  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.
