A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems

TL;DR

This study compares four popular constraint handling techniques within a differential evolution framework to evaluate their effectiveness in dynamic constrained optimization problems, revealing that no single method is best overall.

Contribution

It provides a systematic analysis of constraint handling techniques in DCOPs using a common benchmark and a change detection mechanism, highlighting their varied performance.

Findings

Certain techniques excelled in optimization speed.

Some methods showed higher solution reliability.

No single technique was superior in all aspects.

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.