Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/117740
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Type: Conference paper
Title: A comparison of constraint handling techniques for dynamic constrained optimization problems
Author: Ameca-Alducin, M.
Hasani-Shoreh, M.
Blaikie, W.
Neumann, F.
Mezura-Montes, E.
Citation: IEEE Congress on Evolutionary Computation (CEC), 2018 / vol.OnlinePubl, pp.290-297
Publisher: IEEE
Issue Date: 2018
Series/Report no.: IEEE Congress on Evolutionary Computation
ISBN: 9781509060177
Conference Name: IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI) (08 Jul 2018 - 13 Jul 2018 : Rio de Janeiro, BRAZIL)
Statement of
Responsibility: 
María-Yaneli Ameca-Alducin, Maryam Hasani-Shoreh, Wilson Blaikie, Frank Neumann, Efrén Mezura-Montes
Abstract: Dynamic constrained optimization problems (DCOPs) have gained researchers attention in recent years because a vast majority of real world problems change over time. There are studies about the effect of constrained handling techniques in static optimization problems. However, there lacks any substantial study in the behavior of the most popular constraint handling techniques when dealing with DCOPs. In this paper we study the four most popular used constraint handling techniques and apply a simple Differential Evolution (DE) algorithm coupled with a change detection mechanism to observe the behavior of these techniques. These behaviors were analyzed using a common benchmark to determine which techniques are suitable for the most prevalent types of DCOPs. For the purpose of analysis, common measures in static environments were adapted to suit dynamic environments. While an overall superior technique could not be determined, certain techniques outperformed others in different aspects like rate of optimization or reliability of solutions.
Keywords: Dynamic constrained optimization; constraint-handling techniques; differential evolution
Rights: ©2018 IEEE.
RMID: 0030084766
DOI: 10.1109/CEC.2018.8477750
Grant ID: http://purl.org/au-research/grants/arc/DP160102401
Published version: https://ieeexplore.ieee.org/document/8477750
Appears in Collections:Computer Science publications

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