The Tracking Dynamical Evolutionary Algorithm for Dynamic Environments

摘要

In this paper, we proposed the Tracking Dynamical Evolutionary Algorithm (TDEA) that can efficiently locate and track the optimal solution in a dynamically changing environment. In TDEA, the particle's structure is different from traditional DEA. Each particle's knowledge is applied an "evaporation constant" to gradually weaken the knowledge's validity. Through this mechanism, the knowledge of each particle will be gradually updated in a dynamically changing environment. Compared with the traditional DEA, TDEA can quickly converge to the area of the goal and maintain the shortest distance from the goal.