SOMA — Self-Organizing Migrating Algorithm
Swarm-based algorithms have become one of the foremost researched and applied heuristics in the field of evolutionary computation within the past decade. One of the new and novel approaches is that of the self-organizing migrating algorithm (SOMA). Initially developed and published in 2001 by Prof. Ivan Zelinka, SOMA has been actively researched by a select group of researchers over the past decade and a half.
SOMA is conceptualized on a predator/prey relationship, where the sampling of the search space is conducted on a multi-dimensional facet, with the dimension selection conducted pre-sampling, using a randomly generated PRT vector. Two unique aspects of SOMA, which differentiate it from other swarm-based algorithms, are the creation and application of the PRT vector, and the path length, which specifies the distance and sampling required within a particular dimension.
Over the past few years, SOMA has been modified to solve combinatorial optimization problems. This discrete variant so-called discrete self-organizing migrating algorithm (DSOMA) has been proven to be robust and efficient.
With its ever-expanding applications and utilization, it was thought beneficial and timely to produce a collated work of all the active applications of SOMA, which shows its current state of the art. To this effect, we have reached out and have obtained original research topics in SOMA and its application from a very diverse group of academics and researchers.