A Comparison of Constraint Handling Techniques for Dynamic Constrained Optimization Problems

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.

On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization

“…DE is a stochastic search algorithm that is simple, reliable and fast which showed competitive results in constrained and dynamic optimization [14]. Each vector x i,G in the current population (called at the moment of the reproduction as target vector) generates one trial vector u i,G by using a mutant vector v i,G .…”

“…The other parameters are: frequency of change (f c )=1000, runs=30 and the number of considered times for dynamic perspective of the algorithm 5/k (k = 0.5). Parameters of DE are chosen as n p = 20, CR = 0.2, F is a random number in [0.2, 0.8], and rand/1/bin is the chosen variant of DE [14].…”

“…We choose differential evolution (DE) as our baseline algorithm which has showed competitive results in dynamic and constrained optimization problems [14]. The pre-sented results reveal applying the diversity promotion techniques enhanced algorithm performance significantly based on statistical test applied in modified offline error values.…”

On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints

“…In this section differential evolution algorithm, the applied constraint handling techniques and change detection mechanism are briefly introduced. Differential evolution (DE) is a stochastic search algorithm that is simple, reliable and fast and showed competitive results in constraint and dynamic optimization [15]. Each vector x i,G in the current population (called at the moment of the reproduction as target vector) generates one trial vector u i,G by using a mutant vector v i,G .…”

“…In addition, some papers enhanced both constraint handling and dynamic handling mechanisms [5]. Among the many evolutionary algorithms, DE has showed competitive results in dynamic and constrained optimization problems so far [15].…”