Nature-Inspired Algorithms in Real-World Optimization Problems
Abstract
Eight popular nature inspired algorithms are compared with the blind random search and three advanced adaptive variants of differential evolution (DE) on real-world problems benchmark collected for CEC 2011 algorithms competition. The results show the good performance of the adaptive DE variants and their superiority over the other algorithms in the test problems. Some of the nature-inspired algorithms perform even worse that the blind random search in some problems. This is a strong argument for recommendation for application, where well-verified algorithm successful in competitions should be preferred instead of developing some new algorithms.
References
Bujok, P., Tvrdík: Enhanced success-history based parameter adaptation for differential evolution and realworld optimization problems. In: G. Papa, M. Mernik (eds.) BIOMA, Bioinspired Optimization Methods and their Applications, Bled, Slovenia, pp. 159–171 (2016)
Bujok, P., Tvrdík, J., Poláková, R.: Differential evolution with exponential crossover revisited. In: R. Matoušek (ed.) MENDEL, 22nd International Conference on Soft Computing, Brno, Czech Republic, pp. 17–24 (2016)
Bujok, P., Tvrdík, J., Poláková, R.: Evaluating the performance of shade with competing strategies on cec 2014 single-parameter test suite. In: 2016 IEEE Congress on Evolutionary Computation CEC, pp. 5002–5009 (2016)
Das, S., Mullick, S., Suganthan, P.: Recent advances in differential evolution-an updated survey. Swarm and Evolutionary Computation 27, 1–30 (2016)
Das, S., Suganthan, P.N.: Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Tech. rep., Jadavpur University, India and Nanyang Technological University, Singapore (2010)
Das, S., Suganthan, P.N.: Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation 15, 27–54 (2011)
Fister Jr., I., Yang, X.S., Fister, I., Brest, J., Fister, D.: A brief review of nature-inspired algorithms for optimization. Elektrotehniski vestnik 80(3), 116–122 (2013)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes University, Kayseri, Turkey (2005)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: 1995 IEEE International Conference on Neural Networks Proceedings, Vols 1-6, pp. 1942–1948. IEEE, Neural Networks Council (1995). 1995 IEEE International Conference on Neural Networks ICNN 95, UNIV W AUSTRAIA, PERTH, AUSTRALIA, NOV 27-DEC 01, 1995
Piotrowski, A.P., Napiorkowski, M.J., Napiorkowski, J.J., Rowinski, P.M.: Swarm intelligence and evolutionary algorithms: Performance versus speed. Information Sciences 384, 34–85 (2017)
Rastrigin, L.: Convergence of random search method in extremal control of many-parameter system. Automation and remote control 24(11), 1337–1342 (1964)
al Rifaie, M.M.: Dispersive flies optimisation. In: Federated Conference on Computer Science and Information Systemss, 2014, ACSIS-Annals of Computer Science and Information Systems, vol. 2, pp. 529–538 (2014). Federated Conference on Computer Science and Information Systems FedCSIS, Warsaw, POLAND, SEP 07-10, 2014
Tanabe, R., Fukunaga, A.: Success-history based parameter adaptation for differential evolution. In: IEEE Congress on Evolutionary Computation (CEC), 2013, pp. 71–78 (2013)
Tanabe, R., Fukunaga, A.S.: Improving the search performance of shade using linear population size reduction. In: 2014 IEEE Congress on Evolutionary Computation CEC 2014, pp. 1658–1665 (2014)
Wang, Y., Li, H.X., Huang, T., Li, L.: Differential evolution based on covariance matrix learning and bimodal distribution parameter setting. Applied Soft Computing 18, 232–247 (2014)
Yang, X.S.: A new metaheuristic bat-inspired algorithm. In: J. Gonzalez, D. Pelta, C. Cruz, G. Terrazas, N. Krasnogor (eds.) Nicso 2010: Nature Inspired Cooperative Strategies for Optimization, Studies in Computational Intelligence, vol. 284, pp. 65–74. Univ Laguna; Carnary Govt; Spanish Govt (2010). International Workshop on Nature Inspired Cooperative Strategies for Optimization NICSO 2008, Tenerife, SPAIN, 2008
Yang, X.S.: Flower pollination algorithm for global optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7445 LNCS, 240–249 (2012)
Yang, X.S.: Nature-Inspired Optimization Algorithms. Elsevier (2014)
Yang, X.S., Deb, S.: Cuckoo search via Lévy flights. In: 2009 World Congress on Nature Biologically Inspired Computing NaBIC, pp. 210–214 (2009)
Zelinka, I., Lampinen, J.: Soma – self organizing migrating algorithm. In: R. Matousek (ed.) MENDEL, 6th International Conference On Soft Computing, Brno, Czech Republic, pp. 177–187 (2000)
MENDEL open access articles are normally published under a Creative Commons Attribution-NonCommercial-ShareAlike (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ . Under the CC BY-NC-SA 4.0 license permitted 3rd party reuse is only applicable for non-commercial purposes. Articles posted under the CC BY-NC-SA 4.0 license allow users to share, copy, and redistribute the material in any medium of format, and adapt, remix, transform, and build upon the material for any purpose. Reusing under the CC BY-NC-SA 4.0 license requires that appropriate attribution to the source of the material must be included along with a link to the license, with any changes made to the original material indicated.
