{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T17:56:35Z","timestamp":1773338195737,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T00:00:00Z","timestamp":1741824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["Nos. 62272418, 62102058"],"award-info":[{"award-number":["Nos. 62272418, 62102058"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100017577","name":"basic public welfare research program of Zhejiang Province","doi-asserted-by":"crossref","award":["No. LGG18E050011"],"award-info":[{"award-number":["No. LGG18E050011"]}],"id":[{"id":"10.13039\/501100017577","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Major Open Project of Key Laboratory for Advanced Design and Intelligent Computing of the Ministry of Education","award":["ADIC2023ZD001"],"award-info":[{"award-number":["ADIC2023ZD001"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Membr Comput"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1007\/s41965-025-00183-2","type":"journal-article","created":{"date-parts":[[2025,3,13]],"date-time":"2025-03-13T07:15:21Z","timestamp":1741850121000},"page":"66-88","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Evolutionary state estimate-based adaptive multi-objective particle swarm optimization"],"prefix":"10.1007","volume":"8","author":[{"given":"Wenjie","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Donglin","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changjun","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shi","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,13]]},"reference":[{"key":"183_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.120483","volume":"667","author":"QY Fu","year":"2024","unstructured":"Fu, Q. Y., Li, Q., Li, X. B., Wang, H., Xie, J. P., & Wang, Q. (2024). MOFS-REPLS: A large-scale multi-objective feature selection algorithm based on real-valued encoding and preference leadership strategy. Information Sciences, 667, 120483. https:\/\/doi.org\/10.1016\/j.ins.2024.120483","journal-title":"Information Sciences"},{"key":"183_CR2","doi-asserted-by":"publisher","unstructured":"Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN'95 - International Conference on Neural Networks, Perth, WA, Australia (vol. 4, pp. 1942\u20131948). https:\/\/doi.org\/10.1109\/ICNN.1995.488968.","DOI":"10.1109\/ICNN.1995.488968"},{"key":"183_CR3","doi-asserted-by":"publisher","unstructured":"Coello Coello, C. A., & Lechuga, M. S. (2002). MOPSO: A proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), Honolulu, HI, USA (vol. 2, pp. 1051\u20131056). https:\/\/doi.org\/10.1109\/CEC.2002.1004388.","DOI":"10.1109\/CEC.2002.1004388"},{"key":"183_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101737","volume":"91","author":"DL Zhu","year":"2024","unstructured":"Zhu, D. L., Shen, J. Y., Zhang, Y. M., Li, W. J., Zhu, X. Y., Zhou, C. J., Cheng, S., & Yao, Y. L. (2024). Multi-strategy particle swarm optimization with adaptive forgetting for base station layout. Swarm and Evolutionary Computation, 91, 101737. https:\/\/doi.org\/10.1016\/j.swevo.2024.101737","journal-title":"Swarm and Evolutionary Computation"},{"issue":"2","key":"183_CR5","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1080\/15325000903273379","volume":"38","author":"PK Roy","year":"2009","unstructured":"Roy, P. K., Ghoshal, S. P., & Thakur, S. S. (2009). Biogeography-based optimization for economic load dispatch problems. Electric Power Components and Systems, 38(2), 166\u2013181. https:\/\/doi.org\/10.1080\/15325000903273379","journal-title":"Electric Power Components and Systems"},{"key":"183_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111409","volume":"286","author":"Y Xu","year":"2024","unstructured":"Xu, Y., Li, X. B., Meng, X. P., & Zhang, W. (2024). An iterated greedy heuristic for collaborative human-UAV search of missing tourists. Knowledge-Based Systems, 286, 111409. https:\/\/doi.org\/10.1016\/j.knosys.2024.111409","journal-title":"Knowledge-Based Systems"},{"key":"183_CR7","doi-asserted-by":"publisher","first-page":"5357","DOI":"10.3390\/en6105357","volume":"6","author":"JH Chen","year":"2013","unstructured":"Chen, J. H., Wu, W. C., Zhang, B. M., Wang, B., & Guo, Q. L. (2013). A spinning reserve allocation method for power generation dispatch accommodating L-arge-scale wind power integration. Energies, 6, 5357\u20135381. https:\/\/doi.org\/10.3390\/en6105357","journal-title":"Energies"},{"key":"183_CR8","doi-asserted-by":"publisher","first-page":"1530","DOI":"10.3390\/en15041530","volume":"15","author":"I Meidute-Kavaliauskiene","year":"2022","unstructured":"Meidute-Kavaliauskiene, I., S\u00fct\u00fctemiz, N., Y\u0131ld\u0131r\u0131m, F., Ghorbani, S., & \u010cin\u010dikait\u0117, R. (2022). Optimizing multi cross-docking systems with a multi-objective green location routing problem considering carbon emission and energy consumption. Energies, 15, 1530. https:\/\/doi.org\/10.3390\/en15041530","journal-title":"Energies"},{"key":"183_CR9","doi-asserted-by":"publisher","first-page":"04018010","DOI":"10.1061\/(ASCE)UP.1943-5444.0000425","volume":"144","author":"FX Li","year":"2018","unstructured":"Li, F. X., Gong, Y. A., Cai, L. Y., Sun, C. Y., Chen, Y. M., Liu, Y. X., & Jiang, P. H. (2018). Sustainable land-use allocation: A multiobjective particle swarm optimization model and application in Changzhou, China. Journal of Urban Planning and Development, 144, 04018010. https:\/\/doi.org\/10.1061\/(ASCE)UP.1943-5444.0000425","journal-title":"Journal of Urban Planning and Development"},{"issue":"9","key":"183_CR10","doi-asserted-by":"publisher","first-page":"4408","DOI":"10.3390\/ijms22094408","volume":"22","author":"CP Zhou","year":"2021","unstructured":"Zhou, C. P., Wang, D., Pan, X. Y., & Shen, H. B. (2021). Protein structure refinement using multi-objective particle swarm optimization with decomposition strategy. International Journal of Molecular Sciences, 22(9), 4408. https:\/\/doi.org\/10.3390\/ijms22094408","journal-title":"International Journal of Molecular Sciences"},{"key":"183_CR11","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/s10596-009-9142-1","volume":"14","author":"JE Onwunalu","year":"2010","unstructured":"Onwunalu, J. E., & Durlofsky, L. J. (2010). Application of a particle swarm optimization algorithm for determining optimum well location and type. Computational Geosciences, 14, 183\u2013198. https:\/\/doi.org\/10.1007\/s10596-009-9142-1","journal-title":"Computational Geosciences"},{"issue":"5","key":"183_CR12","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1109\/TEVC.2017.2754271","volume":"22","author":"CT Yue","year":"2018","unstructured":"Yue, C. T., Qu, B. Y., & Liang, J. (2018). A multiobjective particle swarm optimizer using ring topology for solving multimodal multiobjective problems. IEEE Transactions on Evolutionary Computation, 22(5), 805\u2013817. https:\/\/doi.org\/10.1109\/TEVC.2017.2754271","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"183_CR13","doi-asserted-by":"publisher","first-page":"1066","DOI":"10.1016\/j.ins.2022.07.165","volume":"612","author":"JH Yang","year":"2022","unstructured":"Yang, J. H., Yu, J. H., & Huang, C. (2022). Adaptive multistrategy ensemble particle swarm optimization with signal-to-noise ratio distance metric. Information Sciences, 612, 1066\u20131094. https:\/\/doi.org\/10.1016\/j.ins.2022.07.165","journal-title":"Information Sciences"},{"key":"183_CR14","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/1626457","volume":"2021","author":"KG Zou","year":"2021","unstructured":"Zou, K. G., Liu, Y. M., Wang, S. H., Li, N. N., & Wu, Y. W. (2021). A multiobjective particle swarm optimization algorithm based on grid technique and multistrategy. Journal of Mathematics, 2021, 1626457. https:\/\/doi.org\/10.1155\/2021\/1626457","journal-title":"Journal of Mathematics"},{"key":"183_CR15","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.ins.2017.10.037","volume":"427","author":"XY Zhang","year":"2018","unstructured":"Zhang, X. Y., Zheng, X. T., Cheng, R., Qiu, J. F., & Jin, Y. C. (2018). A competitive mechanism based multi-objective particle swarm optimizer with fast convergence. Information Sciences, 427, 63\u201376. https:\/\/doi.org\/10.1016\/j.ins.2017.10.037","journal-title":"Information Sciences"},{"issue":"1","key":"183_CR16","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1007\/s40747-021-00362-5","volume":"8","author":"JQ Lin","year":"2022","unstructured":"Lin, J. Q., He, C., & Cheng, R. (2022). Adaptive dropout for high-dimensional expensive multiobjective optimization. Complex & Intelligent Systems, 8(1), 271\u2013285. https:\/\/doi.org\/10.1007\/s40747-021-00362-5","journal-title":"Complex & Intelligent Systems"},{"key":"183_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2024.3512795","author":"HR Gu","year":"2024","unstructured":"Gu, H. R., Wang, H. D., Mei, Y., Zhang, M. G., & Jin, Y. C. (2024). Surrogate-assisted neighborhood search with only a few weight vectors for expensive large-scale multiobjective binary optimization. IEEE Transactions on Evolutionary Computation. https:\/\/doi.org\/10.1109\/TEVC.2024.3512795","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"183_CR18","doi-asserted-by":"publisher","first-page":"732","DOI":"10.1016\/j.ejor.2015.06.071","volume":"247","author":"QZ Lin","year":"2015","unstructured":"Lin, Q. Z., Li, J. Q., Du, Z. H., Chen, J. Y., & Ming, Z. (2015). A novel multi-objective particle swarm optimization with multiple search strategies. European Journal of Operational Research, 247(3), 732\u2013744. https:\/\/doi.org\/10.1016\/j.ejor.2015.06.071","journal-title":"European Journal of Operational Research"},{"key":"183_CR19","doi-asserted-by":"publisher","unstructured":"Nebro, A. J., Durillo, J. J., Garcia-Nieto, J., Coello Coello, C. A., Luna, F., & Alba, E. (2009). SMPSO: A new PSO-based metaheuristic for multi-objective optimization. In 2009 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making(MCDM), Nashville, TN, USA (pp. 66\u201373). https:\/\/doi.org\/10.1109\/MCDM.2009.4938830","DOI":"10.1109\/MCDM.2009.4938830"},{"key":"183_CR20","doi-asserted-by":"publisher","first-page":"11754","DOI":"10.1038\/s41598-023-38529-4","volume":"13","author":"QL Ye","year":"2023","unstructured":"Ye, Q. L., Wang, Z., Zhao, Y. W., Dai, R., Wu, F., & Yu, M. (2023). A clustering-based competitive particle swarm optimization with grid ranking for multi-objective optimization problems. Science and Reports, 13, 11754. https:\/\/doi.org\/10.1038\/s41598-023-38529-4","journal-title":"Science and Reports"},{"issue":"Suppl 1","key":"183_CR21","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s00521-018-3640-9","volume":"31","author":"W Yu","year":"2019","unstructured":"Yu, W., Li, S. J., Tang, X. Y., & Wang, K. (2019). An efficient top-k ranking method for service selection based on \u03b5-ADMOPSO algorithm. Neural Computing and Applications, 31(Suppl 1), 77\u201392. https:\/\/doi.org\/10.1007\/s00521-018-3640-9","journal-title":"Neural Computing and Applications"},{"issue":"5","key":"183_CR22","doi-asserted-by":"publisher","first-page":"645","DOI":"10.1109\/TEVC.2015.2504730","volume":"20","author":"MQ Li","year":"2016","unstructured":"Li, M. Q., Yang, S. X., & Liu, X. H. (2016). Pareto or non-Pareto: Bi-criterion evolution in multiobjective optimization. IEEE Transactions on Evolutionary Computation, 20(5), 645\u2013665. https:\/\/doi.org\/10.1109\/TEVC.2015.2504730","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"183_CR23","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1016\/j.physa.2018.08.077","volume":"513","author":"F Zou","year":"2019","unstructured":"Zou, F., Chen, D. B., Huang, D. S., Lu, R. Q., & Wang, X. D. (2019). Inverse modelling-based multi-objective evolutionary algorithm with decomposition for community detection in complex networks. Physica A: Statistical Mechanics and its Applications, 513, 662\u2013674. https:\/\/doi.org\/10.1016\/j.physa.2018.08.077","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"183_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2021.100987","volume":"69","author":"JH Zheng","year":"2022","unstructured":"Zheng, J. H., Zhang, Z. Y., Zou, J., Yang, S. X., Ou, J. W., & Hu, Y. R. (2022). A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution. Swarm and Evolutionary Computation, 69, 100987. https:\/\/doi.org\/10.1016\/j.swevo.2021.100987","journal-title":"Swarm and Evolutionary Computation"},{"key":"183_CR25","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s12293-021-00351-8","volume":"14","author":"ZS Song","year":"2022","unstructured":"Song, Z. S., Wang, H. D., & Xu, H. B. (2022). A framework for expensive many-objective optimization with Pareto-based bi-indicator infill sampling criterion. Memetic Computing, 14, 179\u2013191. https:\/\/doi.org\/10.1007\/s12293-021-00351-8","journal-title":"Memetic Computing"},{"issue":"1","key":"183_CR26","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1109\/TEVC.2016.2631279","volume":"22","author":"QZ Lin","year":"2018","unstructured":"Lin, Q. Z., Liu, S. B., Zhu, Q. L., Tang, C. Y., Song, R. Z., Chen, J. Y., Coello Coello, C. A., Wong, K. C., & Zhang, J. (2018). Particle swarm optimization with abalanceable fitness estimation for many-objective optimization problems. IEEE Transactions on Evolutionary Computation, 22(1), 32\u201346. https:\/\/doi.org\/10.1109\/TEVC.2016.2631279","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"2","key":"183_CR27","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182\u2013197. https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"2","key":"183_CR28","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/TEVC.2008.925798","volume":"13","author":"H Li","year":"2009","unstructured":"Li, H., & Zhang, Q. F. (2009). Multiobjective optimization problems with complicated Pareto sets, MOEA\/D and NSGA-II. IEEE Transactions on Evolutionary Computation, 13(2), 284\u2013302. https:\/\/doi.org\/10.1109\/TEVC.2008.925798","journal-title":"IEEE Transactions on Evolutionary Computation"},{"key":"183_CR29","doi-asserted-by":"publisher","DOI":"10.1145\/3512290.3528732","author":"A Panichella","year":"2022","unstructured":"Panichella, A. (2022). An improved Pareto front modeling algorithm for large-scale many-objective optimization. Proceedings of the Genetic and Evolutionary Computation Conference. https:\/\/doi.org\/10.1145\/3512290.3528732","journal-title":"Proceedings of the Genetic and Evolutionary Computation Conference"},{"issue":"4","key":"183_CR30","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1109\/TEVC.2013.2281534","volume":"18","author":"H Jain","year":"2014","unstructured":"Jain, H., & Deb, K. (2014). An evolutionary many-objective optimization algorithm using reference-point based nondominated sorting approach, part II: Handling constraints and extending to an adaptive approach. IEEE Transactions on Evolutionary Computation, 18(4), 602\u2013622. https:\/\/doi.org\/10.1109\/TEVC.2013.2281534","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"7","key":"183_CR31","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1109\/TSMC.2017.2654301","volume":"48","author":"M Elarbi","year":"2018","unstructured":"Elarbi, M., Bechikh, S., Gupta, A., Ben Said, L., & Ong, Y.-S. (2018). A new decomposition-based NSGA-II for many-objective optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 48(7), 1191\u20131210. https:\/\/doi.org\/10.1109\/TSMC.2017.2654301","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"issue":"10","key":"183_CR32","doi-asserted-by":"publisher","first-page":"6222","DOI":"10.1109\/TSMC.2022.3143657","volume":"52","author":"F Ming","year":"2022","unstructured":"Ming, F., Gong, W. Y., & Wang, L. (2022). A two-stage evolutionary algorithm with balanced convergence and diversity for many-objective optimization. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(10), 6222\u20136234. https:\/\/doi.org\/10.1109\/TSMC.2022.3143657","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics: Systems"},{"issue":"3","key":"183_CR33","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1109\/TEVC.2016.2592479","volume":"21","author":"SY Jiang","year":"2017","unstructured":"Jiang, S. Y., & Yang, S. X. (2017). A strength Pareto evolutionary algorithm based on reference direction for multiobjective and many-objective optimization. IEEE Transactions on Evolutionary Computation, 21(3), 329\u2013346. https:\/\/doi.org\/10.1109\/TEVC.2016.2592479","journal-title":"IEEE Transactions on Evolutionary Computation"},{"issue":"3","key":"183_CR34","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.artmed.2014.12.007","volume":"63","author":"J Ye","year":"2015","unstructured":"Ye, J. (2015). Improved cosine similarity measures of simplified neutrosophic sets for medical diagnoses. Artificial Intelligence in Medicine, 63(3), 171\u2013179. https:\/\/doi.org\/10.1016\/j.artmed.2014.12.007","journal-title":"Artificial Intelligence in Medicine"},{"key":"183_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.128421","volume":"283","author":"Q Zhang","year":"2023","unstructured":"Zhang, Q., Zou, D. X., & Duan, N. (2023). An improved differential evolution using self-adaptable cosine similarity for economic emission dispatch. Energy, 283, 128421. https:\/\/doi.org\/10.1016\/j.energy.2023.128421","journal-title":"Energy"},{"key":"183_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101274","volume":"78","author":"W Li","year":"2023","unstructured":"Li, W., Liang, P., Sun, B., Sun, Y. F., & Huang, Y. (2023). Reinforcement learning-based particle swarm optimization with neighborhood differential mutation strategy. Swarm and Evolutionary Computation, 78, 101274. https:\/\/doi.org\/10.1016\/j.swevo.2023.101274","journal-title":"Swarm and Evolutionary Computation"},{"issue":"2","key":"183_CR37","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1109\/TCYB.2019.2925015","volume":"51","author":"WB Liu","year":"2021","unstructured":"Liu, W. B., Wang, Z. D., Yuan, Y., Zeng, N. Y., Hone, K., & Liu, X. H. (2021). A Novel Sigmoid-function-based adaptive weighted particle swarm optimizer. IEEE Transactions on Cybernetics, 51(2), 1085\u20131093. https:\/\/doi.org\/10.1109\/TCYB.2019.2925015","journal-title":"IEEE Transactions on Cybernetics"},{"key":"183_CR38","doi-asserted-by":"publisher","first-page":"10007","DOI":"10.1007\/s00521-019-04527-9","volume":"32","author":"Y Xu","year":"2020","unstructured":"Xu, Y., & Pi, D. C. (2020). A reinforcement learning-based communication topology in particle swarm optimization. Neural Computing and Applications, 32, 10007\u201310032. https:\/\/doi.org\/10.1007\/s00521-019-04527-9","journal-title":"Neural Computing and Applications"},{"issue":"6","key":"183_CR39","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1177\/1748006X19852814","volume":"233","author":"MM Arezki","year":"2019","unstructured":"Arezki, M. M., & Enrico, Z. (2019). An adaptive particle swarm optimization method for multi-objective system reliability optimization. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability., 233(6), 990\u20131001. https:\/\/doi.org\/10.1177\/1748006X19852814","journal-title":"Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability."},{"key":"183_CR40","doi-asserted-by":"publisher","unstructured":"Gou, Q. C., & Li, Q. K. (2020). Task assignment based on PSO algorithm based on Logistic function inertia weight adaptive adjustment. In 2020 3rd International Conference on Unmanned Systems (ICUS), Harbin, China (pp. 825\u2013829). https:\/\/doi.org\/10.1109\/ICUS50048.2020.9274932.","DOI":"10.1109\/ICUS50048.2020.9274932"},{"key":"183_CR41","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1016\/j.ins.2020.06.027","volume":"540","author":"SL Wang","year":"2020","unstructured":"Wang, S. L., Liu, G. Y., Gao, M., Cao, S. L., Guo, A. Z., & Wang, J. C. (2020). Heterogeneous comprehensive learning and dynamic multi-swarm particle swarm optimizer with two mutation operators. Information Sciences, 540, 175\u2013201. https:\/\/doi.org\/10.1016\/j.ins.2020.06.027","journal-title":"Information Sciences"},{"key":"183_CR42","doi-asserted-by":"publisher","unstructured":"Song, Z. S., Wang, H. D., & Xu, H. B. (2021). Pareto-based bi-indicator infill sampling criterion for expensive multiobjective optimization. In: Evolutionary multi-criterion optimization: 11th international conference, EMO 2021, Shenzhen, China, March 28\u201331, 2021, Proceedings 11 (pp. 531\u2013542). Springer. https:\/\/doi.org\/10.1007\/978-3-030-72062-9_42.","DOI":"10.1007\/978-3-030-72062-9_42"},{"key":"183_CR43","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.swevo.2015.05.002","volume":"24","author":"N Lynn","year":"2015","unstructured":"Lynn, N., & Suganthan, P. N. (2015). Heterogeneous comprehensive learning particle swarm optimization with enhanced exploration and exploitation. Swarm and Evolutionary Computation, 24, 11\u201324. https:\/\/doi.org\/10.1016\/j.swevo.2015.05.002","journal-title":"Swarm and Evolutionary Computation"},{"key":"183_CR44","doi-asserted-by":"publisher","unstructured":"Kukkonen, S., & Deb, K. (2006). Improved pruning of non-dominated solutions based on crowding distance for bi-objective optimization problems. In 2006 IEEE International Conference on Evolutionary Computation, Vancouver, BC, Canada (pp. 1179\u20131186). https:\/\/doi.org\/10.1109\/CEC.2006.1688443.","DOI":"10.1109\/CEC.2006.1688443"},{"key":"183_CR45","doi-asserted-by":"publisher","unstructured":"Yu, F. R., Fu, X. L., Li, H. H., & Dong, G. F. (2016). Improved Roulette wheel selection-based genetic algorithm for TSP. In 2016 International Conference on Network and Information Systems for Computers (ICNISC), Wuhan, China (pp. 151\u2013154). https:\/\/doi.org\/10.1109\/ICNISC.2016.041.","DOI":"10.1109\/ICNISC.2016.041"},{"issue":"2","key":"183_CR46","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1162\/106365600568202","volume":"8","author":"E Zitzler","year":"2000","unstructured":"Zitzler, E., Deb, K., & Thiele, L. (2000). Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary Computation, 8(2), 173\u2013195. https:\/\/doi.org\/10.1162\/106365600568202","journal-title":"Evolutionary Computation"},{"key":"183_CR47","volume-title":"Evolutionary multiobjective optimization. Advanced information and knowledge processing","author":"K Deb","year":"2005","unstructured":"Deb, K., Thiele, L., Laumanns, M., & Zitzler, M. E. (2005). Scalable test problems for evolutionary multiobjective optimization. In A. Abraham, L. Jain, & R. Goldberg (Eds.), Evolutionary multiobjective optimization. Advanced information and knowledge processing. London: Springer."},{"key":"183_CR48","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/s10710-005-6164-x","volume":"6","author":"CA Coello Coello","year":"2005","unstructured":"Coello Coello, C. A., & Cort\u00e9s, N. C. (2005). Solving multiobjective optimization problems using an artificial immune system. Genetic Programming and Evolvable Machines, 6, 163\u2013190. https:\/\/doi.org\/10.1007\/s10710-005-6164-x","journal-title":"Genetic Programming and Evolvable Machines"},{"issue":"4","key":"183_CR49","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1109\/MCI.2017.2742868","volume":"12","author":"Y Tian","year":"2017","unstructured":"Tian, Y., Cheng, R., Zhang, X. Y., & Jin, Y. C. (2017). PlatEMO: A MATLAB platform for evolutionary multi-objective optimization [Educational Forum]. IEEE Computational Intelligence Magazine, 12(4), 73\u201387. https:\/\/doi.org\/10.1109\/MCI.2017.2742868","journal-title":"IEEE Computational Intelligence Magazine"}],"container-title":["Journal of Membrane Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41965-025-00183-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41965-025-00183-2","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41965-025-00183-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T07:24:36Z","timestamp":1773300276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s41965-025-00183-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,13]]},"references-count":49,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["183"],"URL":"https:\/\/doi.org\/10.1007\/s41965-025-00183-2","relation":{},"ISSN":["2523-8906","2523-8914"],"issn-type":[{"value":"2523-8906","type":"print"},{"value":"2523-8914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,13]]},"assertion":[{"value":"14 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2025","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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}