Do We Need Change Detection for Dynamic Optimization Problems?

Abstract

Solving dynamic optimization problems is more challenging than static ones. When a change in the objective landscape occurs, the search process may not be powerful enough to track new optima. For population based algorithms this is referred to as diversity loss problem. Furthermore, the memory of old optima becomes outdated and if not correctly dealt with, the evolution of the search process may be misguided. Recently, a new interesting trend in dealing with optimization in dynamic environments has emerged toward developing new algorithms that are able to effectively handle changes without using any change detection scheme, and hence no extra computational cost is needed. There exist several works in the literature that attempt to maintain diversity without change detection. However, not that much work has been devoted to studies that investigate the possibility to overcome the outdated memory problem without expensive change detection. This study presents a comprehensive survey of the various change detection based methods. As part of this survey, we include a classification of the change detection schemes and we identify the main features of each method.