1. Faris, H., Aljarah, I., Al-Betar, M. A., & Mirjalili, S. (2017). Grey wolf optimizer: A review of recent variants and applications.Neural Computing and Applications, 1–23.

   Google Scholar 

2. Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing.Science,220(4598), 671–680.

   Google Scholar 

3. Holland, J. H. (1992). Genetic algorithms.Scientific American,267(1), 66–73.

   Article  Google Scholar 

4. Storn, R., & Price, K. (1997). Differential evolutiona simple and efficient heuristic for global optimization over continuous spaces.Journal of Global Optimization,11(4), 341–359.

   Article MathSciNet  Google Scholar 

5. Dorigo, M., & Birattari, M. (2011). Ant colony optimization. InEncyclopedia of machine learning (pp. 36–39). Boston: Springer.

   Google Scholar 

6. Kennedy, J. (2011). Particle swarm optimization. InEncyclopedia of machine learning (pp. 760–766). Boston: Springer.

   Google Scholar 

7. Chitsaz, H., & Aminisharifabad, M. (2015). Exact learning of rna energy parameters from structure.Journal of Computational Biology,22(6), 463–473.

   Article MathSciNet  Google Scholar 

8. Aminisharifabad, M., Yang, Q., & Wu, X. (2018). A penalized autologistic regression with application for modeling the microstructure of dual-phase high strength steel.Journal of Quality Technology (in-press).

   Google Scholar 

9. Clerc, M. (2004). Discrete particle swarm optimization, illustrated by the traveling salesman problem. InNew optimization techniques in engineering (pp. 219–239). Berlin: Springer.

   Google Scholar 

10. Mirjalili, S., & Lewis, A. (2013). S-shaped versus V-shaped transfer functions for binary particle swarm optimization.Swarm and Evolutionary Computation,9, 1–14.

    Article  Google Scholar 

11. Saremi, S., Mirjalili, S., & Lewis, A. (2015). How important is a transfer function in discrete heuristic algorithms.Neural Computing and Applications,26(3), 625–640.

    Article  Google Scholar 

12. Coello, C. A. C., Pulido, G. T., & Lechuga, M. S. (2004). Handling multiple objectives with particle swarm optimization.IEEE Transactions on Evolutionary Computation,8(3), 256–279.

    Article  Google Scholar 

13. Mostaghim, S., & Teich, J. (2003). Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO). InProceedings of the 2003 IEEE Swarm Intelligence Symposium, 2003. SIS 2003 (pp. 26–33). IEEE.

    Google Scholar 

14. Sierra, M. R., & Coello, C. A. C. (2005). Improving PSO-based multi-objective optimization using crowding, mutation and-dominance. InInternational Conference on Evolutionary Multi-Criterion Optimization (pp. 505–519). Berlin: Springer.

    Google Scholar 

15. Li, X. (2003). A non-dominated sorting particle swarm optimizer for multiobjective optimization. InGenetic and Evolutionary Computation Conference (pp. 37–48)

    Google Scholar 

16. Reyes-Sierra, M., & Coello, C. C. (2006). Multi-objective particle swarm optimizers: A survey of the state-of-the-art.International Journal of Computational Intelligence Research,2(3), 287–308.

    MathSciNet  Google Scholar 

17. Parsopoulos, K. E., & Vrahatis, M. N. (2002). Particle swarm optimization method for constrained optimization problems.Intelligent TechnologiesTheory and Application: New Trends in Intelligent Technologies,76(1), 214–220.

    MATH  Google Scholar 

18. Pulido, G. T., & Coello, C. A. C. (2004). A constraint-handling mechanism for particle swarm optimization. InIEEE congress on evolutionary computation (Vol. 2, pp. 1396–1403).

    Google Scholar 

19. Coello, C. A. C. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state of the art.Computer Methods in Applied Mechanics and Engineering,191(11–12), 1245–1287.

    Article MathSciNet  Google Scholar 

20. Eberhart, R. C., & Shi, Y. (2001). Tracking and optimizing dynamic systems with particle swarms. InProceedings of the 2001 Congress on Evolutionary Computation, 2001 (Vol. 1, pp. 94–100). IEEE.

    Google Scholar 

21. Blackwell, T., & Branke, J. (2004). Multi-swarm optimization in dynamic environments. InWorkshops on Applications of Evolutionary Computation (pp. 489–500). Berlin: Springer.

    Google Scholar 

22. Luan, F., Choi, J. H., & Jung, H. K. (2012). A particle swarm optimization algorithm with novel expected fitness evaluation for robust optimization problems.IEEE Transactions on Magnetics,48(2), 331–334.

    Article  Google Scholar 

23. Mirjalili, S., Lewis, A., & Mostaghim, S. (2015). Confidence measure: A novel metric for robust meta-heuristic optimisation algorithms.Information Sciences,317, 114–142.

    Article  Google Scholar 

24. Mirjalili, S., Lewis, A., & Dong, J. S. (2018). Confidence-based robust optimisation using multi-objective meta-heuristics.Swarm and Evolutionary Computation.

    Google Scholar 

25. Ono, S., Yoshitake, Y., & Nakayama, S. (2009). Robust optimization using multi-objective particle swarm optimization.Artificial Life and Robotics,14(2), 174.

    Article  Google Scholar 

26. Mirjalili, S., Lewis, A., & Mirjalili, S A M. (2015) Multi-objective optimisation of marine propellers.Procedia Computer Science,51, 2247–2256.

    Google Scholar 

27. Drela, M. (1989). XFOIL: An analysis and design system for low Reynolds number airfoils. InLow Reynolds number aerodynamics (pp. 1–12). Springer, Berlin, Heidelberg.

    Google Scholar 

Download references