Abstract
Evolutionary Algorithms are efficient alternatives to solve complex optimization problems. The high computational cost of these algorithms commonly motivates their implementation to run in parallel computational environments. Island Model enables the parallel implementation of Evolutionary Algorithms relatively easily to incorporate the migration operation into the evolutionary process. The inclusion of new solutions in a population, previously evolved in another population, can contribute positively to the problem’s solution quality. In this work, a performance index was added to the Island Model, aiming to indicate how efficiently each island’s population is in solving the problem according to its algorithm. Islands with higher performance indexes receive more individuals in migrations. In this way, these algorithms become more active in the evolutionary process. The experiments demonstrated that the new model solutions were as good as the solutions from each problem’s best algorithm. We also noticed that even if we remove the most efficient algorithm from the model, it still adapts and provides efficient solutions.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley, Hoboken (2005)
Bessaou, M., Pétrowski, A., Siarry, P.: Island model cooperating with speciation for multimodal optimization. In: Schoenauer, M., et al. (eds.) PPSN 2000. LNCS, vol. 1917, pp. 437–446. Springer, Heidelberg (2000). https://doi.org/10.1007/3-540-45356-3_43
Beyer, H.G., Schwefel, H.P.: Evolution strategies - a comprehensive introduction. Nat. Comput. 1(1), 3–52 (2002)
Biscani, F., Izzo, D., Yam, C.H.: A global optimisation toolbox for massively parallel engineering optimisation. arXiv abs/1004.3824 (2010)
Candan, C., Goeffon, A., Lardeux, F., Saubion, F.: A dynamic island model for adaptive operator selection. In: Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation, GECCO 2012, pp. 1253–1260. Association for Computing Machinery, New York, NY, USA (2012)
Derbel, B., Verel, S.: DAMS: distributed adaptive metaheuristic selection. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, GECCO 2011, pp. 1955–1962. Association for Computing Machinery, New York, NY, USA (2011)
Dolan, E.D., More, J.J.: Benchmarking optimization software with performance profiles. Math. Program. 91(2), 201–213 (2002)
Duarte, G., Lemonge, A., Goliatt, L.: A dynamic migration policy to the island model. In: 2017 IEEE Congress on Evolutionary Computation (CEC), pp. 1135–1142 (June 2017)
Duarte, G., Lemonge, A., Goliatt, L.: A new strategy to evaluate the attractiveness in a dynamic island model. In: 2018 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (July 2018)
Friedberg, R.M.: A learning machine: part I. IBM J. Res. Dev. 2(1), 2–13 (1958)
Geem, Z.W., Kim, J.H., Loganathan, G.: A new heuristic optimization algorithm: harmony search. SIMULATION 76(2), 60–68 (2001)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)
Gustafson, S., Burke, E.K.: The speciating island model: an alternative parallel evolutionary algorithm. J. Parallel Distrib. Comput. 66(8), 1025–1036 (2006). Special Issue: Parallel Bioinspired Algorithms
Jankee, C., Verel, S., Derbel, B., Fonlupt, C.: Distributed adaptive metaheuristic selection: comparisons of selection strategies. In: Bonnevay, S., Legrand, P., Monmarché, N., Lutton, E., Schoenauer, M. (eds.) EA 2015. LNCS, vol. 9554, pp. 83–96. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31471-6_7
Kurdi, M.: A new hybrid island model genetic algorithm for job shop scheduling problem. Comput. Ind. Eng. 88(Suppl. C), 273–283 (2015)
Lardeux, F., Goëffon, A.: A dynamic island-based genetic algorithms framework. In: Deb, K., et al. (eds.) SEAL 2010. LNCS, vol. 6457, pp. 156–165. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17298-4_16
Li, C., Yang, S.: An island based hybrid evolutionary algorithm for optimization. In: Li, X., et al. (eds.) SEAL 2008. LNCS, vol. 5361, pp. 180–189. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-89694-4_19
Liang, J.J., Qu, B.Y., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Technical report (December 2013)
Ma, H., Shen, S., Yu, M., Yang, Z., Fei, M., Zhou, H.: Multi-population techniques in nature inspired optimization algorithms: a comprehensive survey. Swarm Evol. Comput. 44, 365–387 (2019)
Märtens, M., Izzo, D.: The asynchronous island model and NSGA-II: study of a new migration operator and its performance. In: Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation, GECCO 2013, pp. 1173–1180. ACM, New York, NY, USA (2013)
Parpinelli, R.S., Lopes, H.S.: An ecology-based heterogeneous approach for cooperative search. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds.) SBIA 2012. LNCS (LNAI), pp. 212–221. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34459-6_22
Ruciński, M., Izzo, D., Biscani, F.: On the impact of the migration topology on the island model. Parallel Comput. 36(10–11), 555–571 (2010). Parallel Architectures and Bioinspired Algorithms
Skolicki, Z., De Jong, K.: The influence of migration sizes and intervals on island models. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, GECCO 2005, pp. 1295–1302. Association for Computing Machinery, New York, NY, USA (2005)
Skolicki, Z.M.: An Analysis of Island Models in Evolutionary Computation. Ph.D. thesis, Fairfax, VA, USA (2007)
Storn, R., Price, K.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)
Tanabe, R., Fukunaga, A.: Evaluation of a randomized parameter setting strategy for island-model evolutionary algorithms. In: 2013 IEEE Congress on Evolutionary Computation, pp. 1263–1270 (2013)
Acknowledgment
The authors acknowledge the financial support of CNPq (429639/2016-3), FAPEMIG (APQ-00334/18), and CAPES - Finance Code 001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Pontes, R.C.A.F., Duarte, G.R., Goliatt, L. (2020). Migration Guided by a Performance Index in Heterogeneous Island Models. In: Filipič, B., Minisci, E., Vasile, M. (eds) Bioinspired Optimization Methods and Their Applications. BIOMA 2020. Lecture Notes in Computer Science(), vol 12438. Springer, Cham. https://doi.org/10.1007/978-3-030-63710-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-63710-1_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-63709-5
Online ISBN: 978-3-030-63710-1
eBook Packages: Computer ScienceComputer Science (R0)