Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/113064
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Type: Conference paper
Title: On the use of repair methods in differential evolution for dynamic constrained optimization
Author: Ameca-Alducin, M.
Hasani-Shoreh, M.
Neumann, F.
Citation: Proceedings of the 21st International Conference on the Applications of Evolutionary Computation (EvoApplications), as published in Lecture Notes in Computer Science, 2018 / Sim, K., Kaufmann, P., Ascheid, G., Bacardit, J., Cagnoni, S., Cotta, C., DAndreagiovanni, F., Divina, F., EsparciaAlcazar, A., DeVega, F., Glette, K., Hidalgo, J., Hubert, J., Iacca, G., Kramer, O., Mavrovouniotis, M., Garcia, A., Nguyen, T., Schaefer, R., Silva, S., Tonda, A., Urquhart, N., Zhang, M. (ed./s), vol.10784, pp.832-847
Publisher: Springer International Publishing AG
Publisher Place: Cham, Switzerland
Issue Date: 2018
Series/Report no.: Lecture Notes in Computer Science; 10784
ISBN: 3319775375
9783319775371
ISSN: 0302-9743
1611-3349
Conference Name: 21st International Conference on the Applications of Evolutionary Computation (EvoApplications) (04 Apr 2018 - 06 Apr 2018 : Parma, ITALY)
Statement of
Responsibility: 
Maria-Yaneli Ameca-Alducin, Maryam Hasani-Shoreh, and Frank Neumann
Abstract: Dynamic constrained optimization problems have received increasing attention in recent years.We study differential evolution which is one of the high performing class of algorithms for constrained continuous optimization in the context of dynamic constrained optimization. The focus of our investigations are repairmethods which are crucial when dealing with dynamic constrained problems. Examining recently introduced benchmarks for dynamic constrained continuous optimization, we analyze different repair methods with respect to the obtained offline error and the success rate in dependence of the severity of the dynamic change. Our analysis points out the benefits and drawbacks of the different repair methods and gives guidance to its applicability in dependence on the dynamic changes of the objective function and constraints.
Keywords: Repair methods; dynamic constrained optimization Constraint-handling techniques; differential evolution
Description: Also part of the Theoretical Computer Science and General Issues book sub series (LNTCS, volume 10784)
Rights: © Springer International Publishing AG, part of Springer Nature 2018
RMID: 0030084778
DOI: 10.1007/978-3-319-77538-8_55
Grant ID: http://purl.org/au-research/grants/arc/DP140103400
http://purl.org/au-research/grants/arc/DP160102401
Published version: https://www.springer.com/gp/book/9783319775371
Appears in Collections:Computer Science publications

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