  Population diversity plays a key role in evolutionary algorithms that enables
global exploration and avoids premature convergence. This is especially more
crucial in dynamic optimization in which diversity can ensure that the
population keeps track of the global optimum by adapting to the changing
environment. Dynamic constrained optimization problems (DCOPs) have been the
target for many researchers in recent years as they comprehend many of the
current real-world problems. Regardless of the importance of diversity in
dynamic optimization, there is not an extensive study investigating the effects
of diversity promotion techniques in DCOPs so far. To address this gap, this
paper aims to investigate how the use of different diversity mechanisms may
influence the behavior of algorithms in DCOPs. To achieve this goal, we apply
and adapt the most common diversity promotion mechanisms for dynamic
environments using differential evolution (DE) as our base algorithm. The
results show that applying diversity techniques to solve DCOPs in most test
cases lead to significant enhancement in the baseline algorithm in terms of
modified offline error values.
