How to cite this paper
Buch, H & Trivedi, I. (2021). Ions motion optimization algorithm for multiobjective optimization problems.Decision Science Letters , 10(2), 93-110.
Refrences
Akbari, R., Hedayatzadeh, R., Ziarati, K., & Hassanizadeh, B. (2012). A multi-objective artificial bee colony algorithm. Swarm and Evolutionary Computation, 2, 39-52.
Buch, H., & Trivedi, I. (2020). A new non-dominated sorting ions motion algorithm: Development and applications. Decision Science Letters, 9(1), 59-76.
Coello, C. A. C. (2011). An introduction to multi-objective particle swarm optimizers. In Soft computing in industrial applications (pp. 3-12). Springer, Berlin, Heidelberg.
Coello, C. A. C., & Cortés, N. C. (2005). Solving multiobjective optimization problems using an artificial immune system. Genetic Programming and Evolvable Machines, 6(2), 163-190.
Coello, C. C., & Lechuga, M. S. (2002, May). MOPSO: A proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600) (Vol. 2, pp. 1051-1056). IEEE.
Coello, C. A. C. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41(2), 113-127.
Coello, C. A. C. (2009). Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored. Frontiers of Computer Science in China, 3(1), 18-30.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (Vol. 16). John Wiley & Sons.
Deb, Kalyanmoy, Samir Agrawal, Amrit Pratap, and T Meyarivan. 2000. “A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II.” In Parallel Problem Solving from Nature PPSN VI, eds. Marc Schoenauer et al. Berlin, Heidelberg: Springer Berlin Heidelberg, 849–58.
Dorigo, M., & Di Caro, G. (1999, July). Ant colony optimization: a new meta-heuristic. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 2, pp. 1470-1477). IEEE.
Javidy, B., Hatamlou, A., & Mirjalili, S. (2015). Ions motion algorithm for solving optimization problems. Applied Soft Computing, 32, 72-79.
Knowles, J., & Corne, D. (1999, July). The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) (Vol. 1, pp. 98-105). IEEE.
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, 134, 50-71.
Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191.
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., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
Mirjalili, S., 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.
Mirjalili, S. Z., Mirjalili, S., Saremi, S., Faris, H., & Aljarah, I. (2018). Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence, 48(4), 805-820.
Ngatchou, P., Zarei, A., & El-Sharkawi, A. (2005, November). Pareto multi objective optimization. In Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems (pp. 84-91). IEEE.
Schott, J. R. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization (No. AFIT/CI/CIA-95-039). Air Force Inst of Tech Wright-Patterson AFB OH.
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248.
Van Veldhuizen, D. A. (1999). Multiobjective Evolutionary Algorithms: Classifications, Analyses and New Innovations. IRE Transactions on Education.
Nanda, S. J. (2016, September). Multi-objective moth flame optimization. In 2016 International conference on Advances in computing, communications and informatics (ICACCI) (pp. 2470-2476). IEEE.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.
Zhang, M., Wang, H., Cui, Z., & Chen, J. (2018). Hybrid multi-objective cuckoo search with dynamical local search. Memetic Computing, 10(2), 199-208.
Zou, F., Wang, L., Hei, X., Chen, D., & Wang, B. (2013). Multi-objective optimization using teaching-learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26(4), 1291-1300.
Buch, H., & Trivedi, I. (2020). A new non-dominated sorting ions motion algorithm: Development and applications. Decision Science Letters, 9(1), 59-76.
Coello, C. A. C. (2011). An introduction to multi-objective particle swarm optimizers. In Soft computing in industrial applications (pp. 3-12). Springer, Berlin, Heidelberg.
Coello, C. A. C., & Cortés, N. C. (2005). Solving multiobjective optimization problems using an artificial immune system. Genetic Programming and Evolvable Machines, 6(2), 163-190.
Coello, C. C., & Lechuga, M. S. (2002, May). MOPSO: A proposal for multiple objective particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No. 02TH8600) (Vol. 2, pp. 1051-1056). IEEE.
Coello, C. A. C. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41(2), 113-127.
Coello, C. A. C. (2009). Evolutionary multi-objective optimization: some current research trends and topics that remain to be explored. Frontiers of Computer Science in China, 3(1), 18-30.
Deb, K. (2001). Multi-objective optimization using evolutionary algorithms (Vol. 16). John Wiley & Sons.
Deb, Kalyanmoy, Samir Agrawal, Amrit Pratap, and T Meyarivan. 2000. “A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II.” In Parallel Problem Solving from Nature PPSN VI, eds. Marc Schoenauer et al. Berlin, Heidelberg: Springer Berlin Heidelberg, 849–58.
Dorigo, M., & Di Caro, G. (1999, July). Ant colony optimization: a new meta-heuristic. In Proceedings of the 1999 congress on evolutionary computation-CEC99 (Cat. No. 99TH8406) (Vol. 2, pp. 1470-1477). IEEE.
Javidy, B., Hatamlou, A., & Mirjalili, S. (2015). Ions motion algorithm for solving optimization problems. Applied Soft Computing, 32, 72-79.
Knowles, J., & Corne, D. (1999, July). The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. In Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) (Vol. 1, pp. 98-105). IEEE.
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, 134, 50-71.
Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163-191.
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., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: a nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495-513.
Mirjalili, S., 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.
Mirjalili, S. Z., Mirjalili, S., Saremi, S., Faris, H., & Aljarah, I. (2018). Grasshopper optimization algorithm for multi-objective optimization problems. Applied Intelligence, 48(4), 805-820.
Ngatchou, P., Zarei, A., & El-Sharkawi, A. (2005, November). Pareto multi objective optimization. In Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems (pp. 84-91). IEEE.
Schott, J. R. (1995). Fault tolerant design using single and multicriteria genetic algorithm optimization (No. AFIT/CI/CIA-95-039). Air Force Inst of Tech Wright-Patterson AFB OH.
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary computation, 2(3), 221-248.
Van Veldhuizen, D. A. (1999). Multiobjective Evolutionary Algorithms: Classifications, Analyses and New Innovations. IRE Transactions on Education.
Nanda, S. J. (2016, September). Multi-objective moth flame optimization. In 2016 International conference on Advances in computing, communications and informatics (ICACCI) (pp. 2470-2476). IEEE.
Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.
Zhang, M., Wang, H., Cui, Z., & Chen, J. (2018). Hybrid multi-objective cuckoo search with dynamical local search. Memetic Computing, 10(2), 199-208.
Zou, F., Wang, L., Hei, X., Chen, D., & Wang, B. (2013). Multi-objective optimization using teaching-learning-based optimization algorithm. Engineering Applications of Artificial Intelligence, 26(4), 1291-1300.