{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:57:51Z","timestamp":1743145071363,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819635375"},{"type":"electronic","value":"9789819635382"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-3538-2_15","type":"book-chapter","created":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T12:30:00Z","timestamp":1740745800000},"page":"208-222","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Parallel TD3 for\u00a0Policy Gradient-Based Multi-condition Multi-objective Optimisation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-1103-2577","authenticated-orcid":false,"given":"Dasun Shalila","family":"Balasooriya","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1039-4766","authenticated-orcid":false,"given":"Alan","family":"Blair","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4916-8392","authenticated-orcid":false,"given":"Ben","family":"Wilks","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7173-7892","authenticated-orcid":false,"given":"Craig","family":"Wheeler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9543-2719","authenticated-orcid":false,"given":"Tahir","family":"Jauhar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7886-3653","authenticated-orcid":false,"given":"Stephan","family":"Chalup","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,28]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","unstructured":"Balsooriya, D.S., Blair, A., Wheeler, C., Chalup, S.: Multi-condition multi-objective airfoil shape optimisation using deep reinforcement learning compared to genetic algorithms. In: Optimization, Learning Algorithms and Applications. Proceedings of the OL2A 2024 Conference. Springer Nature Switzerland (in press), accepted 02.06.2024 (2024). https:\/\/doi.org\/10.1007\/978-3-031-77432-4_17","DOI":"10.1007\/978-3-031-77432-4_17"},{"key":"15_CR2","doi-asserted-by":"publisher","first-page":"89497","DOI":"10.1109\/ACCESS.2020.2990567","volume":"8","author":"J Blank","year":"2020","unstructured":"Blank, J., Deb, K.: PYMOO: multi-objective optimization in Python. IEEE Access 8, 89497\u201389509 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.2990567","journal-title":"IEEE Access"},{"key":"15_CR3","doi-asserted-by":"publisher","unstructured":"Dankwa, S., Zheng, W.: Twin-delayed DDPG: A deep reinforcement learning technique to model a continuous movement of an intelligent robot agent. In: Proceedings of the 3rd international conference on vision, image and signal processing, pp.\u00a01\u20135 (2019) https:\/\/doi.org\/10.1145\/3387168.3387199","DOI":"10.1145\/3387168.3387199"},{"key":"15_CR4","doi-asserted-by":"publisher","unstructured":"Deb, K., Agrawal, S., Pratap, A., Meyarivan, T.: A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Parallel Problem Solving from Nature PPSN VI: 6th International Conference Paris, France, 18\u201320 September 2000 Proceedings 6, pp. 849\u2013858. Springer (2000). https:\/\/doi.org\/10.1007\/3-540-45356-3_83","DOI":"10.1007\/3-540-45356-3_83"},{"key":"15_CR5","unstructured":"Deb, K., Saxena, D.: Searching for pareto-optimal solutions through dimensionality reduction for certain large-dimensional multi-objective optimization problems. In: Proceedings of the World Congress on Computational Intelligence (WCCI-2006), pp. 3352\u20133360 (2006)"},{"key":"15_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-84010-4_1","volume-title":"Low Reynolds Number Aerodyn.","author":"M Drela","year":"1989","unstructured":"Drela, M.: XFOIL: an analysis and design system for low Reynolds number airfoils. In: Mueller, T.J. (ed.) Low Reynolds Number Aerodyn., pp. 1\u201312. Springer, Berlin Heidelberg, Berlin, Heidelberg (1989). https:\/\/doi.org\/10.1007\/978-3-642-84010-4_1"},{"key":"15_CR7","unstructured":"Fujimoto, S., van Hoof, H., Meger, D.: Addressing function approximation error in actor-critic methods. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol.\u00a080, pp. 1587\u20131596. PMLR (2018). https:\/\/proceedings.mlr.press\/v80\/fujimoto18a.html"},{"key":"15_CR8","doi-asserted-by":"publisher","unstructured":"Ghraieb, H., Viquerat, J., Larcher, A., Meliga, P., Hachem, E.: Single-step deep reinforcement learning for two-and three-dimensional optimal shape design. AIP Adv. 12(8) (2022). https:\/\/doi.org\/10.1063\/5.0097241","DOI":"10.1063\/5.0097241"},{"key":"15_CR9","doi-asserted-by":"publisher","unstructured":"Ghraieb, H., Viquerat, J., Larcher, A., Meliga, P., Hachem, E.: Single-step deep reinforcement learning for open-loop control of laminar and turbulent flows. Phys. Rev. Fluids 6(5), 053902 (2021). https:\/\/doi.org\/10.1103\/PhysRevFluids.6.053902","DOI":"10.1103\/PhysRevFluids.6.053902"},{"key":"15_CR10","doi-asserted-by":"publisher","unstructured":"Hilbert, R., Janiga, G., Baron, R., Th\u00e9venin, D.: Multi-objective shape optimization of a heat exchanger using parallel genetic algorithms. Int. J. Heat Mass Transf. 49(15), 2567\u20132577 (2006). https:\/\/doi.org\/10.1016\/j.ijheatmasstransfer.2005.12.015","DOI":"10.1016\/j.ijheatmasstransfer.2005.12.015"},{"key":"15_CR11","unstructured":"Kalyonmoy, D.: Multi-Objective Optimization using Evolutionary Algorithms. Wiley, Chinchester (2001)"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Kemker, R., McClure, M., Abitino, A., Hayes, T., Kanan, C.: Measuring catastrophic forgetting in neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, no. 1 (2018)","DOI":"10.1609\/aaai.v32i1.11651"},{"key":"15_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.matlet.2023.135067","volume":"351","author":"D Khatamsaz","year":"2023","unstructured":"Khatamsaz, D., Vela, B., Arr\u00f3yave, R.: Multi-objective Bayesian alloy design using multi-task gaussian processes. Mater. Lett. 351, 135067 (2023). https:\/\/doi.org\/10.1016\/j.matlet.2023.135067","journal-title":"Mater. Lett."},{"key":"15_CR14","doi-asserted-by":"publisher","unstructured":"Kim, S., Kim, I., You, D.: Multi-condition multi-objective optimization using deep reinforcement learning. J. Comput. Phys. 462, 111263 (2022). https:\/\/doi.org\/10.1016\/j.jcp.2022.111263","DOI":"10.1016\/j.jcp.2022.111263"},{"key":"15_CR15","doi-asserted-by":"publisher","unstructured":"Kursawe, F.: A variant of evolution strategies for vector optimization. In: International Conference on Parallel Problem Solving from Nature, pp. 193\u2013197. Springer (1990). https:\/\/doi.org\/10.1007\/BFb0029752","DOI":"10.1007\/BFb0029752"},{"key":"15_CR16","doi-asserted-by":"publisher","unstructured":"Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning (2019). https:\/\/doi.org\/10.48550\/arXiv.1509.02971","DOI":"10.48550\/arXiv.1509.02971"},{"key":"15_CR17","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/s00158-008-0330-8","volume":"39","author":"GC Marano","year":"2009","unstructured":"Marano, G.C., Quaranta, G., Greco, R.: Multi-objective optimization by genetic algorithm of structural systems subject to random vibrations. Struct. Multidiscip. Optim. 39, 385\u2013399 (2009). https:\/\/doi.org\/10.1007\/s00158-008-0330-8","journal-title":"Struct. Multidiscip. Optim."},{"key":"15_CR18","unstructured":"Marco, N., Desideri, J.A., Lanteri, S.: Multi-objective optimization in CFD by genetic algorithms. Tech. Rep. RR-3686, INRIA (1999). https:\/\/inria.hal.science\/inria-00072983"},{"key":"15_CR19","doi-asserted-by":"publisher","unstructured":"Miettinen, K.: Nonlinear Multiobjective Optimization, vol.\u00a012. Springer Science & Business Media (1999). https:\/\/doi.org\/10.1007\/978-1-4615-5563-6","DOI":"10.1007\/978-1-4615-5563-6"},{"issue":"1","key":"15_CR20","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1109\/41.824144","volume":"47","author":"S Obayashi","year":"2000","unstructured":"Obayashi, S., Tsukahara, T., Nakamura, T.: Multiobjective genetic algorithm applied to aerodynamic design of cascade airfoils. IEEE Trans. Industr. Electron. 47(1), 211\u2013216 (2000). https:\/\/doi.org\/10.1109\/41.824144","journal-title":"IEEE Trans. Industr. Electron."},{"key":"15_CR21","unstructured":"Paszke, A., et al.: PyTorch: an imperative style, high-performance deep learning library (2019). https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2019\/file\/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf"},{"issue":"1","key":"15_CR22","doi-asserted-by":"publisher","first-page":"106","DOI":"10.3390\/app11010106","volume":"11","author":"S Qin","year":"2020","unstructured":"Qin, S., Wang, S., Wang, L., Wang, C., Sun, G., Zhong, Y.: Multi-objective optimization of cascade blade profile based on reinforcement learning. Appl. Sci. 11(1), 106 (2020). https:\/\/doi.org\/10.3390\/app11010106","journal-title":"Appl. Sci."},{"key":"15_CR23","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT press (2018)"},{"issue":"6","key":"15_CR24","doi-asserted-by":"publisher","first-page":"1648","DOI":"10.1016\/j.cor.2012.01.001","volume":"40","author":"YY Tan","year":"2013","unstructured":"Tan, Y.Y., Jiao, Y.C., Li, H., Wang, X.K.: MOEA\/D+ uniform design: a new version of MOEA\/D for optimization problems with many objectives. Comput. Oper. Res. 40(6), 1648\u20131660 (2013). https:\/\/doi.org\/10.1016\/j.cor.2012.01.001","journal-title":"Comput. Oper. Res."},{"key":"15_CR25","doi-asserted-by":"publisher","unstructured":"Van\u00a0Moffaert, K., Drugan, M.M., Now\u00e9, A.: Scalarized multi-objective reinforcement learning: Novel design techniques. In: 2013 IEEE Symposium on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL), pp. 191\u2013199. IEEE (2013). https:\/\/doi.org\/10.1109\/ADPRL.2013.6615007","DOI":"10.1109\/ADPRL.2013.6615007"},{"issue":"9","key":"15_CR26","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.2514\/2.274","volume":"35","author":"A Vicini","year":"1997","unstructured":"Vicini, A., Quagliarella, D.: Inverse and direct airfoil design using a multiobjective genetic algorithm. AIAA J. 35(9), 1499\u20131505 (1997). https:\/\/doi.org\/10.2514\/2.274","journal-title":"AIAA J."},{"issue":"1","key":"15_CR27","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1007\/s00521-022-07779-0","volume":"35","author":"J Viquerat","year":"2023","unstructured":"Viquerat, J., Duvigneau, R., Meliga, P., Kuhnle, A., Hachem, E.: Policy-based optimization: single-step policy gradient method seen as an evolution strategy. Neural Comput. Appl. 35(1), 449\u2013467 (2023). https:\/\/doi.org\/10.1007\/s00521-022-07779-0","journal-title":"Neural Comput. Appl."},{"key":"15_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcp.2020.110080","volume":"428","author":"J Viquerat","year":"2021","unstructured":"Viquerat, J., Rabault, J., Kuhnle, A., Ghraieb, H., Larcher, A., Hachem, E.: Direct shape optimization through deep reinforcement learning. J. Comput. Phys. 428, 110080 (2021). https:\/\/doi.org\/10.1016\/j.jcp.2020.110080","journal-title":"J. Comput. Phys."},{"key":"15_CR29","doi-asserted-by":"publisher","unstructured":"Yonekura, K., Hattori, H.: Framework for design optimization using deep reinforcement learning. Struct. Multidiscip. Optim. 60, 1709\u20131713 (2019) https:\/\/doi.org\/10.1007\/s00158-019-02276-w","DOI":"10.1007\/s00158-019-02276-w"},{"key":"15_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119066","volume":"642","author":"K Yonekura","year":"2023","unstructured":"Yonekura, K., Hattori, H., Shikada, S., Maruyama, K.: Turbine blade optimization considering smoothness of the Mach number using deep reinforcement learning. Inf. Sci. 642, 119066 (2023). https:\/\/doi.org\/10.1016\/j.ins.2023.119066","journal-title":"Inf. Sci."},{"key":"15_CR31","doi-asserted-by":"publisher","unstructured":"Zhang, F., Li, J., Li, Z.: A TD3-based multi-agent deep reinforcement learning method in mixed cooperation-competition environment. Neurocomputing 411, 206\u2013215 (2020) https:\/\/doi.org\/10.1016\/j.neucom.2020.05.097","DOI":"10.1016\/j.neucom.2020.05.097"},{"issue":"6","key":"15_CR32","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang, Q., Li, H.: MOEA\/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712\u2013731 (2007). https:\/\/doi.org\/10.1109\/TEVC.2007.892759","journal-title":"IEEE Trans. Evol. Comput."},{"key":"15_CR33","unstructured":"Zitzler, E.: Evolutionary algorithms for multiobjective optimization: methods and applications. Phd thesis, TIK-SCHRIFTENREIHE NR. 30, Swiss Federal Institute of Technology Zurich, Computer Engineering and Networks Laboratory (1999)"},{"issue":"2","key":"15_CR34","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.: Comparison of multiobjective evolutionary algorithms: empirical results. Evol. Comput. 8(2), 173\u2013195 (2000). https:\/\/doi.org\/10.1162\/106365600568202","journal-title":"Evol. Comput."}],"container-title":["Lecture Notes in Computer Science","Evolutionary Multi-Criterion Optimization"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-3538-2_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,28]],"date-time":"2025-02-28T12:30:09Z","timestamp":1740745809000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-3538-2_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819635375","9789819635382"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-3538-2_15","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"28 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EMO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Evolutionary Multi-Criterion Optimization","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canberra, ACT","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 March 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 March 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"emo2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.emo2025.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}