Bacterial Foraging Algorithm For Dynamic Environments
Wenzhong Tang, Qinghua Wu, Jon R. Saunders · 2006
Optimization in dynamic environments has received great attention in recent years [1]. Different from static optimization problems, its convergence and searching ability is cautiously desired. Over the last two decades, evolutionary algorithms (EAs), designed to solve the static optimization problems, have been comprehensively and intensively investigated. In recent years, as the emergence of another member of the EA family -bacterial foraging algorithm (BFA), the self-adaptability of individuals in the group searching activities has attracted a great deal of interests. In this paper, a BFA aiming for optimization in dynamic environments, called DBFA, is studied. A test bed proposed previously in [2] is adopted to evaluate the performance of DBFA. The simulation studies offer a range of changes in a dynamic environment. The simulation results show that DBFA can adapt to various environmental changes which occur in different probabilities, with both satisfactory accuracy and stability, in comparison with a recent work on bacterial foraging [3].