The cooling-slope (CS) casting technique is one of the simple semi-solid processing (SSP) processes a foundryman uses to produce the feedstock. This study attempts to develop mathematical regression models and optimise the CS parameters process for predicting optimal feedstock performance, which utilises tensile strength and impact strength to reduce the number of experimental runs and material wastage. This study considers several parameters, including pouring temperature, pouring distance, and slanting angles for producing quality feedstock. Hence, multi-objective optimisation (MOO) techniques using computational approaches utilised alongside the caster while deciding to design are applied to help produce faster and more accurate output. The experiment was performed based on the full factorial design (FFD). Then, mathematical regression models were developed from the data obtained and implemented as an objective function equation in the MOO optimisation process. In this study, MOO named multi-objective Jaya (MOJaya) was improved in terms of hybrid MOJaya and inertia weight with archive K-Nearest Neighbor (MOiJaya-aKNN) algorithm. The proposed algorithm was improved in terms of the search process and archive selection to achieve a better feedstock performance through the CS. The study’s findings showed that the values of tensile and impact strengths from MOiJaya_aKNN are close to the experiment values. The results show that the hybrid MOJaya has improved the prediction of feedstock using optimal CS parameters.

• Abdelgneia, M. A. H., Omar, M. Z., Ghazalib, M. J., Gebrilb, M. A., & Mohammed, M. N. (2019). The effect of the rheocast process on the microstructure and mechanical properties of Al-5.7Si-2Cu-0.3Mg alloy. Jurnal Kejuruteraan, 31(2), 317-326. https://doi.org/10.17576/jkukm-2019-31(2)-17

• Abdin, Z., Prabantariksob, R. M., Fahmy, E., & Farhan, A. (2022). Analysis of the efficiency of insurance companies in Indonesia. Decision Science Letters, 11(2022), 105-112. https://doi.org/10.5267/j.dsl.2022.1.002

• Agarwal, N., Pradhan, M. K., & Shrivastava, N. (2018). A new respond Jaya algorithm for optimization of EDM process parameters. Materials Todays Proceedings, 5(11, Part 3), 23759-23768. https://doi.org/10.1016/j.matpr.2018.10.167

• Annamalai, S., Periyakgoundar, S., & Gunasekaran, S. (2019). Magnesium alloys: A review of applications. Materials and Technology, 53(6), 881-890. https://doi.org/10.17222/mit.2019.065

• Asadollahi-Yazdi, E., Gardan, J., & Lafon, P. (2018). Multi-objective optimization of additive manufacturing process. IFAC-PapersOnLine, 51(11), 152-157. https://doi.org/10.1016/j.ifacol.2018.08.250

• Balachandran, G. (2018). Challenges in special steel making. IOP Conference Series: Materials Science and Engineering, 314, Article 012016. https://doi.org/10.1088/1757-899X/314/1/012016

• Binesh, B., & Aghaie-Khafri, M. (2017). Modelling and optimization of semi-solid processing of 7075 Al alloy. Materials Research Express, 4, Article 096502. https://doi.org/10.1088/2053-1591/aa8272

• Brezocnik, M., & Župerl, U. (2021). Optimization of the continuous casting process of hypoeutectoid steel grades using multiple linear regression and genetic programming - An industrial study. Metals, 11(6), Article 972. https://doi.org/10.3390/met11060972

• Britto, A., & Pozo, A. (2012). Using archiving methods to control convergence and diversity for many-objective problems in particle swarm optimization. In 2012 IEEE Congress on Evolutionary Computation (pp. 1-8). IEEE Publishing. https://doi.org/10.1109/CEC.2012.6256149

• Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197.

• El-Ashmawi, W. H., Ali, A. F., & Slowik, A. (2020). An improved Jaya algorithm with a modified swap operator for solving team formation problem. Soft Computing, 24, 16627-16641. https://doi.org/10.1007/s00500-020-04965-x

• Esonye, C., Onukwuli, O. D., Anadebe, V. C., Ezeugo, J. N. O., & Ogbodo, N. J. (2021). Application of soft-computing techniques for statistical modeling and optimization of Dyacrodes edulis seed oil extraction using polar and non-polar solvents. Heliyon, 7(3), Article e06342. https://doi.org/10.1016/j.heliyon.2021.e06342

• Fadaee, M., Mahdavi-Meymand, A., & Zounemat-Kermani, M. (2022). Suspended sediment prediction using integrative soft computing models: On the analogy between the butterfly optimization and genetic algorithms. Geocarto International, 37(4), 961-977. https://doi.org/10.1080/10106049.2020.1753821

• Feng, Q., & Zhou, X. (2019). Automated and robust multi-objective optimal design of thin-walled product injection process based on hybrid RBF-MOGA. The International Journal of Advanced Manufacturing Technology, 101, 2217-2231. https://doi.org/10.1007/s00170-018-3084-5

• Feng, Y., Lu, R., Gao, Y., Zheng, H., Wang, Y., & Mo, W. (2018). Multi-objective optimization of VBHF in sheet metal deep-drawing using Kriging, MOABC, and set pair analysis. The International Journal of Advanced Manufacturing Technology, 96, 3127-3138. https://doi.org/10.1007/s00170-017-1506-4

• Ganesh, N., Shankar, R., Kalita, K., Jangir, P., Oliva, D., & Pérez-Cisneros, M. (2023). A novel decomposition-based multi-objective symbiotic organism search optimization algorithm. Mathematics, 11(8), Article 1898. https://doi.org/10.3390/math11081898

• Goudos, S. K., Deruyck, M., Plets, D., Martens, L., Psannis, K. E., Sarigiannidis, P., & Joseph, W. (2019). A novel design approach for 5G massive MIMO and NB-IoT green networks using a hybrid jaya-differential evolution algorithm. IEEE Access, 7, 105687-105700. https://doi.org/10.1109/ACCESS.2019.2932042

• Guo, Y., Liu, W., Sun, M., Xu, B., & Li, D. (2018). A method based on semi-solid forming for eliminating coarse dendrites and shrinkage porosity of H13 tool steel. Metals, 8(4), Article 277. https://doi.org/10.3390/met8040277

• Jangir, P., Buch, H., Mirjalili, S., & Manoharan, P. (2023). MOMPA: Multi-objective marine predator algorithm for solving multi-objective optimization problems. Evolutionary Intelligence, 16, 169-195. https://doi.org/10.1007/s12065-021-00649-z

• Ji, Z., & Xie, Z. (2008). Multi-objective optimization of continuous casting billet based on ant colony system algorithm. In 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application (Vol. 1, pp. 262-266). IEEE Publishing. https://doi.org/10.1109/PACIIA.2008.28

• Jian, X., & Weng, Z. (2020). A logistic chaotic JAYA algorithm for parameters identification of photovoltaic cell and module models. Optik, 203, Article 164041. https://doi.org/10.1016/j.ijleo.2019.164041

• Khosravi, H., Eslami-Farsani, R., & Askari-Paykani, M. (2014). Modeling and optimization of cooling slope process parameters for semi-solid casting of A356 Al alloy. Transactions of Nonferrous Metals Society of China (English Edition), 24(4), 961-968. https://doi.org/10.1016/S1003-6326(14)63149-6

• Kor, J., Chen, X., Sun, Z., & Hu, H. (2011). Casting design through multi-objective optimization. IFAC Proceedings, 44(1), 11642-11647. https://doi.org/10.3182/20110828-6-IT-1002.01726

• Kumar, S., Jangir, P., Tejani, G. G., Premkumar, M., & Alhelou, H. H. (2021). MOPGO: A new physics-based multi-objective plasma generation optimizer for solving structural optimization problems. IEEE Access, 9, 84982-85016. https://doi.org/10.1109/ACCESS.2021.3087739

• Kumar, S. D., Mandal, A., & Chakraborty, M. (2014). Cooling slope casting process of semi-solid aluminum alloys: A review. International Journal of Engineering Research & Technology (IJERT), 3(7), 269-283.

• Kumar, S. B., Idris, M. H., Farah, N. F. N., & Kamal, R. (2013). Investigation of mechanical properties of AZ91D magnesium alloy by gravity die casting process. Jurnal Mekanikal, 36, 1-9.

• Li, K., Chen, R., Fu, G., & Yao, X. (2019). Two-archive evolutionary algorithm for constrained multiobjective optimization. IEEE Transactions on Evolutionary Computation, 23(2), 303-315. https://doi.org/10.1109/TEVC.2018.2855411

• Li, S., Fan, X., Huang, H., & Cao, Y. (2020). Multi-objective optimization of injection molding parameters, based on the Gkriging-NSGA-vague method. Journal of Applied Polymer Science, 137(19), Article 48659. https://doi.org/10.1002/app.48659

• Mishra, P., & Sahu, A. (2018). Manufacturing process optimization using pso by optimal machine combination on cluster level. Materials Today: Proceedings, 5(9), 19200-19208. https://doi.org/10.1016/j.matpr.2018.06.275

• Nafisi, S., & Ghomashchi, R. (2019). Semi-solid processing of alloys and composites. Metals, 9(5), Article 526. https://doi.org/10.3390/met9050526

• Narayanan, R. C., Ganesh, N., Čep, R., Jangir, P., Chohan, J. S., & Kalita, K. (2023). A novel many-objective sine-cosine algorithm (MaOSCA) for engineering applications. Mathematics, 11(10), Article 2301. https://doi.org/10.3390/math11102301

• Onifade, M., Lawal, A. I., Aladejare, A. E., Bada, S., & Idris, M. A. (2022). Prediction of gross calorific value of solid fuels from their proximate analysis using soft computing and regression analysis. International Journal of Coal Preparation and Utilization, 42(4), 1170-1184. https://doi.org/10.1080/19392699.2019.1695605

• Pandya, S. B., Visumathi, J., Mahdal, M., Mahanta, T. K., & Jangir, P. (2022). A novel MOGNDO algorithm for security-constrained optimal power flow problems. Electronics, 11(22), Article 3825. https://doi.org/10.3390/electronics11223825

• Patel, G. C. M., Krishna, P., Vundavilli, P. R., & Parappagoudar, M. B. (2016a). Multi-objective optimization of squeeze casting process using genetic algorithm and particle swarm optimization. Archives of Foundry Engineering, 16(3), 172-186. https://doi.org/10.1515/afe-2016-0073

• Patel, G. C. M., Krishna, P., & Parappagoudar, M. B. (2016b). Modelling and multi-objective optimisation of squeeze casting process using regression analysis and genetic algorithm. Australian Journal of Mechanical Engineering, 14(3), 182-198. https://doi.org/10.1080/14484846.2015.1093231

• Patel, G. C. M., Krishna, P., & Parappagoudar, M. B. (2015). Modelling of squeeze casting process using design of experiments and response surface methodology. International Journal of Cast Metals Research, 28(3), 167-180. https://doi.org/10.1179/1743133614Y.0000000144

• Premkumar, M., Jangir, P., Sowmya, R., Elavarasan, R. M., & Kumar, B. S. (2021). Enhanced chaotic JAYA algorithm for parameter estimation of photovoltaic cell/modules. ISA Transactions, 116, 139-166. https://doi.org/10.1016/j.isatra.2021.01.045

• Premkumar, M., Jangir, P., Kumar, B. S., Alqudah, M. A., & Nisar, K. S. (2022). Multi-objective grey wolf optimization algorithm for solving real-world BLDC motor design problem. Computers, Materials & Continua, 70(2), 2435-2452. https://doi.org/10.32604/cmc.2022.016488

• Raed, A. Z., Mohammed, Z., Al, A., Awadallah, M. A., Doush, I. A., & Assaleh, K. (2022). An intensive and comprehensive overview of JAYA Algorithm, its versions and applications. Archives of Computational Methods in Engineering, 29(2), 763-792. https://doi.org/10.1007/s11831-021-09585-8

• Rao, R. V. (2011). Advanced modeling and optimization of manufacturing processes. Springer. https://doi.org/10.1007/978-0-85729-015-1

• Rao, R. V., Rai, D. P., Ramkumar, J., & Balic, J. (2016). A new multi-objective Jaya algorithm for optimization of modern machining processes. Advances in Production Engineering & Management, 11(4), 271-286. http://dx.doi.org/10.14743/apem2016.4.226

• Rao, R. Venkata, Keesari, H. S., Oclon, P., & Taler, J. (2019). Improved multi-objective Jaya optimization algorithm for a solar dish Stirling engine. Journal of Renewable and Sustainable Energy, 11(2), Article 025903. https://doi.org/10.1063/1.5083142

• Rao, R. V. (2018). Jaya: An advanced optimization algorithm and its engineering applications. Springer. https://doi.org/10.1007/978-3-319-78922-4

• Said, R. M., Sallehuddin, R., Mohd Radzi, N. H., & Mohd Kamal, M. R. (2021). Jaya algorithm for optimization of cooling slope casting process parameters. Journal of Physics: Conference Series, 2129(1), Article 012042. https://doi.org/10.1088/1742-6596/2129/1/012042

• Singh, A., Singh, R. M., Kumar, A. R. S., Kumar, A., Hanwat, S., & Tripathi, V. K. (2021). Evaluation of soft computing and regression-based techniques for the estimation of evaporation. Journal of Water and Climate Change, 12(1), 32-43. https://doi.org/10.2166/wcc.2019.101

• Son, Y. G., Jung, S. S., Park, Y. H., & Lee, Y. C. (2021). Effect of semi-solid processing on the microstructure and mechanical properties of aluminum alloy chips with eutectic Mg2Si intermetallics. Metals, 11(9), Article 1414. https://doi.org/10.3390/met11091414

• Tanvir, M. H., Hussain, A., Rahman, M. M. T., & Ishraq, S, Zishan, K., Rahul, S. T., & Habib, M. A. (2020). Multi-objective optimization of turning operation of stainless steel using a hybrid whale optimization algorithm. Journal of Manufacturing and Materials Processing, 4(3), Article 64. https://doi.org/10.3390/jmmp4030064

• Tavakolpour-Saleh, A. R., Zare, S. H., & Badjian, H. (2017). Multi-objective optimization of stirling heat engine using gray wolf optimization algorithm. International Journal of Engineering, 30(6), 150-160.

• Tugiman, T., Thayab, A., Ariani, F., Sitorus, T., Suhandi, S., & Rizki, R. (2019). The effect of cooling slope on mechanical properties of aluminum-8.5wt.% Si alloy produced by gravity casting. In Proceedings of the 2nd Annual Conference of Engineering and Implementation on Vocational Education (ACEIVE 2018) (pp. 1-7). EAI Publishing. https://doi.org/10.4108/eai.3-11-2018.2285718

• Vinh, L., & Nguyen, N. S. (2020). Parameters extraction of solar cells using modified JAYA algorithm. Optik, 203, Article 164034. https://doi.org/10.1016/j.ijleo.2019.164034

• Warid, W., Hizam, H., Mariun, N., & Wahab, N. I. A. (2018). A novel quasi-oppositional modified Jaya algorithm for multi-objective optimal power flow solution. Applied Soft Computing, 65, 360-373. https://doi.org/10.1016/j.asoc.2018.01.039

• Wu, C., & He, Y. (2020). Solving the set-union knapsack problem by a novel hybrid Jaya algorithm. Soft Computing, 24, 1883-1902. https://doi.org/10.1007/s00500-019-04021-3

• Wu, H., Yang, X., Cao, G., Zhao, L., & Yang, L. (2021). Design and optimisation of die casting process for heavy-duty automatic transmission oil circuit board. International Journal of Cast Metals Research, 31(2), 88-96. https://doi.org/10.1080/13640461.2021.1904673

• Zamli, K., Alsewari, A., & S. Ahmed, B. (2018). Multi-start jaya algorithm for software module clustering problem. Azerbaijan Journal of High Performance Computing, 1(1), 87-112.

• Zheng, K., Lin, Y., Chen, W., & Liu, L. (2020). Numerical simulation and optimization of casting process of copper alloy water-meter shell. Advances in Mechanical Engineering, 12(5), 1-12. https://doi.org/10.1177/1687814020923450

• Zhenghao, Li, J., & Hao, H. (2020). Non-probabilistic method to consider uncertainties in structural damage identification based on hybrid Jaya and tree seeds algorithm. Engineering Structures, 220, Article 110925. https://doi.org/10.1016/j.engstruct.2020.110925

• Zhou, D., Kang, Z., Yang, C., Su, X., & Chen, C. C. (2022). A novel approach to model and optimize qualities of castings produced by differential pressure casting process. International Journal of Metalcasting, 16, 259-277. https://doi.org/10.1007/s40962-021-00596-6

• Zitzler, E., Deb, K., & Thiele, L. (1999). Comparison of multiobjective evolutionary algorithms: empirical results. Evolutionary Computation, 8(2), 173-195. https://doi.org/10.1162/106365600568202