Abstract
Evolutionary multi-agent systems (EMAS) turned out to be quite efficient technique for solving complex problems, both benchmark ones (as well-known multi-dimensional functions, e.g. Rastrigin, Schwefel etc) and more practical ones (like Optimal Golomb Ruler or Low Autocorrelation Binary Sequence). However the already classic design of the EMAS (these metaheuristics have been developed for over 15 years) has still many places for improvement. Hybridization is one of such means, and it turns out that incorporating Differential Evolution mechanisms into EMAS (altering the reproduction strategy by making it more social-aware) improves the accuracy of the search. This paper deals with discussion of selected means for hybridization of EMAS with DE, and provides an insight into the efficacy of the novel algorithm compared with classic techniques based on multidimensional benchmark problems.
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Byrski, A., Schaefer, R., Smołka, M.: Asymptotic guarantee of success for multi-agent memetic systems. Bull. Pol. Acad. Sci. Tech. Sci. 61(1), 257–278 (2013)
Byrski, A., Debski, R., Kisiel-Dorohinicki, M.: Agent-based computing in an augmented cloud environment. Comput. Syst. Sci. Eng. 27(1) (2012)
Byrski, A., Drezewski, R., Siwik, L., Kisiel-Dorohinicki, M.: Evolutionary multi-agent systems. Knowl. Eng. Rev. 30(2), 171–186 (2015). https://doi.org/10.1017/S0269888914000289
Cantú-Paz, E.: A summary of research on parallel genetic algorithms. IlliGAL Report No. 95007. University of Illinois (1995)
Caponio, A., Neri, F., Tirronen, V.: Super-fit control adaptation in memetic differential evolution frameworks. Soft Comput. 13(8), 811–831 (2009). https://doi.org/10.1007/s00500-008-0357-1
Cetnarowicz, K., Kisiel-Dorohinicki, M., Nawarecki, E.: The application of evolution process in multi-agent world (MAW) to the prediction system. In: Tokoro, M. (ed.) Proceedings of the 2nd International Conference on Multi-Agent Systems (ICMAS 1996). AAAI Press (1996)
Das, S., Konar, A., Chakraborty, U.K.: Improving particle swarm optimization with differentially perturbed velocity. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 177–184. ACM (2005)
Das, S., Suganthan, P.N.: Differential evolution: a survey of the state-of-the-art. IEEE Trans. Evol. Comput. 15(1), 4–31 (2011). https://doi.org/10.1109/TEVC.2010.2059031
Digalakis, J., Margaritis, K.: An experimental study of benchmarking functions for evolutionary algorithms. Int. J. Comput. Math. 79(4), 403–416 (2002). citeseer.ist.psu.edu/digalakis02experimental.html
Franklin, S., Graesser, A.: Is It an agent, or just a program? A taxonomy for autonomous agents. In: Müller, J.P., Wooldridge, M.J., Jennings, N.R. (eds.) ATAL 1996. LNCS, vol. 1193, pp. 21–35. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0013570
Gämperle, R., Müller, S.D., Koumoutsakos, P.: A parameter study for differential evolution. In: Grmela, A., Mastorakis, N. (eds.) Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation, pp. 293–298. WSEAS Press (2002)
He, X., Han, L.: A novel binary differential evolution algorithm based on artificial immune system. In: Proceedings of the IEEE Congress on Evolutionary Computation, pp. 2267–2272. IEEE (2007)
Hendtlass, T.: A combined swarm differential evolution algorithm for optimization problems. In: Monostori, L., Váncza, J., Ali, M. (eds.) IEA/AIE 2001. LNCS (LNAI), vol. 2070, pp. 11–18. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45517-5_2
Jitkongchuen, D.: A hybrid differential evolution with grey wolf optimizer for continuous global optimization. In: Proceedings of the 7th International Conference on Information Technology and Electrical Engineering, pp. 51–54. IEEE (2015)
Kannan, S., Slochanal, S.M.R., Subbaraj, P., Padhy, N.P.: Application of particle swarm optimization technique and its variants to generation expansion planning problem. Electr. Power Syst. Res. 70(3), 203–210 (2004). https://doi.org/10.1016/j.epsr.2003.12.009
Kisiel-Dorohinicki, M.: Agent-oriented model of simulated evolution. In: Grosky, W.I., Plášil, F. (eds.) SOFSEM 2002. LNCS, vol. 2540, pp. 253–261. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-36137-5_19
Korczynski, W., Byrski, A., Kisiel-Dorohinicki, M.: Buffered local search for efficient memetic agent-based continuous optimization. J. Comput. Sci. 20(Supplement C), 112–117 (2017). https://doi.org/10.1016/j.jocs.2017.02.001. http://www.sciencedirect.com/science/article/pii/S1877750317301345
Liao, T.W.: Two hybrid differential evolution algorithms for engineering design optimization. Appl. Soft Comput. 10(4), 1188–1199 (2010). https://doi.org/10.1016/j.asoc.2010.05.007
Liu, K., Du, X., Kang, L.: Differential evolution algorithm based on simulated annealing. In: Kang, L., Liu, Y., Zeng, S. (eds.) ISICA 2007. LNCS, vol. 4683, pp. 120–126. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74581-5_13
Noman, N., Iba, H.: Accelerating differential evolution using an adaptive local search. IEEE Trans. Evol. Comput. 12(1), 107–125 (2008). https://doi.org/10.1109/TEVC.2007.895272
Rahmat, N.A., Musirin, I.: Differential Evolution Ant Colony Optimization (DEACO) technique in solving economic load dispatch problem. In: Proceedings of the IEEE International Power Engineering and Optimization Conference, pp. 263–268. IEEE (2012)
Sörensen, K.: Metaheuristics–the metaphor exposed. Int. Trans. Oper. Res. 22(1), 3–18 (2015). https://doi.org/10.1111/itor.12001
Storn, R., Price, K.: Differential evolution: a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical report TR-95-012, ICSI, USA, March 1995
Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 67(1) (1997)
Yang, Z., Yao, X., He, J.: Making a difference to differential evolution. In: Siarry, P., Michalewicz, Z. (eds.) Advances in Metaheuristics for Hard Optimization, pp. 397–414. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-72960-0_19
Zhang, W.J., Xie, X.F.: DEPSO: hybrid particle swarm with differential evolution operator. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, pp. 3816–3821. IEEE (2003)
Zhong, W., Liu, J., Xue, M., Jiao, L.: A multiagent genetic algorithm for global numerical optimization. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(2), 1128–1141 (2004)
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The research presented in this paper was supported by the funds assigned by the Polish Minister of Science and Higher Education to AGH University of Science and Technology.
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Godzik, M., Grochal, B., Piekarz, J., Sieniawski, M., Byrski, A., Kisiel-Dorohinicki, M. (2019). Differential Evolution in Agent-Based Computing. In: Nguyen, N., Gaol, F., Hong, TP., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2019. Lecture Notes in Computer Science(), vol 11432. Springer, Cham. https://doi.org/10.1007/978-3-030-14802-7_20
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