This paper proposes two adaptations to DynDE, a differential evolution-based algorithm for solving
dynamic optimization problems. The first adapted algorithm, Competitive Population Evaluation (CPE),
is a multi-population DE algorithm aimed at locating optima faster in the dynamic environment. This
adaptation is based on allowing populations to compete for function evaluations based on their performance.
The second adapted algorithm, Reinitialization Midpoint Check (RMC), is aimed at improving
the technique used by DynDE to maintain populations on different peaks in the search space. A combination
of the CPE and RMC adaptations is investigated. The new adaptations are empirically compared
to DynDE using various problem sets. The empirical results show that the adaptations constitute an
improvement over DynDE and compares favorably to other approaches in the literature. The general
applicability of the adaptations is illustrated by incorporating the combination of CPE and RMC into
another Differential Evolution-based algorithm, jDE, which is shown to yield improved results.