Abstract
This paper presents a cooperative co-evolutionary dynamic multi-objective optimization algorithm, i.e., DNSGAII-CO for solving DMOPs based on environmental variable grouping. In this algorithm, a new method of grouping decision variables is first presented, in which all the decision variables are divided into two subcomponents according to whether they are interrelated with or without environment parameters. Then, when cooperatively optimizing the two subcomponents by using two populations, two prediction methods, i.e., differential prediction and Cauchy mutation, are employed to initialize them, respectively. The proposed algorithm is applied to a benchmark DMOPs, and compared with two state-of-the-art algorithms. The experimental results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of convergence and distribution.
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
Abello, M.B., Bui, L.T., Michalewicz, Z.: An adaptive approach for solving dynamic scheduling with time-varying number of tasks-Part II. In: 2011 IEEE Congress on Evolutionary Computation (CEC), pp. 1711–1718. IEEE (2011)
Amiri, B., Hossain, L., Crowford, J.: A multiobjective hybrid evolutionary algorithm for clustering in social networks. In: Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation, pp. 1445–1446. ACM (2012)
Deb, K.: Single and multi-objective dynamic optimization: two tales from an evolutionary perspective. Indian Institute of Technology (2011)
Deb, K., Rao, U.B.N., Karthik, S.: Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling. In: Obayashi, S., Deb, K., Poloni, C., Hiroyasu, T., Murata, T. (eds.) EMO 2007. LNCS, vol. 4403, pp. 803–817. Springer, Heidelberg (2007)
Farina, M., Deb, K., Amato, P.: Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Trans. Evol. Comput. 8(5), 425–442 (2004)
Kaiwartya, O., Kumar, S., Lobiyal, D., Tiwari, P.K., Abdullah, A.H., Hassan, A.N.: Multiobjective dynamic vehicle routing problem and time seed based solution using particle swarm optimization. J. Sens. 2015 (2015)
Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Automatic design of scheduling policies for dynamic multi-objective job shop scheduling via cooperative coevolution genetic programming. IEEE Trans. Evol. Comput. 18(2), 193–208 (2014)
Omidvar, M.N., Li, X., Mei, Y., Yao, X.: Cooperative co-evolution with differential grouping for large scale optimization. IEEE Trans. Evol. Comput. 18(3), 378–393 (2014)
Tang, J., Alam, S., Lokan, C., Abbass, H.A.: A multi-objective evolutionary method for dynamic airspace re-sectorization using sectors clipping and similarities. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2012)
Wu, P.P., Campbell, D., Merz, T.: Multi-objective four-dimensional vehicle motion planning in large dynamic environments. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 41(3), 621–634 (2011)
Zhang, Z.: Multiobjective optimization immune algorithm in dynamic environments and its application to greenhouse control. Appl. Soft Comput. 8(2), 959–971 (2008)
Acknowledgments
This work was jointly supported by National Natural Science foundation of China (No. 61375067, 61473299, 61573361), National Basic Research Program of China (973 Program) (No. 2014CB046306-2), Natural Science foundation of Anhui Province (No. 1608085QG169), and Natural Science Foundation of Anhui Higher Education Institutions (No. KJ2014B17).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Xu, B., Zhang, Y., Gong, D., Rong, M. (2016). Cooperative Co-evolutionary Algorithm for Dynamic Multi-objective Optimization Based on Environmental Variable Grouping. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_56
Download citation
DOI: https://doi.org/10.1007/978-3-319-41000-5_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-40999-3
Online ISBN: 978-3-319-41000-5
eBook Packages: Computer ScienceComputer Science (R0)