Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/123899
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Type: Conference paper
Title: On the behaviour of differential evolution for problems with dynamic linear constraints
Author: Hasani Shoreh, M.
Ameca-Alducin, M.Y.
Blaikie, W.
Neumann, F.
Schoenauer, M.
Citation: Proceedings: 2019 IEEE Congress on Evolutionary Computation (CEC 2019), 2019, vol.abs/1905.04099, pp.3045-3052
Publisher: IEEE
Publisher Place: online
Issue Date: 2019
Series/Report no.: IEEE Congress on Evolutionary Computation
ISBN: 9781728121543
Conference Name: IEEE Congress on Evolutionary Computation (CEC) (10 Jun 2019 - 13 Jun 2019 : Wellington, New Zealand)
Statement of
Responsibility: 
Maryam Hasani-Shoreh, Marìa-Yaneli Ameca-Alducin, Wilson Blaikie, Frank Neumann
Abstract: Evolutionary algorithms have been widely applied for solving dynamic constrained optimization problems (DCOPs) as a common area of research in evolutionary optimization. Current benchmarks proposed for testing these problems in the continuous spaces are either not scalable in problem dimension or the settings for the environmental changes are not flexible. Moreover, they mainly focus on non-linear environmental changes on the objective function. While the dynamism in some real-world problems exists in the constraints and can be emulated with linear constraint changes. The purpose of this paper is to introduce a framework which produces benchmarks in which a dynamic environment is created with simple changes in linear constraints (rotation and translation of constraint's hyperplane). Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function. Different constraint handling techniques will then be used to compare with our benchmark. The results reveal that with these changes set, there was an observable effect on the performance of the constraint handling techniques.
Rights: © 2019 IEEE
DOI: 10.1109/CEC.2019.8790067
Grant ID: http://purl.org/au-research/grants/arc/DP160102401
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