Differential Evolution › Disadvantages

A population of size $NP$ requires $NP$ candidate evaluations before mutation begins, and a complete generation requires another $NP$. This can be expensive when one evaluation runs a long simulation. Derivative-based local methods remain useful baselines when derivatives are available; their cost and result depend on the objective, starting point, and stopping rule.

A finite DE run does not certify a global optimum. Its population can collapse around a suboptimal region and leave little variation for further steps. A local polish may refine its best candidate, but those additional evaluations must be counted. Population spread and best-so-far cost help diagnose a run; neither is an optimality certificate.

Coordinate-wise mutation differences scale with the coordinates themselves. With matched bounds and initial populations, changing units by a diagonal affine transformation does not inherently freeze a small-scale coordinate. Numerical precision, stopping tolerances, or a local solver can still make units matter. Binomial crossover selects coordinate axes, so rotation is a different issue: rotating the variables can change the composite search.