A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems
This work addresses a gap in understanding constraint handling for dynamic optimization problems, which is incremental as it applies existing techniques to a new context.
The paper tackled the lack of studies on constraint handling techniques for dynamic constrained optimization problems (DCOPs) by comparing four popular techniques using a Differential Evolution algorithm with change detection on a common benchmark. The result showed no overall superior technique, but certain ones outperformed in aspects like optimization rate or solution reliability.
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.