MB‐GNG: Addressing drawbacks in multi‐objective optimization estimation of distribution algorithms

We examine the model‐building issue related to multi‐objective estimation of distribution algorithms (MOEDAs) and show that some of their, as yet overlooked, characteristics render most current MOEDAs unviable when addressing optimization problems with many objectives. We propose a novel model‐building growing neural gas (MB‐GNG) network that is specially devised for properly dealing with that issue and therefore yields a better performance. Experiments are conducted in order to show from an empirical point of view the advantages of the new algorithm.