How to cite this paper
Buch, H & Trivedi, I. (2020). A new non-dominated sorting ions motion algorithm: Development and applications.Decision Science Letters , 9(1), 59-76.
Refrences
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Coello Coello, C. A., & Lechuga, M. S. (2002). MOPSO: A proposal for multiple objective particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, 2, 1051–1056.
Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, LTD, p. 497.
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Parallel Problem Solving from Nature PPSN VI, 849–858.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Horn, J, Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference On, 1, 82–87.
Horn, Jeffrey, & Nafpliotis, N. (1993). Multiobjective Optimization Using The Niched Pareto Genetic Algorithm. IlliGAL Report No. 93005, 82–87.
Javidy, B., Hatamlou, A., & Mirjalili, S. (2015). Ions motion algorithm for solving optimization problems. Applied Soft Computing Journal, 32, 72–79.
Mahfoud, S. (1995). Niching methods for genetic algorithms. Urbana, (95001), 251.
Mirjalili, S. (2016). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073.
Mirjalili, S., Jangir, P., Mirjalili, S. Z., Saremi, S., & Trivedi, I. N. (2017). Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2017.07.018
Mirjalili, S., Jangir, P., & Saremi, S. (2017). Multi-objective ant lion optimizer: A multi-objective optimization algorithm for solving engineering problems. Applied Intelligence, 46(1), 79–95.
Mirjalili, S. M., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106–119.
Rao, R. V., Rai, D. P., & Balic, J. (2017). A multi-objective algorithm for optimization of modern machining processes. Engineering Applications of Artificial Intelligence, 61, 103–125.
Sadollah, A., Eskandar, H., & Kim, J. H. (2015). Water cycle algorithm for solving constrained multi-objective optimization problems. Applied Soft Computing, 27, 279–298.
Savsani, V., & Tawhid, M. A. (2017). Non-dominated sorting moth flame optimization (NS-MFO) for multi-objective problems. Engineering Applications of Artificial Intelligence, 63, 20–32.
Schott, J. R. (1995). Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization.
Sierra, M. R., & Coello Coello, C. A. (2005). Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance. https://doi.org/10.1007/978-3-540-31880-4_35
Tawhid, M. A., & Savsani, V. (2017). Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems. Neural Computing and Applications, 1–15.
Veldhuizen, D. A. Van, Van Veldhuizen, D. A., & Lamont, G. B. (1998). Multiobjective Evolutionary Algorithm Research: A History and Analysis.
Venkata Rao, R., & Patel, V. (2014). A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems. International Journal of Industrial Engineering Computations, 1–22.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82.
Yang, X.-S. (2011). Bat algorithm for multi-objective optimisation. International Journal of Bio-Inspired Computation, 3(5), 267–274. https://doi.org/10.1504/IJBIC.2011.042259
Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-Report, 103.
Branke, J., Kaußler, T., & Schmeck, H. (2001). Guidance in evolutionary multi-objective optimization. Advances in Engineering Software, 32(6), 499–507.
Coello Coello, C. A., & Lechuga, M. S. (2002). MOPSO: A proposal for multiple objective particle swarm optimization. Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, 2, 1051–1056.
Deb, K. (2001). Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, LTD, p. 497.
Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. Parallel Problem Solving from Nature PPSN VI, 849–858.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Horn, J, Nafpliotis, N., & Goldberg, D. E. (1994). A niched Pareto genetic algorithm for multiobjective optimization. Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference On, 1, 82–87.
Horn, Jeffrey, & Nafpliotis, N. (1993). Multiobjective Optimization Using The Niched Pareto Genetic Algorithm. IlliGAL Report No. 93005, 82–87.
Javidy, B., Hatamlou, A., & Mirjalili, S. (2015). Ions motion algorithm for solving optimization problems. Applied Soft Computing Journal, 32, 72–79.
Mahfoud, S. (1995). Niching methods for genetic algorithms. Urbana, (95001), 251.
Mirjalili, S. (2016). Dragonfly algorithm: A new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Computing and Applications, 27(4), 1053–1073.
Mirjalili, S., Jangir, P., Mirjalili, S. Z., Saremi, S., & Trivedi, I. N. (2017). Optimization of problems with multiple objectives using the multi-verse optimization algorithm. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2017.07.018
Mirjalili, S., Jangir, P., & Saremi, S. (2017). Multi-objective ant lion optimizer: A multi-objective optimization algorithm for solving engineering problems. Applied Intelligence, 46(1), 79–95.
Mirjalili, S. M., Saremi, S., Mirjalili, S. M., & Coelho, L. D. S. (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization. Expert Systems with Applications, 47, 106–119.
Rao, R. V., Rai, D. P., & Balic, J. (2017). A multi-objective algorithm for optimization of modern machining processes. Engineering Applications of Artificial Intelligence, 61, 103–125.
Sadollah, A., Eskandar, H., & Kim, J. H. (2015). Water cycle algorithm for solving constrained multi-objective optimization problems. Applied Soft Computing, 27, 279–298.
Savsani, V., & Tawhid, M. A. (2017). Non-dominated sorting moth flame optimization (NS-MFO) for multi-objective problems. Engineering Applications of Artificial Intelligence, 63, 20–32.
Schott, J. R. (1995). Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization.
Sierra, M. R., & Coello Coello, C. A. (2005). Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance. https://doi.org/10.1007/978-3-540-31880-4_35
Tawhid, M. A., & Savsani, V. (2017). Multi-objective sine-cosine algorithm (MO-SCA) for multi-objective engineering design problems. Neural Computing and Applications, 1–15.
Veldhuizen, D. A. Van, Van Veldhuizen, D. A., & Lamont, G. B. (1998). Multiobjective Evolutionary Algorithm Research: A History and Analysis.
Venkata Rao, R., & Patel, V. (2014). A multi-objective improved teaching-learning based optimization algorithm for unconstrained and constrained optimization problems. International Journal of Industrial Engineering Computations, 1–22.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82.
Yang, X.-S. (2011). Bat algorithm for multi-objective optimisation. International Journal of Bio-Inspired Computation, 3(5), 267–274. https://doi.org/10.1504/IJBIC.2011.042259
Zitzler, E., Laumanns, M., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm. TIK-Report, 103.