2018/02/15 by María-Yaneli Ameca-Alducin, Ameca-Alducin, Maria-Yaneli, Maryam Hasani-Shoreh +7
Computer Science · #Metaheuristic Optimization Algorithms Research #Constraint Satisfaction and Optimization #Advanced Multi-Objective Optimization Algorithms
paper · pdf · doi:10.48550/arxiv.1802.05825
Dynamic constrained optimization problems (DCOPs) have gained researchers\nattention in recent years because a vast majority of real world problems change\nover time. There are studies about the effect of constrained handling\ntechniques in static optimization problems. However, there lacks any\nsubstantial study in the behavior of the most popular constraint handling\ntechniques when dealing with DCOPs. In this paper we study the four most\npopular used constraint handling techniques and apply a simple Differential\nEvolution (DE) algorithm coupled with a change detection mechanism to observe\nthe behavior of these techniques. These behaviors were analyzed using a common\nbenchmark to determine which techniques are suitable for the most prevalent\ntypes of DCOPs. For the purpose of analysis, common measures in static\nenvironments were adapted to suit dynamic environments. While an overall\nsuperior technique could not be determined, certain techniques outperformed\nothers in different aspects like rate of optimization or reliability of\nsolutions.\n