{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:14:15Z","timestamp":1779203655538,"version":"3.51.4"},"reference-count":39,"publisher":"Fuji Technology Press Ltd.","issue":"3","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62333019"],"award-info":[{"award-number":["62333019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2023TQ0337"],"award-info":[{"award-number":["2023TQ0337"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2024M763061"],"award-info":[{"award-number":["2024M763061"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities, China University of Geosciences"},{"DOI":"10.13039\/501100013314","name":"Higher Education Discipline Innovation Project","doi-asserted-by":"publisher","award":["B17040"],"award-info":[{"award-number":["B17040"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JACIII","J. Adv. Comput. Intell. Intell. Inform."],"published-print":{"date-parts":[[2026,5,20]]},"abstract":"<jats:p>With declining hydrocarbon reserves, optimizing directional wellbore trajectory planning is crucial for accessing complex reservoirs while managing cost and risk. Multi-segment composite trajectories have been widely used in complex formations, but their high-dimensional nature challenges the efficiency of gradient-free multi-objective optimization methods. This paper proposes a conjugate gradient-assisted multi-objective algorithm to accelerate the optimization process. A dual-population update strategy integrates gradient-based search with constraint satisfaction, while adaptive step size and hybrid search mitigate multi-objective gradient conflicts. Gradients for formation-related objectives are approximated via finite differences. The algorithm is validated on a five-segment B\u00e9zier trajectory for an extended-reach horizontal wellbore, demonstrating superior normalized hypervolume, convergence, and diversity compared to gradient-based and evolutionary algorithms. Ablation studies verify the effectiveness of the hybrid strategy. The selected trajectory achieves an optimal balance among wellbore length, curvature, and stability under all constraints. To our knowledge, this is the first application of gradient-based multi-objective optimization to wellbore trajectory planning, offering a promising pathway toward real-time drilling trajectory design with enhanced efficiency and robustness.<\/jats:p>","DOI":"10.20965\/jaciii.2026.p0761","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:02:09Z","timestamp":1779202929000},"page":"761-780","source":"Crossref","is-referenced-by-count":0,"title":["A Conjugate Gradient-Accelerated Constrained Multi-Objective Optimizer for High-Dimensional Problems with Application to Wellbore Trajectory Planning"],"prefix":"10.20965","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8075-8556","authenticated-orcid":true,"given":"Jiafeng","family":"Xu","sequence":"first","affiliation":[{"name":"School of Automation, China University of Geosciences, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9924-6833","authenticated-orcid":true,"given":"Xin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"School of Future Technology, China University of Geosciences, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1148-0666","authenticated-orcid":true,"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Automation, China University of Geosciences, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"8550","published-online":{"date-parts":[[2026,5,20]]},"reference":[{"key":"key-10.20965\/jaciii.2026.p0761-1","doi-asserted-by":"crossref","unstructured":"D. Zhang, M. Wu, C. Lu, L. Chen, W. Cao, and J. Hu, \u201cIntelligent Compensating Method for MPC-Based Deviation Correction with Stratum Uncertainty in Vertical Drilling Process,\u201d J. Adv. Comput. Intell. Inform., Vol.25, No.1, pp. 23-30, 2021. https:\/\/doi.org\/10.20965\/jaciii.2021.p0023","DOI":"10.20965\/jaciii.2021.p0023"},{"key":"key-10.20965\/jaciii.2026.p0761-2","doi-asserted-by":"crossref","unstructured":"H. Wei, N. Yao, H. Tian, Y. Yao, J. Zhang, and H. Li, \u201cA Predicting Model for Near-Horizontal Directional Drilling Path Based on BP Neural Network in Underground Coal Mine,\u201d J. Adv. Comput. Intell. Inform., Vol.26, No.3, pp. 279-288, 2022. https:\/\/doi.org\/10.20965\/jaciii.2022.p0279","DOI":"10.20965\/jaciii.2022.p0279"},{"key":"key-10.20965\/jaciii.2026.p0761-3","doi-asserted-by":"crossref","unstructured":"W. Li, M. Wu, S. Chen, L. Mu, C. Lu, and L. Chen, \u201cDesign of an Intelligent Control System for Compound Directional Drilling in Underground Coal Mines,\u201d J. Adv. Comput. Intell. Inform., Vol.28, No.4, pp. 1052-1062, 2024. https:\/\/doi.org\/10.20965\/jaciii.2024.p1052","DOI":"10.20965\/jaciii.2024.p1052"},{"key":"key-10.20965\/jaciii.2026.p0761-4","doi-asserted-by":"crossref","unstructured":"D. Chen, K. Mao, Z. Ye, W. Li, W. Yan, and H. Wang, \u201cAn artificial intelligent well trajectory design method combining both geological and engineering objectives,\u201d Geoenergy Sci. Eng., Vol.236, Article No.212736, 2024. https:\/\/doi.org\/10.1016\/j.geoen.2024.212736","DOI":"10.1016\/j.geoen.2024.212736"},{"key":"key-10.20965\/jaciii.2026.p0761-5","doi-asserted-by":"crossref","unstructured":"Z. Cai, X. Lai, M. Wu, C. Lu, and L. Chen, \u201cTrajectory Azimuth Control Based on Equivalent Input Disturbance Approach for Directional Drilling Process,\u201d J. Adv. Comput. Intell. Inform., Vol.25, No.1, pp. 31-39, 2021. https:\/\/doi.org\/10.20965\/jaciii.2021.p0031","DOI":"10.20965\/jaciii.2021.p0031"},{"key":"key-10.20965\/jaciii.2026.p0761-6","doi-asserted-by":"crossref","unstructured":"Z. Cai and G. Hu, \u201cStability Analysis of Drilling Inclination System with Time-Varying Delay via Free-Matrix-Based Lyapunov\u2013Krasovskii Functional,\u201d J. Adv. Comput. Intell. Inform., Vol.25, No.6, pp. 1031-1038, 2021. https:\/\/doi.org\/10.20965\/jaciii.2021.p1031","DOI":"10.20965\/jaciii.2021.p1031"},{"key":"key-10.20965\/jaciii.2026.p0761-7","doi-asserted-by":"crossref","unstructured":"S. Vlemmix, G. J. P. Joosten, D. R. Brouwer, and J. D. Jansen, \u201cAdjoint-Based Well Trajectory Optimization in a Thin Oil Rim,\u201d EUROPEC\/EAGE Conf. and Exhib. (09EURO), Article No.SPE-121891-MS, 2009. https:\/\/doi.org\/10.2118\/121891-MS","DOI":"10.2118\/121891-MS"},{"key":"key-10.20965\/jaciii.2026.p0761-8","doi-asserted-by":"crossref","unstructured":"R. G. Hanea, P. Casanova, F. H. Wilschut, and R. M. Fonseca, \u201cWell Trajectory Optimization Constrained to Structural Uncertainties,\u201d SPE Res. Simul. Conf., Article No.SPE-182680-MS, 2017. https:\/\/doi.org\/10.2118\/182680-MS","DOI":"10.2118\/182680-MS"},{"key":"key-10.20965\/jaciii.2026.p0761-9","doi-asserted-by":"crossref","unstructured":"E. G. D. Barros, A. Chitu, and O. Leeuwenburgh, \u201cEnsemble-based well trajectory and drilling schedule optimization-application to the Olympus benchmark model,\u201d Comput. Geosci., Vol.24, No.6, pp. 2095-2109, 2020. https:\/\/doi.org\/10.1007\/s10596-020-09952-7","DOI":"10.1007\/s10596-020-09952-7"},{"key":"key-10.20965\/jaciii.2026.p0761-10","doi-asserted-by":"crossref","unstructured":"X. Wu and K. Zhang, \u201cThree-dimensional trajectory design for horizontal well based on optimal switching algorithms,\u201d ISA Trans., Vol.58, pp. 348-356, 2015. https:\/\/doi.org\/10.1016\/j.isatra.2015.04.002","DOI":"10.1016\/j.isatra.2015.04.002"},{"key":"key-10.20965\/jaciii.2026.p0761-11","doi-asserted-by":"crossref","unstructured":"Y. Guo and E. Feng, \u201cNonlinear dynamical systems of trajectory design for 3D horizontal well and their optimal controls,\u201d J. Comput. Appl. Math., Vol.212, Issue 2, pp. 179-186, 2008. https:\/\/doi.org\/10.1016\/j.cam.2006.11.034","DOI":"10.1016\/j.cam.2006.11.034"},{"key":"key-10.20965\/jaciii.2026.p0761-12","doi-asserted-by":"crossref","unstructured":"R. Lu and A. C. Reynolds, \u201cJoint Optimization of Well Locations, Types, Drilling Order, and Controls Given a Set of Potential Drilling Paths,\u201d SPE J., Vol.25, Issue 03, pp. 1285-1306, 2020. https:\/\/doi.org\/10.2118\/193885-PA","DOI":"10.2118\/193885-PA"},{"key":"key-10.20965\/jaciii.2026.p0761-13","doi-asserted-by":"crossref","unstructured":"U. Singh, R. Pathan, A. D. Joshi, A. Cav\u00e9, C. Fouchard, and A. Baume, \u201cAccelerated Design of Sidetrack and Deepening Well Trajectories,\u201d SPE J., Vol.29, Issue 04, pp. 1862-1872, 2024. https:\/\/doi.org\/10.2118\/218395-PA","DOI":"10.2118\/218395-PA"},{"key":"key-10.20965\/jaciii.2026.p0761-14","doi-asserted-by":"crossref","unstructured":"W. Li, X. Yang, C. Lu, Q. Li, P. Fang, X. Wu, H. Huang, H. Fan, N. Yao, H. Tian, and M. Wu, \u201cModeling and optimization of trajectory deviation for compound directional drilling in coal mines,\u201d Neurocomputing, Vol.618, Article No.129029, 2025. https:\/\/doi.org\/10.1016\/j.neucom.2024.129029","DOI":"10.1016\/j.neucom.2024.129029"},{"key":"key-10.20965\/jaciii.2026.p0761-15","doi-asserted-by":"crossref","unstructured":"J. Shu, G. Han, Z. Yue, L. Cheng, Y. Dong, and X. Liang, \u201cOptimizing Well Trajectories for Enhanced Oil Production in Naturally Fractured Reservoirs: Integrating Particle Swarm Optimization with an Innovative Semi-Analytical Model Framework,\u201d SPE J., Vol.30, Issue 02, pp. 957-975, 2025. https:\/\/doi.org\/10.2118\/223939-PA","DOI":"10.2118\/223939-PA"},{"key":"key-10.20965\/jaciii.2026.p0761-16","doi-asserted-by":"crossref","unstructured":"Z. Wang, D. Gao, and J. Liu, \u201cMulti-objective sidetracking horizontal well trajectory optimization in cluster wells based on DS algorithm,\u201d J. Petrol. Sci. Eng., Vol.147, pp. 771-778, 2016. https:\/\/doi.org\/10.1016\/j.petrol.2016.09.046","DOI":"10.1016\/j.petrol.2016.09.046"},{"key":"key-10.20965\/jaciii.2026.p0761-17","doi-asserted-by":"crossref","unstructured":"R. Khosravanian, V. Mansouri, D. A. Wood, and M. R. Alipour, \u201cA comparative study of several metaheuristic algorithms for optimizing complex 3-D well-path designs,\u201d J. Pet. Explor. Prod. Technol., Vol.8, No.4, pp. 1487-1503, 2018. https:\/\/doi.org\/10.1007\/s13202-018-0447-2","DOI":"10.1007\/s13202-018-0447-2"},{"key":"key-10.20965\/jaciii.2026.p0761-18","doi-asserted-by":"crossref","unstructured":"J. Xu and X. Chen, \u201cBat Algorithm Optimizer for Drilling Trajectory Designing under Wellbore Stability Constraints,\u201d 2018 37th Chinese Control Conf. (CCC), pp. 10276-10280, 2018. https:\/\/doi.org\/10.23919\/ChiCC.2018.8483403","DOI":"10.23919\/ChiCC.2018.8483403"},{"key":"key-10.20965\/jaciii.2026.p0761-19","doi-asserted-by":"crossref","unstructured":"K. Biswas, M. T. Rahman, A. H. Almulihi, F. Alassery, M. A. H. Al Askary, T. B. Hai, S. S. Kabir, A. I. Khan, and R. Ahmed, \u201cUncertainty handling in wellbore trajectory design: A modified cellular spotted hyena optimizer-based approach,\u201d J. Pet. Explor. Prod. Technol., Vol.12, pp. 2643-2661, 2022. https:\/\/doi.org\/10.1007\/s13202-022-01458-5","DOI":"10.1007\/s13202-022-01458-5"},{"key":"key-10.20965\/jaciii.2026.p0761-20","doi-asserted-by":"crossref","unstructured":"S. Sun, Y. Gao, X. Sun, J. Wu, and H. Chang, \u201cIntelligent optimization of horizontal wellbore trajectory based on reinforcement learning,\u201d Geoenergy Sci. Eng., Vol.244, Article No.213479, 2025. https:\/\/doi.org\/10.1016\/j.geoen.2024.213479","DOI":"10.1016\/j.geoen.2024.213479"},{"key":"key-10.20965\/jaciii.2026.p0761-21","doi-asserted-by":"crossref","unstructured":"W. Zhu, Y. Liu, L. Wang, J. Cao, Y. Li, and Y. Ma, \u201cDeep Reinforcement Learning for Well Trajectory Design with Collision Avoidance,\u201d SPE J., Vol.30, Issue 08, pp. 4545-4560, 2025. https:\/\/doi.org\/10.2118\/228295-PA","DOI":"10.2118\/228295-PA"},{"key":"key-10.20965\/jaciii.2026.p0761-22","doi-asserted-by":"crossref","unstructured":"S. Ghadami, H. Biglarian, H. Beyrami, and M. Salimi, \u201cOptimization of multilateral well trajectories using pattern search and genetic algorithms,\u201d Results Eng., Vol.16, Article No.100722, 2022. https:\/\/doi.org\/10.1016\/j.rineng.2022.100722","DOI":"10.1016\/j.rineng.2022.100722"},{"key":"key-10.20965\/jaciii.2026.p0761-23","doi-asserted-by":"crossref","unstructured":"A. Lamghari, \u201cMine Planning and Oil Field Development: A Survey and Research Potentials,\u201d Math. Geosci., Vol.49, No.3, pp. 395-437, 2017. https:\/\/doi.org\/10.1007\/s11004-017-9676-z","DOI":"10.1007\/s11004-017-9676-z"},{"key":"key-10.20965\/jaciii.2026.p0761-24","doi-asserted-by":"crossref","unstructured":"W. Huang, M. Wu, L. Chen, X. Chen, and W. Cao, \u201cMulti-objective drilling trajectory optimization using decomposition method with minimum fuzzy entropy-based comprehensive evaluation,\u201d Appl. Soft Comput., Vol.107, Article No.107392, 2021. https:\/\/doi.org\/10.1016\/j.asoc.2021.107392","DOI":"10.1016\/j.asoc.2021.107392"},{"key":"key-10.20965\/jaciii.2026.p0761-25","doi-asserted-by":"crossref","unstructured":"W. Huang, M. Wu, L. Chen, J. She, H. Hashimoto, and S. Kawata, \u201cMultiobjective Drilling Trajectory Optimization Considering Parameter Uncertainties,\u201d IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol.52, Issue 2, pp. 1224-1233, 2022. https:\/\/doi.org\/10.1109\/TSMC.2020.3019428","DOI":"10.1109\/TSMC.2020.3019428"},{"key":"key-10.20965\/jaciii.2026.p0761-26","doi-asserted-by":"crossref","unstructured":"J. Xu, X. Chen, W. Cao, and M. Wu, \u201cMulti-objective trajectory planning in the multiple strata drilling process: A bi-directional constrained co-evolutionary optimizer with Pareto front learning,\u201d Expert Syst. Appl., Vol.238, Part F, Article No.122119, 2024. https:\/\/doi.org\/10.1016\/j.eswa.2023.122119","DOI":"10.1016\/j.eswa.2023.122119"},{"key":"key-10.20965\/jaciii.2026.p0761-27","doi-asserted-by":"crossref","unstructured":"Z. Wang, S.-L. Shen, D. Chen, W. Li, W. Li, and Z. Fan, \u201cMulti-objective optimization of the wellbore trajectory considering both geological and engineering factors,\u201d Geoenergy Sci. Eng., Vol.246, Article No.213647, 2025. https:\/\/doi.org\/10.1016\/j.geoen.2025.213647","DOI":"10.1016\/j.geoen.2025.213647"},{"key":"key-10.20965\/jaciii.2026.p0761-28","doi-asserted-by":"crossref","unstructured":"J. Xu, X. Chen, Y. Zhou, M. Zhang, W. Cao, and M. Wu, \u201cExpensive deviation-correction drilling trajectory planning: A constrained multi-objective Bayesian optimization with feasibility-oriented bi-objective acquisition function,\u201d Control Eng. Pract., Vol.156, Article No.106240, 2025. https:\/\/doi.org\/10.1016\/j.conengprac.2025.106240","DOI":"10.1016\/j.conengprac.2025.106240"},{"key":"key-10.20965\/jaciii.2026.p0761-29","doi-asserted-by":"crossref","unstructured":"H. Yavari, J. Qajar, B. S. Aadnoy, and R. Khosravanian, \u201cSelection of Optimal Well Trajectory Using Multi-Objective Genetic Algorithm and TOPSIS Method,\u201d Arab. J. Sci. Eng., Vol.48, No.12, pp. 16831-16855, 2023. https:\/\/doi.org\/10.1007\/s13369-023-08149-1","DOI":"10.1007\/s13369-023-08149-1"},{"key":"key-10.20965\/jaciii.2026.p0761-30","doi-asserted-by":"crossref","unstructured":"J. Kudela, \u201cA critical problem in benchmarking and analysis of evolutionary computation methods,\u201d Nat. Mach. Intell., Vol.4, No.12, pp. 1238-1245, 2022. https:\/\/doi.org\/10.1038\/s42256-022-00579-0","DOI":"10.1038\/s42256-022-00579-0"},{"key":"key-10.20965\/jaciii.2026.p0761-31","doi-asserted-by":"crossref","unstructured":"K.-Z. Liu and C. Gan, \u201cDeterministic Gradient-Descent Learning of Linear Regressions: Adaptive Algorithms, Convergence Analysis and Noise Compensation,\u201d IEEE Trans. Pattern Anal. Mach. Intell., Vol.46, Issue 12, pp. 7867-7877, 2024. https:\/\/doi.org\/10.1109\/TPAMI.2024.3399312","DOI":"10.1109\/TPAMI.2024.3399312"},{"key":"key-10.20965\/jaciii.2026.p0761-32","doi-asserted-by":"crossref","unstructured":"D. A. Wood, \u201cConstrained optimization assists deviated wellbore trajectory selection from families of quadratic and cubic Bezier curves,\u201d Gas Sci. Eng., Vol.110, Article No.204869, 2023. https:\/\/doi.org\/10.1016\/j.jgsce.2022.204869","DOI":"10.1016\/j.jgsce.2022.204869"},{"key":"key-10.20965\/jaciii.2026.p0761-33","doi-asserted-by":"crossref","unstructured":"E. M. E. M. Shokir, M. K. Emera, S. M. Eid, and A. W. Wally, \u201cA New Optimization Model for 3D Well Design,\u201d Oil and Gas Sci. Technol., Vol.59, No.3, pp. 255-266, 2004. https:\/\/doi.org\/10.2516\/ogst:2004018","DOI":"10.2516\/ogst:2004018"},{"key":"key-10.20965\/jaciii.2026.p0761-34","doi-asserted-by":"crossref","unstructured":"J. Fliege and B. F. Svaiter, \u201cSteepest descent methods for multicriteria optimization,\u201d Math. Methods Oper. Res., Vol.51, No.3, pp. 479-494, 2000. https:\/\/doi.org\/10.1007\/s001860000043","DOI":"10.1007\/s001860000043"},{"key":"key-10.20965\/jaciii.2026.p0761-35","doi-asserted-by":"crossref","unstructured":"X. Liu and A. C. Reynolds, \u201cA multiobjective steepest descent method with applications to optimal well control,\u201d Comput. Geosci., Vol.20, No.2, pp. 355-374, 2016. https:\/\/doi.org\/10.1007\/s10596-016-9562-7","DOI":"10.1007\/s10596-016-9562-7"},{"key":"key-10.20965\/jaciii.2026.p0761-36","doi-asserted-by":"crossref","unstructured":"K. Izui, T. Yamada, S. Nishiwaki, and K. Tanaka, \u201cMultiobjective optimization using an aggregative gradient-based method,\u201d Struct. Multidiscip. Optim., Vol.51, No.1, pp. 173-182, 2015. https:\/\/doi.org\/10.1007\/s00158-014-1125-8","DOI":"10.1007\/s00158-014-1125-8"},{"key":"key-10.20965\/jaciii.2026.p0761-37","doi-asserted-by":"crossref","unstructured":"C. K. Goh, Y. S. Ong, K. C. Tan, and E. J. Teoh, \u201cAn investigation on evolutionary gradient search for multi-objective optimization,\u201d 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3741-3746, 2008. https:\/\/doi.org\/10.1109\/CEC.2008.4631304","DOI":"10.1109\/CEC.2008.4631304"},{"key":"key-10.20965\/jaciii.2026.p0761-38","doi-asserted-by":"crossref","unstructured":"K. Deb and H. Jain, \u201cAn Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems with Box Constraints,\u201d IEEE Trans. Evol. Comput., Vol.18, Issue 4, pp. 577-601, 2014. https:\/\/doi.org\/10.1109\/TEVC.2013.2281535","DOI":"10.1109\/TEVC.2013.2281535"},{"key":"key-10.20965\/jaciii.2026.p0761-39","doi-asserted-by":"crossref","unstructured":"Y. Tian, R. Cheng, X. Zhang, and Y. Jin, \u201cPlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization,\u201d IEEE Comput. Intell. Mag., Vol.12, Issue 4, pp. 73-87, 2017. https:\/\/doi.org\/10.1109\/MCI.2017.2742868","DOI":"10.1109\/MCI.2017.2742868"}],"container-title":["Journal of Advanced Computational Intelligence and Intelligent Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.fujipress.jp\/main\/wp-content\/themes\/Fujipress\/hyosetsu.php?ppno=jacii003000030012","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T15:03:52Z","timestamp":1779203032000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.fujipress.jp\/jaciii\/jc\/jacii003000030761"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,20]]},"references-count":39,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,5,20]]},"published-print":{"date-parts":[[2026,5,20]]}},"URL":"https:\/\/doi.org\/10.20965\/jaciii.2026.p0761","relation":{},"ISSN":["1883-8014","1343-0130"],"issn-type":[{"value":"1883-8014","type":"electronic"},{"value":"1343-0130","type":"print"}],"subject":[],"published":{"date-parts":[[2026,5,20]]}}}