{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T03:34:35Z","timestamp":1778038475799,"version":"3.51.4"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T00:00:00Z","timestamp":1719878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In this paper, a novel Many-Objective Whale Optimization Algorithm (MaOWOA) is proposed to overcome the challenges of large-scale many-objective optimization problems (LSMOPs) encountered in diverse fields such as engineering. Existing algorithms suffer from curse of dimensionality i.e., they are unable to balance convergence with diversity in extensive decision-making scenarios. MaOWOA introduces strategies to accelerate convergence, balance convergence and diversity in solutions and enhance diversity in high-dimensional spaces. The prime contributions of this paper are\u2014development of MaOWOA, incorporation an Information Feedback Mechanism (IFM) for rapid convergence, a Reference Point-based Selection (RPS) to balance convergence and diversity and a Niche Preservation Strategy (NPS) to improve diversity and prevent overcrowding. A comprehensive evaluation demonstrates MaOWOA superior performance over existing algorithms (MaOPSO, MOEA\/DD, MaOABC, NSGA-III) across LSMOP1-LSMOP9 benchmarks and RWMaOP1-RWMaOP5 problems. Results validated using Wilcoxon rank sum tests, highlight MaOWOA excellence in key metrics such as generational distance, spread, spacing, runtime, inverse generational distance and hypervolume, outperforming in 71.8% of tested scenarios. Thus, MaOWOA represents a significant advancement in many-objective optimization, offering new avenues for addressing LSMOPs and RWMaOPs\u2019 inherent challenges. This paper details MaOWOA development, theoretical basis and effectiveness, marking a promising direction for future research in optimization strategies amidst growing problem complexity.<\/jats:p>","DOI":"10.1007\/s44196-024-00562-0","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T11:03:07Z","timestamp":1719918187000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Many-Objective Whale Optimization Algorithm for Engineering Design and Large-Scale Many-Objective Optimization Problems"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9289-9495","authenticated-orcid":false,"given":"Kanak","family":"Kalita","sequence":"first","affiliation":[]},{"given":"Janjhyam Venkata Naga","family":"Ramesh","sequence":"additional","affiliation":[]},{"given":"Robert","family":"\u010cep","sequence":"additional","affiliation":[]},{"given":"Pradeep","family":"Jangir","sequence":"additional","affiliation":[]},{"given":"Sundaram B.","family":"Pandya","sequence":"additional","affiliation":[]},{"given":"Ranjan Kumar","family":"Ghadai","sequence":"additional","affiliation":[]},{"given":"Laith","family":"Abualigah","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"562_CR1","doi-asserted-by":"publisher","DOI":"10.1088\/2053-1591\/aa5f6a","author":"N Ahmad","year":"2017","unstructured":"Ahmad, N., et al.: Multi-objective optimization in the development of oil and water repellent cellulose fabric based on response surface methodology and the desirability function. Mater. Res. Express (2017). https:\/\/doi.org\/10.1088\/2053-1591\/aa5f6a","journal-title":"Mater. Res. Express"},{"issue":"19","key":"562_CR2","doi-asserted-by":"publisher","first-page":"6341","DOI":"10.1007\/s00500-017-2687-3","volume":"22","author":"Amarjeet","year":"2018","unstructured":"Amarjeet, Chhabra, J.K.: Many-objective artificial bee colony algorithm for large-scale software module clustering problem. Soft. Comput. 22(19), 6341\u20136361 (2018). https:\/\/doi.org\/10.1007\/s00500-017-2687-3","journal-title":"Soft. Comput."},{"issue":"3","key":"562_CR3","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1109\/TEVC.2014.2339823","volume":"19","author":"M Asafuddoula","year":"2015","unstructured":"Asafuddoula, M., et al.: A decomposition-based evolutionary algorithm for many objective optimization. IEEE Trans. Evol. Comput. 19(3), 445\u2013460 (2015). https:\/\/doi.org\/10.1109\/TEVC.2014.2339823","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"1","key":"562_CR4","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1162\/EVCO_a_00009","volume":"19","author":"J Bader","year":"2011","unstructured":"Bader, J., Zitzler, E.: Hype: An algorithm for fast hypervolume-based many-objective optimization. Evol. Comput. 19(1), 45\u201376 (2011). https:\/\/doi.org\/10.1162\/EVCO_a_00009","journal-title":"Evol. Comput."},{"issue":"4","key":"562_CR5","doi-asserted-by":"publisher","first-page":"2756","DOI":"10.1109\/TNSE.2021.3057915","volume":"8","author":"B Cao","year":"2021","unstructured":"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. 8(4), 2756\u20132764 (2021). https:\/\/doi.org\/10.1109\/TNSE.2021.3057915","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"562_CR6","doi-asserted-by":"publisher","first-page":"681","DOI":"10.1016\/j.asoc.2015.06.020","volume":"35","author":"L Cai","year":"2015","unstructured":"Cai, L., et al.: A clustering-ranking method for many-objective optimization. Appl. Soft Comput. 35, 681\u2013694 (2015). https:\/\/doi.org\/10.1016\/j.asoc.2015.06.020","journal-title":"Appl. Soft Comput."},{"key":"562_CR7","doi-asserted-by":"publisher","DOI":"10.1002\/mmce.21064","author":"YS Chen","year":"2017","unstructured":"Chen, Y.S.: Performance enhancement of multiband antennas through a two-stage optimization technique. Int. J. RF Microw. Comput. Aid. Eng. (2017). https:\/\/doi.org\/10.1002\/mmce.21064","journal-title":"Int. J. RF Microw. Comput. Aid. Eng."},{"issue":"12","key":"562_CR8","doi-asserted-by":"publisher","first-page":"4108","DOI":"10.1109\/TCYB.2016.2600577","volume":"47","author":"R Cheng","year":"2017","unstructured":"Cheng, R., et al.: Test problems for large-scale multiobjective and many-objective optimization. IEEE Trans. Cybern. 47(12), 4108\u20134121 (2017). https:\/\/doi.org\/10.1109\/TCYB.2016.2600577","journal-title":"IEEE Trans. Cybern."},{"issue":"4","key":"562_CR9","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1109\/TEVC.2015.2424921","volume":"19","author":"J Cheng","year":"2015","unstructured":"Cheng, J., et al.: A many-objective evolutionary algorithm with enhanced mating and environmental selections. IEEE Trans. Evol. Comput. 19(4), 592\u2013605 (2015)","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"5","key":"562_CR10","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1109\/TEVC.2016.2519378","volume":"20","author":"R Cheng","year":"2016","unstructured":"Cheng, R., et al.: A reference vector guided evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 20(5), 773\u2013791 (2016). https:\/\/doi.org\/10.1109\/TEVC.2016.2519378","journal-title":"IEEE Trans. Evol. Comput."},{"key":"562_CR11","volume-title":"Evolutionary algorithms for solving multi-objective problems genetic and evolutionary computation series","author":"CA Coello Coello","year":"2007","unstructured":"Coello Coello, C.A., et al.: Evolutionary algorithms for solving multi-objective problems genetic and evolutionary computation series, 2nd edn. Springer (2007)","edition":"2"},{"key":"562_CR12","doi-asserted-by":"publisher","first-page":"100980","DOI":"10.1016\/j.swevo.2021.100980","volume":"68","author":"LRC de Farias","year":"2022","unstructured":"de Farias, L.R.C., Ara\u00fajo, A.F.R.: A decomposition-based many-objective evolutionary algorithm updating weights when required. Swarm Evol. Comput. 68, 100980 (2022). https:\/\/doi.org\/10.1016\/j.swevo.2021.100980","journal-title":"Swarm Evol. Comput."},{"issue":"4","key":"562_CR13","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1109\/TEVC.2013.2281535","volume":"18","author":"K Deb","year":"2014","unstructured":"Deb, K., Jain, H.: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part i: solving problems with box constraints. IEEE Trans. Evol. Comput. 18(4), 577\u2013601 (2014). https:\/\/doi.org\/10.1109\/TEVC.2013.2281535","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"2","key":"562_CR14","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1109\/TEVC.2022.3166815","volume":"27","author":"Q Deng","year":"2022","unstructured":"Deng, Q., et al.: Objective space-based population generation to accelerate evolutionary algorithms for large-scale many-objective optimization. IEEE Trans. Evol. Comput. 27(2), 326\u2013340 (2022). https:\/\/doi.org\/10.1109\/TEVC.2022.3166815","journal-title":"IEEE Trans. Evol. Comput."},{"key":"562_CR15","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.ins.2016.09.026","volume":"374","author":"EMN Figueiredo","year":"2016","unstructured":"Figueiredo, E.M.N., et al.: Many objective particle swarm optimization. Inf. Sci. 374, 115\u2013134 (2016). https:\/\/doi.org\/10.1016\/j.ins.2016.09.026","journal-title":"Inf. Sci."},{"issue":"4\u20136","key":"562_CR16","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/j.cma.2006.07.010","volume":"196","author":"T Goel","year":"2007","unstructured":"Goel, T., et al.: Response surface approximation of Pareto optimal front in multi-objective optimization. Comput. Methods Appl. Mech. Eng. 196(4\u20136), 879\u2013893 (2007). https:\/\/doi.org\/10.1016\/j.cma.2006.07.010","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"562_CR17","doi-asserted-by":"publisher","first-page":"116118","DOI":"10.1016\/j.eswa.2021.116118","volume":"189","author":"Q Gu","year":"2022","unstructured":"Gu, Q., et al.: An improved competitive particle swarm optimization for many-objective optimization problems. Expert Syst. Appl. 189, 116118 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2021.116118","journal-title":"Expert Syst. Appl."},{"issue":"2","key":"562_CR18","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1109\/TEVC.2016.2598687","volume":"21","author":"Z He","year":"2017","unstructured":"He, Z., Yen, G.G.: Many-objective evolutionary algorithms based on coordinated selection strategy. IEEE Trans. Evol. Comput. 21(2), 220\u2013233 (2017). https:\/\/doi.org\/10.1109\/TEVC.2016.2598687","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"2","key":"562_CR19","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1109\/TEVC.2013.2258025","volume":"18","author":"Z He","year":"2014","unstructured":"He, Z., et al.: Fuzzy-based pareto optimality for many-objective evolutionary algorithms. IEEE Trans. Evol. Comput. 18(2), 269\u2013285 (2014). https:\/\/doi.org\/10.1109\/TEVC.2013.2258025","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"3","key":"562_CR20","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1109\/TEVC.2016.2592479","volume":"21","author":"S Jiang","year":"2017","unstructured":"Jiang, S., Yang, S.: A strength pareto evolutionary algorithm based on reference direction for multiobjective and many-objective optimization. IEEE Trans. Evol. Comput. 21(3), 329\u2013346 (2017). https:\/\/doi.org\/10.1109\/TEVC.2016.2592479","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"8","key":"562_CR21","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1016\/j.asoc.2017.09.017","volume":"62","author":"T Lafet\u00e1","year":"2018","unstructured":"Lafet\u00e1, T., et al.: Meands: A many-objective evolutionary algorithm based on non-dominated decomposed sets applied to multicast routing. Appl. Soft Comput. 62(8), 851\u2013866 (2018). https:\/\/doi.org\/10.1016\/j.asoc.2017.09.017","journal-title":"Appl. Soft Comput."},{"issue":"1","key":"562_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2792984","volume":"48","author":"B Li","year":"2015","unstructured":"Li, B., et al.: Many-objective evolutionary algorithms:a survey. ACM Comput. Surv. 48(1), 1\u201335 (2015)","journal-title":"ACM Comput. Surv."},{"key":"562_CR23","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1016\/j.asoc.2018.02.048","volume":"67","author":"F Li","year":"2018","unstructured":"Li, F., et al.: A two-stage r2 indicator based evolutionary algorithm for many-objective optimization. Appl. Soft Comput. 67, 245\u2013260 (2018). https:\/\/doi.org\/10.1016\/j.asoc.2018.02.048","journal-title":"Appl. Soft Comput."},{"issue":"5","key":"562_CR24","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1109\/TEVC.2014.2373386","volume":"19","author":"K Li","year":"2015","unstructured":"Li, K., et al.: An evolutionary many-objective optimization algorithm based on dominance and decomposition. IEEE Trans. Evol. Comput. 19(5), 694\u2013716 (2015). https:\/\/doi.org\/10.1109\/TEVC.2014.2373386","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"4","key":"562_CR25","doi-asserted-by":"publisher","first-page":"26194","DOI":"10.1109\/ACCESS.2018.2832181","volume":"6","author":"K Li","year":"2018","unstructured":"Li, K., et al.: Evolutionary many-objective optimization: A comparative study of the state-of-the-art. IEEE Access 6(4), 26194\u201326214 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2832181","journal-title":"IEEE Access"},{"issue":"3","key":"562_CR26","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1109\/TEVC.2013.2262178","volume":"18","author":"M Li","year":"2014","unstructured":"Li, M., et al.: Shift-based density estimation for pareto-based algorithms in many-objective optimization. IEEE Trans. Evol. Comput. 18(3), 348\u2013365 (2014). https:\/\/doi.org\/10.1109\/TEVC.2013.2262178","journal-title":"IEEE Trans. Evol. Comput."},{"key":"562_CR27","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.artint.2015.06.007","volume":"228","author":"M Li","year":"2015","unstructured":"Li, M., et al.: Bi-goal evolution for many-objective optimization problems. Artif. Intell. 228, 45\u201365 (2015). https:\/\/doi.org\/10.1016\/j.artint.2015.06.007","journal-title":"Artif. Intell."},{"issue":"3","key":"562_CR28","doi-asserted-by":"publisher","first-page":"450","DOI":"10.1109\/TEVC.2013.2281533","volume":"18","author":"HL Liu","year":"2014","unstructured":"Liu, H.L., et al.: Decomposition of a multiobjective optimization problem into a number of simple multiobjective subproblems. IEEE Trans. Evol. Comput. 18(3), 450\u2013455 (2014). https:\/\/doi.org\/10.1109\/TEVC.2013.2281533","journal-title":"IEEE Trans. Evol. Comput."},{"key":"562_CR29","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","volume":"95","author":"S Mirjalili","year":"2016","unstructured":"Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51\u201367 (2016). https:\/\/doi.org\/10.1016\/j.advengsoft.2016.01.008","journal-title":"Adv. Eng. Softw."},{"issue":"10","key":"562_CR30","first-page":"2301","volume":"11","author":"RC Narayanan","year":"2023","unstructured":"Narayanan, R.C., et al.: A novel many-objective sine\u2013cosine algorithm (MaOSCA) for engineering. Appl. Math. 11(10), 2301 (2023)","journal-title":"Appl. Math."},{"key":"562_CR31","doi-asserted-by":"publisher","first-page":"108190","DOI":"10.1016\/j.ress.2021.108190","volume":"220","author":"R Nath","year":"2022","unstructured":"Nath, R., Muhuri, P.K.: Evolutionary optimization based solution approaches for many objective reliability-redundancy allocation problem. Reliab. Eng. Syst. Saf. 220, 108190 (2022). https:\/\/doi.org\/10.1016\/j.ress.2021.108190","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"562_CR32","doi-asserted-by":"publisher","first-page":"102181","DOI":"10.1016\/j.mex.2023.102181","volume":"10","author":"N Panagant","year":"2023","unstructured":"Panagant, N., et al.: Many-objective meta-heuristic methods for solving constrained truss optimisation problems: a comparative analysis. MethodsX 10, 102181 (2023). https:\/\/doi.org\/10.1016\/j.mex.2023.102181","journal-title":"MethodsX"},{"issue":"11","key":"562_CR33","doi-asserted-by":"publisher","first-page":"6599","DOI":"10.1109\/TMC.2022.3199876","volume":"22","author":"Z Xiao","year":"2023","unstructured":"Xiao, Z., Shu, J., Jiang, H., Lui, J.C.S., Min, G., Liu, J., Dustdar, S.: Multi-objective parallel task offloading and content caching in D2D-aided MEC networks. IEEE Trans. Mobile Comput. 22(11), 6599\u20136615 (2023). https:\/\/doi.org\/10.1109\/TMC.2022.3199876","journal-title":"IEEE Trans. Mobile Comput."},{"issue":"5","key":"562_CR34","doi-asserted-by":"publisher","first-page":"3597","DOI":"10.1109\/TII.2019.2952565","volume":"16","author":"B Cao","year":"2020","unstructured":"Cao, B., Zhao, J., Yang, P., Gu, Y., Muhammad, K., Rodrigues, J.J., de Albuquerque, V.H.: Multiobjective 3-D topology optimization of next-generation wireless data center network. IEEE Trans. Ind. Inform. 16(5), 3597\u20133605 (2020). https:\/\/doi.org\/10.1109\/TII.2019.2952565","journal-title":"IEEE Trans. Ind. Inform."},{"key":"562_CR35","doi-asserted-by":"publisher","first-page":"117555","DOI":"10.1016\/j.eswa.2022.117555","volume":"204","author":"C Lu","year":"2022","unstructured":"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. 204, 117555 (2022). https:\/\/doi.org\/10.1016\/j.eswa.2022.117555","journal-title":"Expert Syst. Appl."},{"key":"562_CR36","doi-asserted-by":"publisher","first-page":"9462048","DOI":"10.1155\/2020\/9462048","volume":"2020","author":"L Yin","year":"2020","unstructured":"Yin, L., Zhuang, M., Jia, J., Wang, H.: Energy saving in flow-shop scheduling management: an improved multiobjective model based on grey wolf optimization algorithm. Math. Probl. Eng. 2020, 9462048 (2020). https:\/\/doi.org\/10.1155\/2020\/9462048","journal-title":"Math. Probl. Eng."},{"issue":"1","key":"562_CR37","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1109\/TEVC.2016.2587808","volume":"21","author":"Y Xiang","year":"2017","unstructured":"Xiang, Y., et al.: A vector angle-based evolutionary algorithm for unconstrained many-objective optimization. IEEE Trans. Evol. Comput. 21(1), 131\u2013152 (2017). https:\/\/doi.org\/10.1109\/TEVC.2016.2587808","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"5","key":"562_CR38","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1109\/TEVC.2012.2227145","volume":"17","author":"S Yang","year":"2013","unstructured":"Yang, S., et al.: A grid-based evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 17(5), 721\u2013736 (2013). https:\/\/doi.org\/10.1109\/TEVC.2012.2227145","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"2","key":"562_CR39","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1109\/TEVC.2015.2443001","volume":"20","author":"Y Yuan","year":"2016","unstructured":"Yuan, Y., et al.: Balancing convergence and diversity in decomposition-based many-objective optimizers. IEEE Trans. Evol. Comput. 20(2), 180\u2013198 (2016). https:\/\/doi.org\/10.1109\/TEVC.2015.2443001","journal-title":"IEEE Trans. Evol. Comput."},{"key":"562_CR40","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1016\/j.swevo.2018.05.004","volume":"44","author":"S Zapotecas-Mart\u00ednez","year":"2019","unstructured":"Zapotecas-Mart\u00ednez, S., et al.: Libea: A lebesgue indicator-based evolutionary algorithm for multi-objective optimization. Swarm Evol. Comput. 44, 404\u2013419 (2019). https:\/\/doi.org\/10.1016\/j.swevo.2018.05.004","journal-title":"Swarm Evol. Comput."},{"issue":"9","key":"562_CR41","doi-asserted-by":"publisher","first-page":"2703","DOI":"10.1109\/TCYB.2017.2711038","volume":"47","author":"L Zhang","year":"2017","unstructured":"Zhang, L., et al.: A mixed representation-based multiobjective evolutionary algorithm for overlapping community detection. IEEE Trans. Cybern. 47(9), 2703\u20132716 (2017). https:\/\/doi.org\/10.1109\/TCYB.2017.2711038","journal-title":"IEEE Trans. Cybern."},{"issue":"6","key":"562_CR42","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang, Q., et al.: moea\/d: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712\u2013731 (2007)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"562_CR43","doi-asserted-by":"publisher","first-page":"118049","DOI":"10.1016\/j.envres.2023.118049","volume":"245","author":"UA Bhatti","year":"2024","unstructured":"Bhatti, U.A., et al.: Global production patterns: Understanding the relationship between greenhouse gas emissions, agriculture greening and climate variability. Environ. Res. 245, 118049 (2024)","journal-title":"Environ. Res."},{"key":"562_CR44","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2023.037958","author":"UA Bhatti","year":"2023","unstructured":"Bhatti, U.A., et al.: Deep learning-based trees disease recognition and classification using hyperspectral data. Comput. Mater. Continua (2023). https:\/\/doi.org\/10.32604\/cmc.2023.037958","journal-title":"Comput. Mater. Continua"},{"key":"562_CR45","doi-asserted-by":"publisher","first-page":"137969","DOI":"10.1016\/j.jclepro.2023.137969","volume":"417","author":"UA Bhatti","year":"2023","unstructured":"Bhatti, U.A., et al.: The effects of socioeconomic factors on particulate matter concentration in China: New evidence from spatial econometric model. J. Clean. Prod. 417, 137969 (2023)","journal-title":"J. Clean. Prod."},{"key":"562_CR46","doi-asserted-by":"publisher","first-page":"1142957","DOI":"10.3389\/fpls.2023.1142957","volume":"14","author":"S Wang","year":"2023","unstructured":"Wang, S., et al.: Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust. Front. Plant Sci. 14, 1142957 (2023)","journal-title":"Front. Plant Sci."},{"key":"562_CR47","doi-asserted-by":"publisher","first-page":"120496","DOI":"10.1016\/j.eswa.2023.120496","volume":"229","author":"UA Bhatti","year":"2023","unstructured":"Bhatti, U.A., et al.: MFFCG\u2013Multi feature fusion for hyperspectral image classification using graph attention network. Expert Syst. Appl. 229, 120496 (2023)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"562_CR48","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1109\/TEVC.2014.2378512","volume":"19","author":"X Zhang","year":"2015","unstructured":"Zhang, X., et al.: A knee point-driven evolutionary algorithm for many-objective optimization. IEEE Trans. Evol. Comput. 19(6), 761\u2013776 (2015). https:\/\/doi.org\/10.1109\/TEVC.2014.2378512","journal-title":"IEEE Trans. Evol. Comput."},{"issue":"3","key":"562_CR49","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/MCI.2017.2708578","volume":"12","author":"X Zhang","year":"2017","unstructured":"Zhang, X., Duan, F., Zhang, L., Cheng, F., Jin, Y., Tang, K.: Pattern recommendation in task-oriented applications: a multi-objective perspective [application notes]. IEEE Comput. Intell. Mag. 12(3), 43\u201353 (2017)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"562_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2020.02.066","volume":"522","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., et al.: Enhancing MOEA\/D with information feedback models for large-scale many-objective optimization. Inf. Sci. 522, 1\u201316 (2020). https:\/\/doi.org\/10.1016\/j.ins.2020.02.066","journal-title":"Inf. Sci."},{"key":"562_CR51","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.ins.2021.11.027","volume":"583","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., et al.: An efficient interval many-objective evolutionary algorithm for cloud task scheduling problem under uncertainty. Inf. Sci. 583, 56\u201372 (2022). https:\/\/doi.org\/10.1016\/j.ins.2021.11.027","journal-title":"Inf. Sci."},{"key":"562_CR52","doi-asserted-by":"crossref","unstructured":"Zitzler, E., Knzli, S.: Indicator-based selection in multiobjective search. In: International Conference on Parallel Problem Solving from Nature, vol. 3242, pp. 832\u2013842. Springer (2004)","DOI":"10.1007\/978-3-540-30217-9_84"}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00562-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00562-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00562-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T11:15:20Z","timestamp":1719918920000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00562-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,2]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["562"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00562-0","relation":{},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,2]]},"assertion":[{"value":"1 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2024","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"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Institutional Review Board Statement:"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed Consent"}}],"article-number":"171"}}