PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

(µ + λ) evolution strategy with socio-cognitive mutation

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on socio-cognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.
Twórcy
  • AGH University, Al. Mickiewicza 30, 30-059 Krakow
  • AGH University, Al. Mickiewicza 30, 30-059 Krakow
  • AGH University, Al. Mickiewicza 30, 30-059 Krakow
  • AGH University, Al. Mickiewicza 30, 30-059 Krakow
  • Institute of Systems Science Research, Warsaw, Poland; AGH University, Al. Mickiewicza 30, 30-059 Krakow
autor
  • Czestochowa University of Technology, Poland
  • Institute of Systems Science Research, Warsaw, Poland; AGH University, Al. Mickiewicza 30, 30-059 Krakow
autor
  • Southern University of Science and Technology, Shenzhen, China
  • Southern University of Science and Technology, Shenzhen, China
  • AGH University, Al. Mickiewicza 30, 30-059 Krakow
Bibliografia
  • [1] Z. Michalewicz and D. B. Fogel, How to Solve It: Modern Heuristics. Springer Science & Business Media, 2013.
  • [2] D. H. Wolpert and W. G. Macready, “No free lunch theorems for optimization,” IEEE Transactions on Evolutionary Computation, vol. 1, no. 1, Apr. 1997, pp. 67–82, doi: 10.1109/4235.585893.
  • [3] E.‐G. Talbi, Metaheuristics: From Design to Implementation. John Wiley & Sons, 2009.
  • [4] K. Sörensen, “Metaheuristics—the metaphor exposed,” International Transactions in Operational Research, vol. 22, no. 1, 2015, pp. 3–18, doi: 10.1111/itor.12001.
  • [5] G. Lakoff and M. Johnson, Metaphors We Live By. Chicago, IL: University of Chicago Press, 2003. Accessed: Feb. 16, 2024. [Online]. Available: https://press.uchicago.edu/ucp/books/book/chicago/M/bo3637992.html.
  • [6] A. Bandura, “Self‐efϐicacy: Toward a unifying theory of behavioral change,” Psychological Review, vol. 84, no. 2, 1977, pp. 191–215, doi: 10.1037/0033‐295X.84.2.191.
  • [7] A. Bandura, Social foundations of thought and action: A social cognitive theory. in Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ, US: Prentice‐Hall, Inc, 1986, pp. xiii, 617.
  • [8] A. Bandura, D. Ross, and S. A. Ross, “Transmission of aggression through imitation of aggressive models,” The Journal of Abnormal and Social Psychology, vol. 63, no. 3, 1961, pp. 575–582, doi: 10.1037/h0045925.
  • [9] A. Byrski, et al., “Socio‐cognitively inspired ant colony optimization,” Journal of Computational Science, vol. 21, Jul. 2017, pp. 397–406, doi: 10.1016/j.jocs.2016.10.010.
  • [10] I. Bugajski, et al., “Enhancing Particle Swarm Optimization with Socio‐cognitive Inspirations,” Procedia Computer Science, vol. 80, Jan. 2016, pp. 804–813, doi: 10.1016/j.procs.2016.05.370.
  • [11] A. Urbanczyk, B. Nowak, P. Orzechowski, J. H. Moore, M. Kisiel‐Dorohinicki, and A. Byrski, “Socio‐cognitive Evolution Strategies,” in Computational Science – ICCS 2021, M. Paszynski, D. Kranzlmüller, V. V. Krzhizhanovskaya, J. J. Dongarra, and P. M. A. Sloot, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2021, pp. 329–342. doi: 10.1007/978‐3‐030‐77964‐1_26.
  • [12] E.‐G. Talbi, “A Taxonomy of Hybrid Metaheuristics,” Journal of Heuristics, vol. 8, no. 5, Sep. 2002, pp. 541–564, doi: 10.1023/A:1016540724870.
  • [13] Y.‐S. Ong, M.‐H. Lim, N. Zhu, and K.‐W. Wong, “Classification of adaptive memetic algorithms: a comparative study,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 36, no. 1, Feb. 2006, pp. 141–152, doi: 10.1109/TSMCB.2005.856143.
  • [14] Robert Schaefer, Aleksander Byrski, Joanna Kolodziej, and Maciej Smolka. An agent‐based model of hierarchic genetic search. Comput. Math. Appl., 64(12):3763–3776, 2012.
  • [15] Kamil Pietak, Adam Wos, Aleksander Byrski, and Marek Kisiel‐Dorohinicki. Functional integrity of multi‐agent computational system supported by component‐based implementation. In Vladimír Marík, Thomas I. Strasser, and Alois Zoitl, editors, Holonic and Multi-Agent Systems for Manufacturing, 4th International Conference on Industrial Applications of Holonic and MultiAgent Systems, HoloMAS 2009, Linz, Austria, August 31 - September 2, 2009. Proceedings, volume 5696 of Lecture Notes in Computer Science, pages 82–91. Springer, 2009.
  • [16] Robert Schaefer, Aleksander Byrski, and Maciej Smolka. Stochastic model of evolutionary and immunological multi‐agent systems: Parallel execution of local actions. Fundam. Informaticae, 95(2‐3):325–348, 2009.
  • [17] I. Rechenberg, Cybernetic Solution Path of an Experimental Problem by Ingo Rechenberg. Royal Aircraft Establishment, 1965.
  • [18] H.‐P. Schwefel, Numerische Optimierung von Computer-Modellen mittels der Evolutions strategie: Mit einer vergleichenden Einführung in die Hill-Climbing- und Zufallsstrategie. Basel: Birkhäuser, 1977. doi: 10. 1007/978‐3‐0348‐5927‐1.
  • [19] D. V. Arnold, “Weighted multirecombination evolution strategies,” Theoretical Computer Science, vol. 361, no. 1, Aug. 2006, pp. 18–37, doi: 10.1016/j.tcs.2006.04.003.
  • [20] D. Brockhoff, A. Auger, N. Hansen, D. V. Arnold, and T. Hohm, “Mirrored Sampling and Sequential Selection for Evolution Strategies,” in Parallel Problem Solving from Nature, PPSN XI, R. Schaefer, C. Cotta, J. Kołodziej, and G. Rudolph, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2010, pp. 11–21. doi: 10.1007/978‐3‐642‐15844‐5_2.
  • [21] T.‐Y. Huang and Y.‐Y. Chen, “Modified evolution strategies with a diversity‐based parent‐inclusion scheme,” in Proceedings of the 2000. IEEE International Conference on Control Applications. Conference Proceedings (Cat. No.00CH37162), Sep. 2000, pp. 379–384. doi: 10.1109/CCA.2000.897454.
  • [22] N. Hansen and A. Ostermeier, “Completely Derandomized Self‐Adaptation in Evolution Strategies,” Evolutionary Computation, vol. 9, no. 2, Jun. 2001, pp. 159–195, doi: 10.1162/106365601750190398.
  • [23] P. P. Repoussis, C. D. Tarantilis, O. Bräysy, and G. Ioannou, “A hybrid evolution strategy for the open vehicle routing problem,” Computers & Operations Research, vol. 37, no. 3, Mar. 2010, pp. 443–455, doi: 10.1016/j.cor.2008.11.003.
  • [24] D. Koulocheris, H. Vrazopoulos, and V. Dertimanis, “Hybrid evolution strategy for the design of welded beams,” in Proc. of Int. Congress on Evolutionary Methods for Design, Optimization and Control with Applications to Industrial Problems EUROGEN 2003, 2003.
  • [25] L. Dos Santos Coelho and P. Alotto,“Electromagnetic device optimization by hybrid evolution strategy approaches,” COMPEL –The international journal for computation and mathematics in electrical and electronic engineering, vol. 26, no. 2, Apr. 2007, pp. 269–279, doi: 10.1108/03321640710727638.
  • [26] R. Storn and K. Price, “Differential Evolution -A Simple and Efϐicient Heuristic for global Optimization over Continuous Spaces,” Journal of Global Optimization, vol. 11, no. 4, Dec. 1997, pp. 341–359, doi: 10.1023/A:1008202821328.
  • [27] Kenneth Price, Rainer M. Storn, and Jouni A. Lampinen. Differential Evolution. in Natural Computing Series. Berlin/Heidelberg: Springer-Verlag, 2005. doi: 10.1007/3‐540‐31306‐0.
  • [28] C.‐L. Hwang, Y.‐J. Lai, and T.‐Y. Liu, “A new approach for multiple objective decision making,” Computers & Operations Research, vol. 20, no. 8, Oct. 1993, pp. 889–899, doi: 10.1016/0305‐0548(93)90109‐V.
  • [29] M. Nabywaniec, et al., “Socio‐cognitive Optimization of Time‐delay Control Problems using Agent‐based Metaheuristics,” in 2022 IEEE 11th International Conference on Intelligent Systems (IS), Oct. 2022, pp. 1–7. doi: 10.1109/IS57118. 2022.10019693.
  • [30] P. Kipinski, et al., “Socio‐cognitive Optimization of Time‐delay Control Problems using Evolutionary Metaheuristics.” arXiv, Oct. 23, 2022. doi: 10.48550/arXiv.2210.12872.
  • [31] J. Dieterich and B. Hartke, “Empirical Review of Standard Benchmark Functions Using Evolutionary Global Optimization,” Applied Mathematics, vol. 03, Jul. 2012, doi: 10.4236/am.2012.330215.
  • [32] M. López‐Ibáñez, J. Dubois‐Lacoste, L. Pérez Cáceres, M. Birattari, and T. Stützle, “The irace package: Iterated racing for automatic algorithm configuration,” Operations Research Perspectives, vol. 3, Jan. 2016, pp. 43–58, doi: 10.1016/j.orp.2016.09.002.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5f48712c-a730-4b40-b1ee-f2233138e518
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.