A multi-population differential evolution algorithm based on cellular learning automata and evolutionary context information for optimization in dynamic environments

Abstract

This paper presents a multi-population differential evolution algorithm to address dynamic optimization problems. In the proposed approach, a cellular learning automaton adjusts the behavior of each subpopulation by adaptively controlling its updating schemes. As the environment changes over time, an evolving population may go through a quite number of state transitions. Each state demands specific characteristics from an optimizer; hence, an adapted evolutionary scheme for one state may be unsuitable for the upcoming ones. Additionally, a learning approach may have limited time to adapt to a newly encountered state due to the frequentness of the environmental changes. Hence, it is infeasible for a dynamic optimizer to unlearn its existing beliefs to accommodate the practices required to embrace newly encountered states. In order to address this issue, we introduce a context dependent learning approach, which can adapt the behavior of each subpopulation according to the contexts of its different states. The performance of the proposed approach is compared with several state-of-the-art dynamic optimizers over the GDBG benchmark set. Comparison results indicate that the proposed method can achieve statistically superior performances on a wide range of tested instances.