Differential Evolution and Engineering Problems
Abstract
In this paper, the performance of the Differential Evolution algorithm is evaluated when solving real-world problems. A Set of 13 engineering optimisation problems was selected from the fields of mechanics and industry to illustrate the usability of the Differential Evolution algorithm. Twelve variants of the standard Differential Evolution with various settings of the control parameters are compared with 19 state-of-the-art adaptive variants of this algorithm. The results are analysed statistically to achieve significant differences. Three variants of adaptive Differential Evolution provided better results compared to other algorithms. Some adaptive variants of Differential Evolution perform significantly worse than the original Differential Evolution with the fixed setting of the control parameters.
References
Bayzidi, H., Talatahari, S., Saraee, M., and Lamarche, C.-P. Social network search for solving engineering optimization problems. Computational Intelligence and Neuroscience 2021 (2021).
Brest, J., Greiner, S., Boskovic, B., Mernik, M., and Zumer, V. Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. IEEE Transactions on Evolutionary Computation 10 (2006), 646–657.
Brest, J., Maucec, M. S., and Boskovic, B. Single objective real-parameter optimization: Algorithm jso. In 2017 IEEE Congress on Evolutionary Computation (CEC) (2017), pp. 1311–1318.
Brest, J., Maucec, M. S., and Boskovic, B. The 100-digit challenge: Algorithm jDE100. In 2019 IEEE Congress on Evolutionary Computation (CEC) (2019), pp. 19–26.
Bujok, P. Competition of strategies in jso algorithm. In Swarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing (Cham, 2020), A. Zamuda, S. Das, P. N. Suganthan, and B. K. Panigrahi, Eds., Springer, pp. 113–121.
Bujok, P. The real-life application of differential evolution with a distance-based mutationselection. Mathematics 9, 16 (2021).
Bujok, P., and Tvrdık, J. A comparison of various strategies in differential evolution. In MENDEL, 17th International Conference on Soft Computing, Brno, Czech Republic (2011), R. Matousek, Ed., pp. 48–55.
Bujok, P., and Tvrdık, J. New adaptive variant of differential evolution and real-world optimization problems. In Proceedings of BIOMA 2016, he 7th International Conference on Bioinspired Optimization Methods and their Applications. 2016.
Bujok, P., Tvrdık, J., and Polakova, R. Evaluating the performance of shade with competing strategies on CEC 2014 single-parameter test suite. In IEEE Congress on Evolutionary Computation (CEC) 2016 (2016), pp. 5002–5009.
Kononova, A. V., Vermetten, D., Caraffini, F., Mitran, M.-A., and Zaharie, D. The importance of being constrained: Dealing with infeasible solutions in differential evolution and beyond. Evolutionary Computation (may 2023), 1–46.
Kudela, J. A critical problem in benchmarking and analysis of evolutionary computation methods. Nature Machine Intelligence 4, 12 (2022), 1238–1245.
Polakova, R., Tvrdik, J., and Bujok, P. Evaluating the performance of l-shade with competing strategies on cec2014 single parameteroperator test suite. In 2016 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) (2016), IEEE Congress on Evolutionary Computation, IEEE; IEEE Computat Intelligence Soc; Int Neural Network Soc; Evolutionary Programming Soc; IET; IEEE BigData Initiat; Gulf Univ Sci & Technol, pp. 1181–1187. IEEE Congress on Evolutionary Computation (CEC) held as part of IEEE World Congress on Computational Intelligence (IEEE WCCI), Vancouver, CANADA, JUL 24-29, 2016.
Storn, R., and Price, K. V. Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11 (1997), 341–359.
Tanabe, R., and Fukunaga, A. S. Successhistory based parameter adaptation for differential evolution. In IEEE Congress on Evolutionary Computation (CEC), 2013 (June 2013), pp. 71–78.
Tanabe, R., and Fukunaga, A. S. Improving the search performance of shade using linear population size reduction. In IEEE Congress on Evolutionary Computation (CEC) 2014 (2014), pp. 1658–1665.
Tang, L., Dong, Y., and Liu, J. Differential evolution with an individual-dependent mechanism. IEEE Transactions on Evolutionary Computation 19, 4 (2015), 560–574.
Wang, Y., Li, H.-X., Huang, T., and Li, L. Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Applied Soft Computing 18 (2014), 232–247.
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