journal-article","created":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T12:13:09Z","timestamp":1772280789000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Competitive differential evolution with success-failure adaptation mechanism: performance benchmarking and application in eggplant disease diagnosis"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5271-2298","authenticated-orcid":false,"given":"Zhongmin","family":"Wang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5593-4591","authenticated-orcid":false,"given":"Daihong","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5029-0294","authenticated-orcid":false,"given":"Jun","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Mahmoud","family":"Abdel-Salam","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Hu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8127-7233","authenticated-orcid":false,"given":"Essam H.","family":"Houssein","sequence":"additional","affiliation":[]},{"given":"Nagwan Abdel","family":"Samee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4605-5579","authenticated-orcid":false,"given":"Rui","family":"Zhong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,28]]},"reference":[{"key":"2931_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.106959","volume":"126","author":"A Mohammadi","year":"2023","unstructured":"Mohammadi A, Sheikholeslam F (2023) Intelligent optimization: literature review and state-of-the-art algorithms (1965\u20132022). Eng Appl Artif Intell 126:106959. https:\/\/doi.org\/10.1016\/j.engappai.2023.106959","journal-title":"Eng Appl Artif Intell"},{"issue":"1","key":"2931_CR2","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1007\/s10462-021-10042-y","volume":"55","author":"Z-H Zhan","year":"2022","unstructured":"Zhan Z-H, Shi L, Tan KC, Zhang J (2022) A survey on evolutionary computation for complex continuous optimization. Artif Intell Rev 55(1):59\u2013110. https:\/\/doi.org\/10.1007\/s10462-021-10042-y","journal-title":"Artif Intell Rev"},{"key":"2931_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.cam.2022.114106","volume":"408","author":"K-H Pho","year":"2022","unstructured":"Pho K-H (2022) Improvements of the newton-raphson method. J Comput Appl Math 408:114106. https:\/\/doi.org\/10.1016\/j.cam.2022.114106","journal-title":"J Comput Appl Math"},{"issue":"3","key":"2931_CR4","doi-asserted-by":"publisher","first-page":"682","DOI":"10.3390\/math11030682","volume":"11","author":"Y Tian","year":"2023","unstructured":"Tian Y, Zhang Y, Zhang H (2023) Recent advances in stochastic gradient descent in deep learning. Mathematics 11(3):682. https:\/\/doi.org\/10.3390\/math11030682","journal-title":"Mathematics"},{"issue":"4","key":"2931_CR5","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1093\/comjnl\/7.4.308","volume":"7","author":"JA Nelder","year":"1965","unstructured":"Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308\u2013313. https:\/\/doi.org\/10.1093\/comjnl\/7.4.308","journal-title":"Comput J"},{"issue":"Suppl 1","key":"2931_CR6","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/s11023-023-09633-1","volume":"34","author":"C Carissimo","year":"2024","unstructured":"Carissimo C, Korecki M (2024) Limits of optimization. Mind Mach 34(Suppl 1):117\u2013137. https:\/\/doi.org\/10.1007\/s11023-023-09633-1","journal-title":"Mind Mach"},{"key":"2931_CR7","doi-asserted-by":"publisher","first-page":"3665","DOI":"10.1007\/s00366-020-01025-8","volume":"37","author":"Z Wang","year":"2021","unstructured":"Wang Z, Luo Q, Zhou Y (2021) Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems. Eng Comput 37:3665\u20133698. https:\/\/doi.org\/10.1007\/s00366-020-01025-8","journal-title":"Eng Comput"},{"issue":"3","key":"2931_CR8","doi-asserted-by":"publisher","first-page":"1245","DOI":"10.1007\/s12065-023-00822-6","volume":"17","author":"B Alhijawi","year":"2024","unstructured":"Alhijawi B, Awajan A (2024) Genetic algorithms: theory, genetic operators, solutions, and applications. Evol Intel 17(3):1245\u20131256. https:\/\/doi.org\/10.1007\/s12065-023-00822-6","journal-title":"Evol Intel"},{"key":"2931_CR9","doi-asserted-by":"publisher","unstructured":"Pardalos PM, Mavridou TD (2024) Simulated annealing. In: Encyclopedia of optimization. Springer, pp 1\u20133. https:\/\/doi.org\/10.1007\/978-3-030-54621-2_617-1","DOI":"10.1007\/978-3-030-54621-2_617-1"},{"key":"2931_CR10","doi-asserted-by":"publisher","first-page":"10031","DOI":"10.1109\/ACCESS.2022.3142859","volume":"10","author":"TM Shami","year":"2022","unstructured":"Shami TM, El-Saleh AA, Alswaitti M, Al-Tashi Q, Summakieh MA, Mirjalili S (2022) Particle swarm optimization: a comprehensive survey. IEEE Access 10:10031\u201310061. https:\/\/doi.org\/10.1109\/ACCESS.2022.3142859","journal-title":"IEEE Access"},{"issue":"5","key":"2931_CR11","doi-asserted-by":"publisher","first-page":"3831","DOI":"10.1016\/j.aej.2021.09.013","volume":"61","author":"MF Ahmad","year":"2022","unstructured":"Ahmad MF, Isa NAM, Lim WH, Ang KM (2022) Differential evolution: a recent review based on state-of-the-art works. Alex Eng J 61(5):3831\u20133872. https:\/\/doi.org\/10.1016\/j.aej.2021.09.013","journal-title":"Alex Eng J"},{"key":"2931_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2023.100559","volume":"49","author":"M Nssibi","year":"2023","unstructured":"Nssibi M, Manita G, Korbaa O (2023) Advances in nature-inspired metaheuristic optimization for feature selection problem: a comprehensive survey. Comput Sci Rev 49:100559. https:\/\/doi.org\/10.1016\/j.cosrev.2023.100559","journal-title":"Comput Sci Rev"},{"issue":"3","key":"2931_CR13","doi-asserted-by":"publisher","first-page":"1727","DOI":"10.1007\/s11831-022-09850-4","volume":"30","author":"JO Agushaka","year":"2023","unstructured":"Agushaka JO, Ezugwu AE, Abualigah L, Alharbi SK, Khalifa HAE-W (2023) Efficient initialization methods for population-based metaheuristic algorithms: a comparative study. Archi Comput Methods Eng 30(3):1727\u20131787. https:\/\/doi.org\/10.1007\/s11831-022-09850-4","journal-title":"Archi Comput Methods Eng"},{"key":"2931_CR14","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s00521-013-1367-1","volume":"24","author":"X-S Yang","year":"2014","unstructured":"Yang X-S, Deb S (2014) Cuckoo search: recent advances and applications. Neural Comput Appl 24:169\u2013174. https:\/\/doi.org\/10.1007\/s00521-013-1367-1","journal-title":"Neural Comput Appl"},{"issue":"15","key":"2931_CR15","doi-asserted-by":"publisher","first-page":"11195","DOI":"10.1007\/s00521-019-04629-4","volume":"32","author":"L Abualigah","year":"2020","unstructured":"Abualigah L, Shehab M, Alshinwan M, Alabool H (2020) Salp swarm algorithm: a comprehensive survey. Neural Comput Appl 32(15):11195\u201311215. https:\/\/doi.org\/10.1007\/s00521-019-04629-4","journal-title":"Neural Comput Appl"},{"key":"2931_CR16","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361","journal-title":"Adv Eng Softw"},{"key":"2931_CR17","doi-asserted-by":"publisher","unstructured":"Damia A, Esnaashari M, Parvizimosaed M (2021) Adaptive genetic algorithm based on mutation and crossover and selection probabilities. In: 2021 7th international conference on web research (ICWR). IEEE, pp 86\u201390. https:\/\/doi.org\/10.1109\/ICWR51868.2021.9443124","DOI":"10.1109\/ICWR51868.2021.9443124"},{"issue":"7","key":"2931_CR18","doi-asserted-by":"publisher","first-page":"5455","DOI":"10.1007\/s00500-020-05545-9","volume":"25","author":"AK Das","year":"2021","unstructured":"Das AK, Pratihar DK (2021) Solving engineering optimization problems using an improved real-coded genetic algorithm (irga) with directional mutation and crossover. Soft Comput 25(7):5455\u20135481. https:\/\/doi.org\/10.1007\/s00500-020-05545-9","journal-title":"Soft Comput"},{"issue":"5","key":"2931_CR19","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.1007\/s42235-023-00367-5","volume":"20","author":"H Hu","year":"2023","unstructured":"Hu H, Shan W, Chen J, Xing L, Heidari AA, Chen H, He X, Wang M (2023) Dynamic individual selection and crossover boosted forensic-based investigation algorithm for global optimization and feature selection. J Bionic Eng 20(5):2416\u20132442. https:\/\/doi.org\/10.1007\/s42235-023-00367-5","journal-title":"J Bionic Eng"},{"issue":"12","key":"2931_CR20","doi-asserted-by":"publisher","first-page":"13930","DOI":"10.1109\/TITS.2023.3308593","volume":"24","author":"J Li","year":"2023","unstructured":"Li J, Liu R, Wang R (2023) Elastic strategy-based adaptive genetic algorithm for solving dynamic vehicle routing problem with time windows. IEEE Trans Intell Transp Syst 24(12):13930\u201313947. https:\/\/doi.org\/10.1109\/TITS.2023.3308593","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2931_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119805","volume":"222","author":"H Cui","year":"2023","unstructured":"Cui H, Li X, Gao L (2023) An improved multi-population genetic algorithm with a greedy job insertion inter-factory neighborhood structure for distributed heterogeneous hybrid flow shop scheduling problem. Expert Syst Appl 222:119805. https:\/\/doi.org\/10.1016\/j.eswa.2023.119805","journal-title":"Expert Syst Appl"},{"key":"2931_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101575","volume":"87","author":"G Zhou","year":"2024","unstructured":"Zhou G, Xie Y, Lan H, Tian W, Buyya R, Wu K (2024) Information interaction and partial growth-based multi-population growable genetic algorithm for multi-dimensional resources utilization optimization of cloud computing. Swarm Evol Comput 87:101575. https:\/\/doi.org\/10.1016\/j.swevo.2024.101575","journal-title":"Swarm Evol Comput"},{"key":"2931_CR23","doi-asserted-by":"publisher","unstructured":"Hong J, Shi L, Du K-J, Chen C-H, Wang H, Zhang J, Zhan Z-H (2023) Amulti-population genetic algorithm for multiobjective recommendation system. In: 2023 IEEE symposium series on computational intelligence (SSCI). IEEE, pp 998\u20131003. https:\/\/doi.org\/10.1109\/SSCI52147.2023.10371831","DOI":"10.1109\/SSCI52147.2023.10371831"},{"issue":"4","key":"2931_CR24","doi-asserted-by":"publisher","first-page":"1781","DOI":"10.1007\/s10845-023-02130-3","volume":"35","author":"N-T Huynh","year":"2024","unstructured":"Huynh N-T (2024) A multi-subpopulation genetic algorithm-based cnn approach for ceramic tile defects classification. J Intell Manuf 35(4):1781\u20131792. https:\/\/doi.org\/10.1007\/s10845-023-02130-3","journal-title":"J Intell Manuf"},{"key":"2931_CR25","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.ins.2020.08.040","volume":"547","author":"G D\u2019Angelo","year":"2021","unstructured":"D\u2019Angelo G, Palmieri F (2021) Gga: a modified genetic algorithm with gradient-based local search for solving constrained optimization problems. Inf Sci 547:136\u2013162. https:\/\/doi.org\/10.1016\/j.ins.2020.08.040","journal-title":"Inf Sci"},{"key":"2931_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122195","volume":"238","author":"R Mishra","year":"2024","unstructured":"Mishra R, Bajpai MK (2024) A novel multi-agent genetic algorithm for limited-view computed tomography. Expert Syst Appl 238:122195. https:\/\/doi.org\/10.1016\/j.eswa.2023.122195","journal-title":"Expert Syst Appl"},{"issue":"1","key":"2931_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ejor.2024.03.030","volume":"321","author":"MA Londe","year":"2025","unstructured":"Londe MA, Pessoa LS, Andrade CE, Resende MG (2025) Biased random-key genetic algorithms: a review. Eur J Oper Res 321(1):1\u201322. https:\/\/doi.org\/10.1016\/j.ejor.2024.03.030","journal-title":"Eur J Oper Res"},{"issue":"12","key":"2931_CR28","doi-asserted-by":"publisher","first-page":"2280","DOI":"10.3390\/sym13122280","volume":"13","author":"N Ul Hassan","year":"2021","unstructured":"Ul Hassan N, Bangyal WH, Ali Khan MS, Nisar K, Ag. Ibrahim AA, Rawat DB (2021) Improved opposition-based particle swarm optimization algorithm for global optimization. Symmetry 13(12):2280. https:\/\/doi.org\/10.3390\/sym13122280","journal-title":"Symmetry"},{"issue":"6","key":"2931_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12652-021-03534-w","volume":"14","author":"S Choudhary","year":"2023","unstructured":"Choudhary S, Sugumaran S, Belazi A, El-Latif AAA (2023) Linearly decreasing inertia weight pso and improved weight factor-based clustering algorithm for wireless sensor networks. J Ambient Intell Humaniz Comput 14(6):1\u201319. https:\/\/doi.org\/10.1007\/s12652-021-03534-w","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"2","key":"2931_CR30","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s12065-018-0188-7","volume":"14","author":"A Agrawal","year":"2021","unstructured":"Agrawal A, Tripathi S (2021) Particle swarm optimization with adaptive inertia weight based on cumulative binomial probability. Evol Intell 14(2):305\u2013313. https:\/\/doi.org\/10.1007\/s12065-018-0188-7","journal-title":"Evol Intell"},{"issue":"10","key":"2931_CR31","doi-asserted-by":"publisher","first-page":"14585","DOI":"10.1007\/s10586-024-04676-0","volume":"27","author":"S Bawa","year":"2024","unstructured":"Bawa S, Rana PS, Tekchandani R (2024) Migration of containers on the basis of load prediction with dynamic inertia weight based pso algorithm. Clust Comput 27(10):14585\u201314609. https:\/\/doi.org\/10.1007\/s10586-024-04676-0","journal-title":"Clust Comput"},{"key":"2931_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-024-04676-0","volume":"238","author":"S Hou","year":"2024","unstructured":"Hou S, Gebreyesus GD, Fujimura S (2024) Day-ahead multi-modal demand side management in microgrid via two-stage improved ring-topology particle swarm optimization. Expert Syst Appl 238:122135. https:\/\/doi.org\/10.1007\/s10586-024-04676-0","journal-title":"Expert Syst Appl"},{"key":"2931_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108039","volume":"114","author":"R Vafashoar","year":"2022","unstructured":"Vafashoar R, Morshedlou H, Meybodi MR (2022) Bifurcated particle swarm optimizer with topology learning particles. Appl Soft Comput 114:108039. https:\/\/doi.org\/10.1016\/j.asoc.2021.108039","journal-title":"Appl Soft Comput"},{"key":"2931_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.100868","volume":"63","author":"EH Houssein","year":"2021","unstructured":"Houssein EH, Gad AG, Hussain K, Suganthan PN (2021) Major advances in particle swarm optimization: theory, analysis, and application. Swarm Evol Comput 63:100868. https:\/\/doi.org\/10.1016\/j.swevo.2021.100868","journal-title":"Swarm Evol Comput"},{"issue":"4","key":"2931_CR35","doi-asserted-by":"publisher","first-page":"16","DOI":"10.4018\/IJAMC.2020100102","volume":"11","author":"WH Bangyal","year":"2020","unstructured":"Bangyal WH, Ahmad J, Rauf HT (2020) An overview of mutation strategies in particle swarm optimization. Int J Appl Metaheuristic Comput (IJAMC) 11(4):16\u201337","journal-title":"Int J Appl Metaheuristic Comput (IJAMC)"},{"key":"2931_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101533","volume":"86","author":"D Tian","year":"2024","unstructured":"Tian D, Xu Q, Yao X, Zhang G, Li Y, Xu C (2024) Diversity-guided particle swarm optimization with multi-level learning strategy. Swarm Evol Comput 86:101533. https:\/\/doi.org\/10.1016\/j.swevo.2024.101533","journal-title":"Swarm Evol Comput"},{"issue":"2","key":"2931_CR37","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s10846-022-01627-y","volume":"105","author":"Y Chen","year":"2022","unstructured":"Chen Y, Liang J, Wu Y, He B, Lin L, Wang Y (2022) Self-regulating and self-perception particle swarm optimization with mutation mechanism. J Intell Robot Syst 105(2):30. https:\/\/doi.org\/10.1007\/s10846-022-01627-y","journal-title":"J Intell Robot Syst"},{"key":"2931_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108780","volume":"178","author":"G Hu","year":"2024","unstructured":"Hu G, Zheng Y, Houssein EH, Wei G (2024) Drpso: a multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation. Comput Biol Med 178:108780. https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108780","journal-title":"Comput Biol Med"},{"issue":"5","key":"2931_CR39","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1109\/TEVC.2009.2014613","volume":"13","author":"J Zhang","year":"2009","unstructured":"Zhang J, Sanderson AC (2009) Jade: adaptive differential evolution with optional external archive. IEEE Trans Evol Comput 13(5):945\u2013958. https:\/\/doi.org\/10.1109\/TEVC.2009.2014613","journal-title":"IEEE Trans Evol Comput"},{"issue":"17","key":"2931_CR40","doi-asserted-by":"publisher","first-page":"10841","DOI":"10.1007\/s00521-021-06216-y","volume":"33","author":"HT Rauf","year":"2021","unstructured":"Rauf HT, Bangyal WHK, Lali MI (2021) An adaptive hybrid differential evolution algorithm for continuous optimization and classification problems. Neural Comput Appl 33(17):10841\u201310867. https:\/\/doi.org\/10.1007\/s00521-021-06216-y","journal-title":"Neural Comput Appl"},{"issue":"1","key":"2931_CR41","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/TEVC.2010.2087271","volume":"15","author":"Y Wang","year":"2011","unstructured":"Wang Y, Cai Z, Zhang Q (2011) Differential evolution with composite trial vector generation strategies and control parameters. IEEE Trans Evol Comput 15(1):55\u201366. https:\/\/doi.org\/10.1109\/TEVC.2010.2087271","journal-title":"IEEE Trans Evol Comput"},{"key":"2931_CR42","doi-asserted-by":"publisher","first-page":"3215","DOI":"10.1007\/s00500-017-2777-2","volume":"22","author":"AW Mohamed","year":"2018","unstructured":"Mohamed AW, Suganthan PN (2018) Real-parameter unconstrained optimization based on enhanced fitness-adaptive differential evolution algorithm with novel mutation. Soft Comput 22:3215\u20133235","journal-title":"Soft Comput"},{"key":"2931_CR43","doi-asserted-by":"publisher","unstructured":"Tanabe R, Fukunaga AS (2014) Improving the search performance of shade using linear population size reduction. In: 2014 IEEE congress on evolutionary computation (CEC). IEEE, pp 1658\u20131665. https:\/\/doi.org\/10.1109\/CEC.2014.6900380","DOI":"10.1109\/CEC.2014.6900380"},{"key":"2931_CR44","doi-asserted-by":"publisher","unstructured":"Brest J, Mau\u010dec MS, Bo\u0161kovi\u0107 B (2016) il-shade: improved l-shade algorithm for single objective real-parameter optimization. In: 2016 IEEE congress on evolutionary computation (CEC). IEEE, pp 1188\u20131195. https:\/\/doi.org\/10.1109\/CEC.2016.7743922","DOI":"10.1109\/CEC.2016.7743922"},{"key":"2931_CR45","doi-asserted-by":"publisher","unstructured":"Awad NH, Ali MZ, Suganthan PN (2016) An ensemble sinusoidal parameter adaptation incorporated with l-shade for solving cec2014 benchmark problems. In: 2016 IEEE congress on evolutionary computation (CEC). IEEE, pp 2958\u20132965. https:\/\/doi.org\/10.1109\/CEC.2016.7744163","DOI":"10.1109\/CEC.2016.7744163"},{"issue":"8","key":"2931_CR46","doi-asserted-by":"publisher","first-page":"4713","DOI":"10.1007\/s00500-022-07604-9","volume":"27","author":"A Jain","year":"2023","unstructured":"Jain A, Nagar S, Singh PK, Dhar J (2023) A hybrid learning-based genetic and grey-wolf optimizer for global optimization. Soft Comput 27(8):4713\u20134759. https:\/\/doi.org\/10.1007\/s00500-022-07604-9","journal-title":"Soft Comput"},{"issue":"3","key":"2931_CR47","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1016\/j.ptlrs.2021.12.004","volume":"7","author":"D Ocran","year":"2022","unstructured":"Ocran D, Ikiensikimama SS, Broni-Bediako E (2022) A compositional function hybridization of pso and gwo for solving well placement optimisation problem. Pet Res 7(3):401\u2013408. https:\/\/doi.org\/10.1016\/j.ptlrs.2021.12.004","journal-title":"Pet Res"},{"key":"2931_CR48","doi-asserted-by":"publisher","first-page":"3665","DOI":"10.1007\/s00366-020-01025-8","volume":"37","author":"Z Wang","year":"2021","unstructured":"Wang Z, Luo Q, Zhou Y (2021) Hybrid metaheuristic algorithm using butterfly and flower pollination base on mutualism mechanism for global optimization problems. Eng Comput 37:3665\u20133698","journal-title":"Eng Comput"},{"key":"2931_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2021.101352","volume":"51","author":"N Soni","year":"2021","unstructured":"Soni N, Sharma EK, Kapoor A (2021) Hybrid meta-heuristic algorithm based deep neural network for face recognition. J Comput Sci 51:101352. https:\/\/doi.org\/10.1016\/j.jocs.2021.101352","journal-title":"J Comput Sci"},{"issue":"6","key":"2931_CR50","doi-asserted-by":"publisher","first-page":"4049","DOI":"10.1007\/s11831-022-09730-x","volume":"29","author":"A Bouaouda","year":"2022","unstructured":"Bouaouda A, Sayouti Y (2022) Hybrid meta-heuristic algorithms for optimal sizing of hybrid renewable energy system: a review of the state-of-the-art. Arch Comput Methods Eng 29(6):4049\u20134083. https:\/\/doi.org\/10.1007\/s11831-022-09730-x","journal-title":"Arch Comput Methods Eng"},{"issue":"1","key":"2931_CR51","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1007\/s40747-022-00827-1","volume":"9","author":"D Cao","year":"2023","unstructured":"Cao D, Xu Y, Yang Z, Dong H, Li X (2023) An enhanced whale optimization algorithm with improved dynamic opposite learning and adaptive inertia weight strategy. Complex Intell Syst 9(1):767\u2013795. https:\/\/doi.org\/10.1007\/s40747-022-00827-1","journal-title":"Complex Intell Syst"},{"issue":"2","key":"2931_CR52","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/s13042-021-01326-4","volume":"13","author":"AG Hussien","year":"2022","unstructured":"Hussien AG, Amin M (2022) A self-adaptive harris hawks optimization algorithm with opposition-based learning and chaotic local search strategy for global optimization and feature selection. Int J Mach Learn Cybern 13(2):309\u2013336. https:\/\/doi.org\/10.1007\/s13042-021-01326-4","journal-title":"Int J Mach Learn Cybern"},{"issue":"18","key":"2931_CR53","doi-asserted-by":"publisher","first-page":"9349","DOI":"10.1007\/s00500-022-07280-9","volume":"26","author":"S Kassaymeh","year":"2022","unstructured":"Kassaymeh S, Abdullah S, Al-Betar MA, Alweshah M, Al-Laham M, Othman Z (2022) Self-adaptive salp swarm algorithm for optimization problems. Soft Comput 26(18):9349\u20139368. https:\/\/doi.org\/10.1007\/s00500-022-07280-9","journal-title":"Soft Comput"},{"key":"2931_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.saa.2024.124718","volume":"321","author":"S Qian","year":"2024","unstructured":"Qian S, Wang Z, Chao H, Xu Y, Wei Y, Gu G, Zhao X, Lu Z, Zhao J, Ren J et al (2024) Application of adaptive chaotic dung beetle optimization algorithm to near-infrared spectral model transfer. Spectrochim Acta Part A Mol Biomol Spectrosc 321:124718. https:\/\/doi.org\/10.1016\/j.saa.2024.124718","journal-title":"Spectrochim Acta Part A Mol Biomol Spectrosc"},{"issue":"7","key":"2931_CR55","doi-asserted-by":"publisher","first-page":"3293","DOI":"10.1007\/s00500-021-06665-6","volume":"26","author":"BH Abed-alguni","year":"2022","unstructured":"Abed-alguni BH, Paul D (2022) Island-based cuckoo search with elite opposition-based learning and multiple mutation methods for solving optimization problems. Soft Comput 26(7):3293\u20133312. https:\/\/doi.org\/10.1007\/s00500-021-06665-6","journal-title":"Soft Comput"},{"issue":"1","key":"2931_CR56","first-page":"57","volume":"17","author":"BH Abed-alguni","year":"2019","unstructured":"Abed-alguni BH (2019) Island-based cuckoo search with highly disruptive polynomial mutation. Int J Artif Intell 17(1):57\u201382","journal-title":"Int J Artif Intell"},{"issue":"15","key":"2931_CR57","doi-asserted-by":"publisher","first-page":"10167","DOI":"10.1007\/s00500-021-05939-3","volume":"25","author":"BH Abed-alguni","year":"2021","unstructured":"Abed-alguni BH, Alawad NA, Barhoush M, Hammad R (2021) Exploratory cuckoo search for solving single-objective optimization problems. Soft Comput 25(15):10167\u201310180. https:\/\/doi.org\/10.1007\/s00500-021-05939-3","journal-title":"Soft Comput"},{"issue":"15","key":"2931_CR58","doi-asserted-by":"publisher","first-page":"17217","DOI":"10.1007\/s10489-022-03269-x","volume":"52","author":"BH Abed-Alguni","year":"2022","unstructured":"Abed-Alguni BH, Paul D, Hammad R (2022) Improved salp swarm algorithm for solving single-objective continuous optimization problems. Appl Intell 52(15):17217\u201317236","journal-title":"Appl Intell"},{"issue":"1","key":"2931_CR59","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s40537-025-01080-2","volume":"12","author":"Z Wang","year":"2025","unstructured":"Wang Z, Zhang Y, Yu J, Gao Y, Zhao G, Houssein EH, Zhong R (2025) Multi-strategy enhanced marine predator algorithm: performance investigation and application in intrusion detection. J Big Data 12(1):38. https:\/\/doi.org\/10.1186\/s40537-025-01080-2","journal-title":"J Big Data"},{"key":"2931_CR60","doi-asserted-by":"crossref","unstructured":"Zhong R, Cao Y, Zhang E, Munetomo M (2024) Introducing competitive mechanism to differential evolution for numerical optimization, arXiv preprint arXiv:2406.05436","DOI":"10.1007\/978-3-031-85638-9_19"},{"key":"2931_CR61","doi-asserted-by":"publisher","unstructured":"Qin A, Suganthan P (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: 2005 IEEE Congress on Evolutionary Computation, vol\u00a02. pp 1785\u20131791. https:\/\/doi.org\/10.1109\/CEC.2005.1554904","DOI":"10.1109\/CEC.2005.1554904"},{"key":"2931_CR62","doi-asserted-by":"publisher","unstructured":"Tanabe R, Fukunaga A (2013) Evaluating the performance of shade on cec 2013 benchmark problems. In: 2013 IEEE congress on evolutionary computation. IEEE, pp 1952\u20131959. https:\/\/doi.org\/10.1109\/CEC.2013.6557798","DOI":"10.1109\/CEC.2013.6557798"},{"key":"2931_CR63","doi-asserted-by":"publisher","unstructured":"Brest J, Mau\u010dec MS, Bo\u0161kovi\u0107 B (2017) Single objective real-parameter optimization: algorithm jso. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 1311\u20131318. https:\/\/doi.org\/10.1109\/CEC.2017.7969456","DOI":"10.1109\/CEC.2017.7969456"},{"key":"2931_CR64","doi-asserted-by":"crossref","unstructured":"Hadi AA, Mohamed AW, Jambi KM (2021) Single-objective real-parameter optimization: enhanced lshade-spacma algorithm. Heuristics for optimization and learning. pp 103\u2013121","DOI":"10.1007\/978-3-030-58930-1_7"},{"key":"2931_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.101010","volume":"68","author":"A Kumar","year":"2022","unstructured":"Kumar A, Biswas PP, Suganthan PN (2022) Differential evolution with orthogonal array-based initialization and a novel selection strategy. Swarm Evol Comput 68:101010","journal-title":"Swarm Evol Comput"},{"key":"2931_CR66","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101283","volume":"78","author":"Y Li","year":"2023","unstructured":"Li Y, Han T, Zhou H, Wei Y, Wang Y, Tan M, Huang C (2023) Apsm-jso: a novel jso variant with an adaptive parameter selection mechanism and a new external archive updating mechanism. Swarm Evol Comput 78:101283. https:\/\/doi.org\/10.1016\/j.swevo.2023.101283","journal-title":"Swarm Evol Comput"},{"key":"2931_CR67","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2025.112777","volume":"171","author":"R Zhong","year":"2025","unstructured":"Zhong R, Hussien AG, Zhang S, Xu Y, Yu J (2025) Space mission trajectory optimization via competitive differential evolution with independent success history adaptation. Appl Soft Comput 171:112777. https:\/\/doi.org\/10.1016\/j.asoc.2025.112777","journal-title":"Appl Soft Comput"},{"key":"2931_CR68","doi-asserted-by":"publisher","unstructured":"Stanovov V, Akhmedova S, Semenkin E (2018) Lshade algorithm with rank-based selective pressure strategy for solving cec 2017 benchmark problems. In: 2018 IEEE congress on evolutionary computation (CEC). IEEE, pp 1\u20138. https:\/\/doi.org\/10.1109\/CEC.2018.8477977","DOI":"10.1109\/CEC.2018.8477977"},{"key":"2931_CR69","doi-asserted-by":"publisher","unstructured":"Yeh J-F, Chen T-Y, Chiang T-C (2019) Modified l-shade for single objective real-parameter optimization. In: 2019 IEEE congress on evolutionary computation (CEC). IEEE, pp 381\u2013386. https:\/\/doi.org\/10.1109\/CEC.2019.8789991","DOI":"10.1109\/CEC.2019.8789991"},{"key":"2931_CR70","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s13042-017-0711-7","volume":"10","author":"AW Mohamed","year":"2019","unstructured":"Mohamed AW, Mohamed AK (2019) Adaptive guided differential evolution algorithm with novel mutation for numerical optimization. Int J Mach Learn Cybern 10:253\u2013277. https:\/\/doi.org\/10.1007\/s13042-017-0711-7","journal-title":"Int J Mach Learn Cybern"},{"key":"2931_CR71","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106609","volume":"96","author":"F Zhao","year":"2020","unstructured":"Zhao F, Zhao L, Wang L, Song H (2020) A collaborative lshade algorithm with comprehensive learning mechanism. Appl Soft Comput 96:106609. https:\/\/doi.org\/10.1016\/j.asoc.2020.106609","journal-title":"Appl Soft Comput"},{"issue":"17","key":"2931_CR72","doi-asserted-by":"publisher","first-page":"10841","DOI":"10.1007\/s00521-021-06216-y","volume":"33","author":"HT Rauf","year":"2021","unstructured":"Rauf HT, Bangyal WHK, Lali MI (2021) An adaptive hybrid differential evolution algorithm for continuous optimization and classification problems. Neural Comput Appl 33(17):10841\u201310867. https:\/\/doi.org\/10.1007\/s00521-021-06216-y","journal-title":"Neural Comput Appl"},{"key":"2931_CR73","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106628","volume":"215","author":"TJ Choi","year":"2021","unstructured":"Choi TJ, Ahn CW (2021) An improved lshade-rsp algorithm with the cauchy perturbation: ilshade-rsp. Knowl Based Syst 215:106628. https:\/\/doi.org\/10.1016\/j.knosys.2020.106628","journal-title":"Knowl Based Syst"},{"key":"2931_CR74","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.100994","volume":"69","author":"W Yi","year":"2022","unstructured":"Yi W, Chen Y, Pei Z, Lu J (2022) Adaptive differential evolution with ensembling operators for continuous optimization problems. Swarm Evol Comput 69:100994. https:\/\/doi.org\/10.1016\/j.swevo.2021.100994","journal-title":"Swarm Evol Comput"},{"key":"2931_CR75","doi-asserted-by":"publisher","unstructured":"Zhong R, Cao Y, Zhang E, Munetomo M (2024) Introducing competitive mechanism to differential evolution for numerical optimization. In: World congress in computer science, computer engineering and applied computing. Springer, pp 251\u2013263. https:\/\/doi.org\/10.1007\/978-3-031-85638-9_19","DOI":"10.1007\/978-3-031-85638-9_19"},{"key":"2931_CR76","doi-asserted-by":"publisher","unstructured":"Van Thieu N (2024) Opfunu: an open-source python library for optimization benchmark functions. J Open Res Softw 12(1). https:\/\/doi.org\/10.5334\/jors.429","DOI":"10.5334\/jors.429"},{"key":"2931_CR77","doi-asserted-by":"publisher","unstructured":"Thieu NV (2023) Enoppy: a python library for engineering optimization problems. Zenodo. https:\/\/doi.org\/10.5281\/zenodo.7602003","DOI":"10.5281\/zenodo.7602003"},{"issue":"2","key":"2931_CR78","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1162\/106365601750190398","volume":"9","author":"N Hansen","year":"2001","unstructured":"Hansen N, Ostermeier A (2001) Completely derandomized self-adaptation in evolution strategies. Evol Comput 9(2):159\u2013195. https:\/\/doi.org\/10.1162\/106365601750190398","journal-title":"Evol Comput"},{"issue":"3","key":"2931_CR79","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1109\/TEVC.2005.857610","volume":"10","author":"JJ Liang","year":"2006","unstructured":"Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281\u2013295. https:\/\/doi.org\/10.1109\/TEVC.2005.857610","journal-title":"IEEE Trans Evol Comput"},{"key":"2931_CR80","doi-asserted-by":"publisher","unstructured":"Awad NH, Ali MZ (2017) Ensemble sinusoidal differential covariance matrix adaptation with euclidean neighborhood for solving cec2017 benchmark problems. In: 2017 IEEE congress on evolutionary computation (CEC). IEEE, pp 372\u201337. https:\/\/doi.org\/10.1109\/CEC.2017.7969336","DOI":"10.1109\/CEC.2017.7969336"},{"issue":"4","key":"2931_CR81","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1109\/TEVC.2022.3185665","volume":"27","author":"Z-J Wang","year":"2022","unstructured":"Wang Z-J, Jian J-R, Zhan Z-H, Li Y, Kwong S, Zhang J (2022) Gene targeting differential evolution: a simple and efficient method for large-scale optimization. IEEE Trans Evol Comput 27(4):964\u2013979. https:\/\/doi.org\/10.1109\/TEVC.2022.3185665","journal-title":"IEEE Trans Evol Comput"},{"key":"2931_CR82","doi-asserted-by":"publisher","first-page":"12239","DOI":"10.1007\/s10586-024-04587-0","volume":"27","author":"R Zhong","year":"2024","unstructured":"Zhong R, Yu J (2024) Dea2h2: differential evolution architecture based adaptive hyper-heuristic algorithm for continuous optimization. Clust Comput 27:12239\u201312266. https:\/\/doi.org\/10.1007\/s10586-024-04587-0","journal-title":"Clust Comput"},{"issue":"2","key":"2931_CR83","first-page":"65","volume":"6","author":"S Holm","year":"1979","unstructured":"Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65\u201370","journal-title":"Scand J Stat"},{"issue":"1","key":"2931_CR84","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1080\/00220973.1993.9943832","volume":"62","author":"DW Zimmerman","year":"1993","unstructured":"Zimmerman DW, Zumbo BD (1993) Relative power of the wilcoxon test, the friedman test, and repeated-measures anova on ranks. J Exp Educ 62(1):75\u201386. https:\/\/doi.org\/10.1080\/00220973.1993.9943832","journal-title":"J Exp Educ"},{"issue":"1","key":"2931_CR85","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"D Wolpert","year":"1997","unstructured":"Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382","journal-title":"IEEE Trans Evol Comput"},{"issue":"1","key":"2931_CR86","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1186\/s13007-024-01291-3","volume":"20","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Zhang D, Zhang Y, Cheng F, Zhao X, Wang M, Fan X (2024) Early detection of verticillium wilt in eggplant leaves by fusing five image channels: a deep learning approach. Plant Methods 20(1):173. https:\/\/doi.org\/10.1186\/s13007-024-01291-3","journal-title":"Plant Methods"},{"key":"2931_CR87","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097\u20131105"},{"key":"2931_CR88","first-page":"770","volume":"2016","author":"Kaiming He","year":"2016","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 770\u2013778","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"2931_CR89","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition, arXiv preprint arXiv:1409.1556"},{"key":"2931_CR90","doi-asserted-by":"publisher","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI conference on artificial intelligence, vol\u00a031. https:\/\/doi.org\/10.1609\/aaai.v31i1.11231","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"2931_CR91","doi-asserted-by":"crossref","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp 4510\u20134520","DOI":"10.1109\/CVPR.2018.00474"},{"key":"2931_CR92","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV). pp 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2931_CR93","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A convnet for the 2020s. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"2931_CR94","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) Pytorch: an imperative style, high-performance deep learning library. In: Wallach H, Larochelle H, Beygelzimer A, d\u2019Alch\u00e9-Buc F, Fox E, Garnett R (eds) Advances in neural information processing systems, vol\u00a032. Curran Associates, Inc"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02931-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02931-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02931-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T12:13:13Z","timestamp":1772280793000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02931-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,28]]},"references-count":94,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["2931"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02931-3","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,28]]},"assertion":[{"value":"15 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"155"}}