|
Baswaraj, S. A., & Rao, M. S. (2020). Optimization of Parameters for Steel Recycling Process by Using Particle Swarm Optimization Algorithm. In Advanced Engineering Optimization Through Intelligent Techniques (pp. 87-93): Springer. Bensingh, R. J., Machavaram, R., Boopathy, S. R., & Jebaraj, C. (2019). Injection molding process optimization of a bi-aspheric lens using hybrid artificial neural networks (ANNs) and particle swarm optimization (PSO). Measurement, 134, 359-374. doi:https://doi.org/10.1016/j.measurement.2018.10.066 Chang, S., Shiu, R. S., & Wu, I. C. (2019). Applying an A-Star Search Algorithm for Generating the Minimized Material Scheme for the Rebar Quantity Takeoff. In ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction (Vol. 36, pp. 812-817). IAARC Publications. Chowdhury, S., Marufuzzaman, M., Tunc, H., Bian, L., & Bullington, W. (2019). A modified Ant Colony Optimization algorithm to solve a dynamic traveling salesman problem: A case study with drones for wildlife surveillance. Journal of Computational Design and Engineering, 6(3), 368-386. doi:https://doi.org/10.1016/j.jcde.2018.10.004 Cobo, Á., Llorente, I., Luna, L., & Luna, M. (2019). A decision support system for fish farming using particle swarm optimization. Computers and Electronics in Agriculture, 161, 121-130. doi:https://doi.org/10.1016/j.compag.2018.03.036 Dawes, J. (2008). Do data characteristics change according to the number of scale points used? An experiment using 5-point, 7-point and 10-point scales. International journal of market research, 50(1), 61-104. Di Francescomarino, C., Dumas, M., Federici, M., Ghidini, C., Maggi, F. M., Rizzi, W., & Simonetto, L. (2018). Genetic algorithms for hyperparameter optimization in predictive business process monitoring. Information Systems, 74, 67-83. doi:https://doi.org/10.1016/j.is.2018.01.003 Dorigo, M., Maniezzo, V., & Colorni, A. (1996). Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), 29-41. doi:10.1109/3477.484436 Faghihi, V., Reinschmidt, K. F., & Kang, J. H. (2014). Construction scheduling using Genetic Algorithm based on Building Information Model. Expert Systems with Applications, 41(16), 7565-7578. doi:https://doi.org/10.1016/j.eswa.2014.05.047 Fernandes Junior, F. E., & Yen, G. G. (2019). Particle swarm optimization of deep neural networks architectures for image classification. Swarm and Evolutionary Computation, 49, 62-74. doi:https://doi.org/10.1016/j.swevo.2019.05.010 Fidanova, S., Roeva, O., Luque, G., & Paprzycki, M. (2020). InterCriteria Analysis of Different Hybrid Ant Colony Optimization Algorithms for Workforce Planning. In Recent Advances in Computational Optimization (pp. 61-81): Springer. Gholizadeh, S., & Fattahi, F. (2014). Design optimization of tall steel buildings by a modified particle swarm algorithm. The Structural Design of Tall and Special Buildings, 23(4), 285-301. doi:10.1002/tal.1042 Goldberg, D. E. (2006). Genetic algorithms. Pearson Education India. Hackl, J., Adey, B. T., & Lethanh, N. (2018). Determination of Near-Optimal Restoration Programs for Transportation Networks Following Natural Hazard Events Using Simulated Annealing. Computer-Aided Civil and Infrastructure Engineering, 33(8), 618-637. doi:10.1111/mice.12346 Ho, C.-H., Chang, P.-T., Hung, K.-C., Lin, K.-P. J. I. M., & Systems, D. (2019). Developing intuitionistic fuzzy seasonality regression with particle swarm optimization for air pollution forecasting. Industrial Management & Data Systems, 119(3), 561-577. doi: https://doi.org/10.1108/IMDS-02-2018-0063 Hocine, A., Zhuang, Z. Y., Kouaissah, N., & Li, D. C. (2020). Weighted-additive fuzzy multi-choice goal programming (WA-FMCGP) for supporting renewable energy site selection decisions. European Journal of Operational Research. 285(2), 642-654. doi: https://doi.org/10.1016/j.ejor.2020.02.009 Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-International Conference on Neural Networks (Vol. 4, pp. 1942-1948). Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. J. s. (1983). Optimization by simulated annealing. Science, 220(4598), 671-680. doi: 10.1126 / science.220.4598.671 Korman, T. M., & Tatum, C. (2001). Development of a knowledge-based system to improve mechanical, electrical, and plumbing coordination. Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22 140, 55-55. doi: https://psycnet.apa.org/record/1933-01885-001 Mangal, M., & Cheng, J. C. P. (2018). Automated optimization of steel reinforcement in RC building frames using building information modeling and hybrid genetic algorithm. Automation in Construction, 90, 39-57. doi:https://doi.org/10.1016/j.autcon.2018.01.013 Moeini, R., & Afshar, M. H. (2019). Extension of the Hybrid Ant Colony Optimization Algorithm for Layout and Size Optimization of Sewer Networks. Journal of Environmental Informatics, 33(2). Nemati, M., Braun, M., & Tenbohlen, S. (2018). Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming. Applied Energy, 210, 944-963. doi:https://doi.org/10.1016/j.apenergy.2017.07.007 Putha, R., Quadrifoglio, L., & Zechman, E. (2012). Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions. Computer-Aided Civil and Infrastructure Engineering, 27(1), 14-28. doi:10.1111/j.1467-8667.2010.00715.x Rezaeeian, A., Davoodi, M., & Jafari, M. K. (2019). Determination of optimum cross-section of earth dams using ant colony optimization algorithm. Scientia Iranica, 26(3), 1104-1121. doi:10.24200/sci.2018.21078 Riley, D. R., Varadan, P., James, J. S., Thomas, H. R. J. J. o. C. E., & Management. (2005). Benefit-cost metrics for design coordination of mechanical, electrical, and plumbing systems in multistory buildings. Journal of construction engineering and management, 131(8), 877-889. Singh, M. M., Sawhney, A., & Borrmann, A. (2019). Integrating rules of modular coordination to improve model authoring in BIM. International Journal of Construction Management, 19(1), 15-31. Stochino, F., & Lopez Gayarre, F. J. A. S. (2019). Reinforced Concrete Slab Optimization with Simulated Annealing. Applied Sciences, 9(15), 3161. doi: https://doi.org/10.3390/app9153161 Torre, G., Fernández-Lugo, S., Guarino, R., & Fernández-Palacios, J. M. (2019). Network analysis by simulated annealing of taxa and islands of Macaronesia (North Atlantic Ocean). Ecography, 42(4), 768-779. doi:10.1111/ecog.03909 Vairavamoorthy, K., & Ali, M. (2000). Optimal Design of Water Distribution Systems Using Genetic Algorithms. Computer-Aided Civil and Infrastructure Engineering, 15(5), 374-382. doi:10.1111/0885-9507.00201 Wang, J., Wang, X., Shou, W., Chong, H.-Y., & Guo, J. (2016). Building information modeling-based integration of MEP layout designs and constructability. Automation in Construction, 61, 134-146. doi:https://doi.org/10.1016/j.autcon.2015.10.003 Wei, L., Zhang, Z., Zhang, D., & Leung, S. C. H. (2018). A simulated annealing algorithm for the capacitated vehicle routing problem with two-dimensional loading constraints. European Journal of Operational Research, 265(3), 843-859. doi:https://doi.org/10.1016/j.ejor.2017.08.035 Xu, C., Gordan, B., Koopialipoor, M., Armaghani, D. J., Tahir, M. M., & Zhang, X. (2019). Improving Performance of Retaining Walls Under Dynamic Conditions Developing an Optimized ANN Based on Ant Colony Optimization Technique. IEEE Access, 7, 94692-94700. doi:10.1109/ACCESS.2019.2927632 Zang, W., Ren, L., Zhang, W., & Liu, X. (2018). A cloud model based DNA genetic algorithm for numerical optimization problems. Future Generation Computer Systems, 81, 465-477. doi:https://doi.org/10.1016/j.future.2017.07.036 Zeferino, J. A., Antunes, A. P., & Cunha, M. C. (2009). An Efficient Simulated Annealing Algorithm for Regional Wastewater System Planning. Computer-Aided Civil and Infrastructure Engineering, 24(5), 359-370. doi:10.1111/j.1467-8667.2009.00594.x Zhang, W., Maleki, A., Rosen, M. A., & Liu, J. (2018). Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage. Energy, 163, 191-207. doi:https://doi.org/10.1016/j.energy.2018.08.112 Zhuang, Z. Y., Lin, C. C., Chen, C. Y., & Su, C. R. (2018). Rank-based comparative research flow benchmarking the effectiveness of AHP–GTMA on aiding decisions of shredder selection by reference to AHP–TOPSIS. Applied Sciences, 8(10), 1974. doi: https://doi.org/10.3390/app8101974
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