An adaptive optimization technique for dynamic environments
The use of evolutionary algorithms (EAs) is beneficial for addressing optimization problems in dynamic environments. The objective function for such problems changes continually; thus, the optimal solutions likewise change. Such dynamic changes pose challenges to EAs due to the poor adaptability of EAs once they have converged. However, appropriate preservation of a sufficient level of individual diversity may help to increase the adaptive search capability of EAs. This paper proposes an EA-based Adaptive Dynamic OPtimization Technique (ADOPT) for solving time-dependent optimization problems. The purpose of this approach is to identify the current optimal solution as well as a set of alternatives that is not only widespread in the decision space, but also performs well with respect to the objective function. The resultant solutions may then serve as a basis solution for the subsequent search while change is occurring. Thus, such an algorithm avoids the clustering of individuals in the same region as well as adapts to changing environments by exploiting diverse promising regions in the solution space. Application of the algorithm to a test problem and a groundwater contaminant source identification problem demonstrates the effectiveness of ADOPT to adaptively identify solutions in dynamic environments.
adaptability evolutionary algorithms diversity adaptive dynamic optimization contaminant source identification