Keywords and phrases: multi-objective optimization, NSGA III, Pareto-optimal solutions, performance metrics, opposition-based learning, convergence, diversity.
Received: October 20, 2022; Revised: November 23, 2022; Accepted: December 28, 2022; Published: January 12, 2023
How to cite this article: Shilpi Jain and Kamlesh Kumar Dubey, Analysis of benefits of integrating the opposition based learning technique into non-dominated sorting genetic algorithm III, Advances and Applications in Discrete Mathematics 36 (2023), 93-119. http://dx.doi.org/10.17654/0974165823007
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
References:
[1] N. Gunantara, A review of multi-objective optimization: methods and its applications, Cogent Eng. 5(1) (2018), 1-16. doi:10.1080/23311916.2018.1502242. [2] J. H. Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence, 1975. [3] N. Srinivas and K. Deb, Multiobjective optimization using nondominated sorting in genetic algorithms, Evol. Comput. 2 (1994), 221-248. doi:10.1162/evco.1994.2.3.221. [4] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput. 6 (2002), 182-197. doi:10.1109/4235.996017. [5] K. Deb and H. Jain, 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 (2014), 577-601. doi:10.1109/TEVC.2013.2281535. [6] K. Sharma and M. K. Trivedi, Latin hypercube sampling-based NSGA-III optimization model for multimode resource constrained time-cost-quality-safety trade-off in construction projects, Int. J. Constr. Manag. 22 (2022), 3158-3168. doi:10.1080/15623599.2020.1843769. [7] H. R. Tizhoosh, Opposition-based learning: a new scheme for machine intelligence, Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet, 2005. doi:10.1109/cimca.2005.1631345. [8] M. Y. Cheng and D. H. Tran, Opposition-based multiple-objective differential evolution to solve the time-cost-environment impact trade-off problem in construction projects, J. Comput. Civ. Eng. 29 (2015). doi:10.1061/(ASCE)CP.1943-5487.0000386. [9] I. Das and J. E. Dennis, Normal-boundary intersection: a new method for generating the Pareto surface in nonlinear multicriteria optimization problems, SIAM J. Optim. 8 (1998). doi:10.1137/S1052623496307510. [10] R. Storn and K. Price, Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces, J. Global Optim. 11 (1997), 341-359. doi:10.1023/A:1008202821328. [11] K. Deb and R. B. Agrawal, Simulated binary crossover for continuous search space, Complex Syst. 9 (1995), 115-148. doi:10.1.1.26.8485Cached. [12] S. Rahnamayan, H. R. Tizhoosh and M. M. Salama, Opposition-based differential evolution, Stud. Comput. Intell.143 (2008), 155-171. doi:10.1007/978-3-540-68830-3_6. [13] A. Panwar and K. N. Jha, A many-objective optimization model for construction scheduling, Constr. Manag. Econ. 37 (2019), 727-739. doi:10.1080/01446193.2019.1590615. [14] F. Habibi, F. Barzinpour and S. J. Sadjadi, Resource-constrained project scheduling problem: review of past and recent developments, J. Proj. Manag. 3 (2018), 55-88. doi:10.5267/j.jpm.2018.1.005. [15] S. Tiwari and S. Johari, Project scheduling by integration of time cost trade-off and constrained resource scheduling, J. Inst. Eng. Ser. A 96 (2015), 37-46. doi:10.1007/s40030-014-0099-2. [16] K. Feng, W. Lu, S. Chen and Y. Wang, An integrated environment-cost-time optimisation method for construction contractors considering global warming, Sustain. 10(11) (2018), 1-22. doi:10.3390/su10114207. [17] D. L. Luong, D. H. Tran and P. T. Nguyen, Optimizing multi-mode time-cost-quality trade-off of construction project using opposition multiple objective difference evolution. Int. J. Constr. Manag. 21 (2021), 271-283. doi:10.1080/15623599.2018.1526630. [18] K. El-Rayes and A. Kandil, Time-cost-quality trade-off analysis for highway construction, J. Constr. Eng. Manag. 131(4) (2005). doi: 10.1061/(ASCE)0733-9364(2005)131:4(477). [19] A. Afshar and H. R. Z. Dolabi, Multi-objective optimization of time-cost-safety using genetic algorithm, Int. J. Optim. Civ. Eng. 4 (2014), 433-450. [20] E. Elbeltagi, M. Ammar, H. Sanad and M. Kassab, Overall multiobjective optimization of construction projects scheduling using particle swarm, Eng. Constr. Archit. Manag. 23 (2016), 265-282. doi: 10.1108/ECAM-11-2014-0135. [21] G. Singh, A. T. Ernst, Resource constraint scheduling with a fractional shared resource, Oper. Res. Lett. 39(5) (2011), 363-368. doi:10.1016/j.orl.2011.06.003 [22] K. Khalili-Damghani, M. Tavana, A. R. Abtahi and F. J. Santos Arteaga, Solving multi-mode time-cost-quality trade-off problems under generalized precedence relations, Optim. Methods Softw. 30 (2015), 965-1001. doi:10.1080/10556788.2015.1005838. [23] K. Deb and M. Goyal, A combined genetic adaptive search (GeneAS) for engineering design, Comput. Sci. Informatics 26 (1996), 30-45. doi: citeulike-article-id:9625478. [24] J. C. Ferreira, C. M. Fonseca and A. Gaspar-Cunha, Methodology to select solutions from the Pareto-optimal set: a comparative study, Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference, 2007. doi:10.1145/1276958.1277117. [25] S. E. Elmaghraby, Activity Networks: Project Planning and Control by Network Models, John Wiley & Sons, New York, 1977.
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