Collaborative Diffusion (CD) takes a lot of the work that A* does and combines (writes) it cheaply for multiple agents to read cheaply. This is because the majority of CD's processing works via a simple CA / diffusion approach that produces a single shared map for every agent to use on a given game update. Agents then perform hill-climbing within that space, which is also very cheap.
The primary downside is that the data structure created by CD must apply for all agents; that is, each agent's subjective view of the environment is identical; A* OTOH needs a path calculation per agent, each frame. In spite of this, the relatively low cost associated with CD would seem to make it, on average, a far more suitable approach than A*, even when we must create unique views for agents (comments / experience on this are welcome).
EDIT Consider the following example using Collaborative Diffusion:
Two armies are on a battlefield, each army emitting a uniform scent. They charge (climb the scent gradient), and as the two lines clash, each agent takes on the first, closest enemy agent on the opposing line. This happens because on each approach step for each unit, it checks whether it's yet adjacent to an enemy; if so it locks on / attacks indiscriminately. This is much freer than specific targeting, which is where A* would seem to be a better choice.
Am I correct in these assumptions? Are there other downsides to CD as opposed to [insert your flavour of] A*, for selecting ANY target between large groups?