The frequency and intensity of individual learning directly define the degree of evolution (exploration) against individual learning (exploitation) in the MA search, for a given fixed limited computational budget.
Clearly, a more intense individual learning provides greater chance of convergence to the local optima but limits the amount of evolution that may be expended without incurring excessive computational resources.
Therefore, care should be taken when setting these two parameters to balance the computational budget available in achieving maximum search performance. When only a portion of the population individuals undergo learning, the issue on which subset of individuals to improve need to be considered to maximize the utility of MA search.
Last but not least, the individual learning procedure/meme used also favors a different neighborhood structure, hence the need to decide which meme or memes to use for a given optimization problem at hand would be required.