Abstract
Solving a multi-objective optimization problem results in a Pareto front approximation, and it differs from single-objective optimization, requiring specific search strategies. These strategies, mostly fitness assignment, are designed to find a set of non-dominated solutions, but different approaches use various schemes to achieve this goal. In many cases, cooperative algorithms such as island model-based algorithms outperform each particular algorithm included in this cooperation. However, we should note that there are some control parameters of the islands’ interaction and, in this paper, we investigate how they affect the performance of the cooperative algorithm. We consider the influence of a migration set size and its interval, the number of islands and two types of cooperation: homogeneous or heterogeneous. In this study, we use the real-valued evolutionary algorithms SPEA2, NSGA-II, and PICEA-g as islands in the cooperation. The performance of the presented algorithms is compared with the performance of other approaches on a set of benchmark multi-objective optimization problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm for multiobjective optimization. In: Evolutionary Methods for Design Optimisation and Control with Application to Industrial Problems, EUROGEN 2001, vol. 3242, no. 103, pp. 95–100 (2002)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Wang, R.: Preference-inspired co-evolutionary algorithms. A thesis submitted in partial fulfillment for the degree of the Doctor of Philosophy, University of Sheffield, p. 231 (2013)
Stringer, J., Lamont G., Akers, G.: Radar phase-coded waveform design using MOEAs. In: WCCI 2012 IEEE World Congress on Computational Intelligence (2012). https://doi.org/10.1109/cec.2012.6256554
Jiang, Sh., Yang, Sh.: A strength pareto evolutionary algorithm based on reference direction for multi-objective and many-objective optimization. IEEE Trans. Evol. Comput. (2017). https://doi.org/10.1109/TEVC.2016.2592479
Guliashki, V., Kirilov, L., Genova, K.: An interactive evolutionary algorithm for multiple objective integer problems. Int. J. Inf. Technol. Secur. 5(2), 45–54 (2013)
Brester, Ch., Ryzhikov, I., Semenkin, E.: Restart operator for multi-objective genetic algorithms: implementation, choice of control parameters and ways of improvement. Int. J. Inf. Technol. Secur. 9(4), 25–36 (2017)
Whitley, D., Rana, S., Heckendorn, R.B.: Island model genetic algorithms and linearly separable problems. In: Corne, D., Shapiro, Jonathan L. (eds.) AISB EC 1997. LNCS, vol. 1305, pp. 109–125. Springer, Heidelberg (1997). https://doi.org/10.1007/BFb0027170
Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley, Hoboken (2005). https://doi.org/10.1002/0471739383
Van Veldhuizen, D.A., Zydallis, J.B., Lamont, G.B.: Considerations in engineering parallel multiobjective evolutionary algorithms. IEEE Trans. Evol. Comput. 7(2), 144–173 (2003). https://doi.org/10.1109/TEVC.2003.810751
Brester, Ch., Ryzhikov, I., Semenkin, E.: Multi-objective optimization algorithms with the island metaheuristic for effective project management problem solving. Organizacija (J. Manag. Inf. Syst. Hum. Resour.) 50(4), 364–373 (2017)
Ryzhikov, I., Brester, Ch., Semenkin, E.: Multi-objective dynamical system parameters and initial value identification approach in chemical disintegration reaction modelling. In: Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017), vol. 1, pp. 497–504 (2017). https://doi.org/10.5220/0006431504970504
Zhang, Q., Zhou, A., Zhao, S., Suganthan, P. N., Liu, W., Tiwari, S.: Multi-objective optimization test instances for the CEC 2009 special session and competition. Technical report CES-487, University of Essex and Nanyang Technological University (2008)
Brester, Ch., Semenkin, E.: Cooperative multi-objective genetic algorithm with parallel implementation. In: Tan, Y., Shi, Y., Buarque, F., Gelbukh, A., Das, S., Engelbrecht, A. (eds.) ICSI 2015. LNCS, vol. 9140, pp. 471–478. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-20466-6_49
Liu, M., Zou, X., Chen, Y., Wu, Z.: Performance assessment of DMOEA-DD with CEC 2009 MOEA competition test instances. In: 2009 IEEE Congress on Evolutionary Computation (2009). https://doi.org/10.1109/cec.2009.4983309
Acknowledgements
This research is supported by the Russian Foundation for Basic Research within project No 16-01-00767.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Brester, C., Ryzhikov, I., Semenkin, E., Kolehmainen, M. (2018). On Island Model Performance for Cooperative Real-Valued Multi-objective Genetic Algorithms. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-93815-8_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93814-1
Online ISBN: 978-3-319-93815-8
eBook Packages: Computer ScienceComputer Science (R0)