International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 17, No. 6, 8 Dec. 2025
Cover page and Table of Contents: PDF (size: 1232KB)
Enhanced NSGA-II Algorithm for Solving Real-world Multi-objective Optimization Problems
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Author(s)
Index Terms
Meta-heuristics, Multi-objective Optimization Problems, Non-dominated Sorting Genetic Algorithm-II, Evolutionary Optimization, Time-cost Tradeoff, Sobol Sequences
Abstract
Multi-objective optimization problems are crucial in real-world scenarios, where multiple solutions exist rather than a single one. Traditional methods like PERT/CPM often struggle to address such problems effectively. Meta- heuristic techniques, such as genetic algorithms and non-dominated sorting genetic algorithms (NSGA-II), are well- suited for finding true Pareto-optimal solutions. This paper introduces an enhanced NSGA-II algorithm, which utilizes Sobol sequences for initial population generation, ensuring uniform search space coverage and faster convergence. The proposed algorithm is validated using benchmark problems from the ZDT test suite and compared with state-of-the- art algorithms. Additionally, real-world optimization problems in project management, particularly the time-cost trade- off (TCT) problem, are solved using the enhanced NSGA-II. The performance evaluation includes key metrics such as standard deviation, providing a comprehensive assessment of the algorithm’s efficiency. Experimental results confirm that the proposed method outperforms traditional NSGA-II and other meta-heuristic algorithms in maintaining a well- distributed Pareto front while ensuring computational efficiency.
Cite This Paper
Muskan Kapoor, Bhupendra Kumar Pathak, Rajiv Kuamr, "Enhanced NSGA-II Algorithm for Solving Real-world Multi-objective Optimization Problems", International Journal of Intelligent Systems and Applications(IJISA), Vol.17, No.6, pp.105-117, 2025. DOI:10.5815/ijisa.2025.06.08
Reference
[1]K. Deb, “Multi-objective Optimisation Using Evolutionary Algorithms: An Introduction,” in: Wang, L., Ng, A., Deb, K. (eds) Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, Springer, London, 2011. DOI: 10.1007/978-0-85729-652-8-1.
[2]A. S. Patil, A. K. Agarwal, K. Sharma, and M. K. Trivedi, “Time-cost trade-off optimization model for retrofitting planning projects using MOGA,” Asian Journal of Civil Engineering, 2024. DOI: 10.1007/s42107-024-01014-y.
[3]P. K. Mandal, “A review of classical methods and Nature-Inspired Algorithms (NIAs) for optimization problems,” Results in Control and Optimization, vol. 100315, 2023.
[4]A. Kaveh, “Applications of Metaheuristic Optimization Algorithms in Civil Engineering,” Springer, 2016. DOI: 10.1007/978-3-319-48012-1.
[5]S. Sharma and V. Kumar, “A comprehensive review on multi-objective optimization techniques: Past, present and future,” Archives of Computational Methods in Engineering, vol. 29, no. 7, pp. 5605–5633, 2022.
[6]G. Karakaya, “Editorial for ‘Recent Advances and Applications of Multi-objective Optimization’,” EURO Journal on Decision Processes, Article ID 100049, 2024.
[7]G. Ulusoy and O¨.. Hazır, “The Time/Cost Trade-off Problems,” in An Introduction to Project Modeling and Planning, pp. 141–165, 2021.
[8]M. Shahriari, “Multi-objective optimization of discrete time–cost tradeoff problem in project networks using non- dominated sorting genetic algorithm,” Journal of Industrial Engineering International, vol. 12, pp. 159–169, 2016.
[9]C. W. Feng, L. Liu, and S. A. Burns, “Using Genetic Algorithms to Solve Construction Time-Cost Trade-Off Problems,” Journal of Computing in Civil Engineering, vol. 11, pp. 184–189, 1997.
[10]P. De, E. J. Dunne, J. B. Ghosh, and C. E. Wells, “The discrete time-cost tradeoff problem revisited,” European Journal of Operational Research, vol. 81, pp. 225–238, 1995.
[11]D. E. Goldberg, “Genetic algorithms in search optimization and machine learning,” Addison-Wesley, 1989.
[12]E. Zitzler and L. Thiele, “Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach,” IEEE Transactions on Evolutionary Computation, vol. 3, pp. 257–271, 1999.
[13]B. K. Pathak, S. Srivastava, and K. Srivastava, “Neural network embedded multiobjective genetic algorithm to solve non-linear time-cost tradeoff problems of project scheduling,” Journal of Scientific and Industrial Research, vol. 67, pp. 124–131, 2008.
[14]D. Agdas, D. J. Warne, J. Osio-Norgaard, and F. J. Masters, “Utility of Genetic Algorithms for Solving Large-Scale Construction Time-Cost Trade-Off Problems,” Journal of Computing in Civil Engineering, vol. 32, 2018.
[15]K. M. Kumar, D. Agrawal, V. K. Vishwakarma, and M. A. Eirgash, “Development of time-cost trade-off optimization model for Indian highway construction projects using non-dominated sorting genetic algorithm-II methodology,” Asian Journal of Civil Engineering, vol. 25, no. 8, pp. 5975–5988, 2024.
[16]A. P. Chassiakos and P. K. Tsikas, “Time-cost trade-off optimization at different project sizes,” in Proceedings of the Eleventh International Conference on Engineering Computational Technology, vol. 2, Paper 14.2, Civil-Comp Press, Edinburgh, UK, 2022. DOI: 10.4203/ccc.2.14.2.
[17]A. Dhawan, K. Sharma, and M. K. Trivedi, “Simulated Annealing-Based Time–Cost Trade-Off Model for Construction,” presented at ICSISCET-2020, 2020.
[18]K. Sharma, S. Gwalior, M. Trivedi, and S. Gwalior, “AHP and NSGA-II based Time-Cost-Quality Trade-Off Optimization model for Construction,” presented at ICSISCET-2020, 2020.
[19]B. K. Pathak and S. Srivastava, “Integrated Fuzzy-HMH for project uncertainties in time-cost tradeoff problem,”Applied Soft Computing Journal, vol. 21, pp. 320–329, 2014.
[20]G. Albayrak, “Novel Hybrid Method in Time–Cost Trade-Off for Resource-Constrained Construction Projects,” Iranian Journal of Science and Technology, Transactions of Civil Engineering, vol. 44, pp. 1295–1307, 2020.
[21]Ankita and S. Kumar Sahana, “Evolutionary based hybrid GA for solving multi-objective grid scheduling problem,”Microsystems Technology, vol. 26, pp. 1405–1416, 2020.
[22]N. Hamta, M. Ehsanifar, and J. Sarikhani, “Presenting a goal programming model in the time-cost-quality trade-off,” International Journal of Construction Management, vol. 21, pp. 1–11, 2021.
[23]Z. Liang and Z. Wang, “Enhancing population diversity based gaining-sharing knowledge based algorithm for global optimization and engineering design problems,” Expert Systems with Applications, Article ID 123958, 2024.
[24]K. Deb, “Multi-objective genetic algorithms: Problem difficulties and construction of test problems,” Evolutionary Computation, vol. 7, pp. 205–230, 1999.
[25]K. Deb, A. Pratap, S. Agarwal, and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,”IEEE Transactions on Evolutionary Computation, vol. 6, pp. 182–197, 2002.
[26]K. Dev, and R. B. Agrawal, ‘Simulated binary crossover for continuous search space,” Complex systems, vol. 9, no. 2, pp. 115-148, 1995.
[27]K. Deb, “Multi-objective optimization using evolutionary algorithms,” Wiley, New York, 2001.
[28] S. Sharma, B. K. Pathak, and R. Kumar, “Adopting an Improved Genetic Algorithm for Multi-Objective Service Composition Optimization in Smart Agriculture,” Austrian Journal of Statistics, vol. 53, no. 5, pp. 11–25, 2024.
[29]T. Göçken, “Solution of fuzzy multi-objective project crashing problem,” Neural Computing and Applications, vol. 23, pp. 2167–2175, 2013.