One of the most difficult problems that is all-pervasive in computing is that of partitioning. It has applications in the partitioning of databases into relations, the realization of the relations themselves into sub-relations based on the partitioning of the attributes, the assignment of processes to processors, graph partitioning, and the task assignment problem, etc. The problem is known to be NP-hard. The benchmark solution for this for the Equi-Partitioning Problem (EPP) has involved the classic field of Learning Automata (LA), and the corresponding algorithm, the Object Migrating Automata (OMA) has been used in all of these application domains. While the OMA is a fixed structure machine, it does not incorporate the Pursuit concept that has, recently, significantly enhanced the field of LA. In this paper, we pioneer the incorporation of the Pursuit concept into the OMA. We do this by a non-intuitive paradigm, namely that of removing (or discarding) from the query stream, queries that could be counter-productive. This can be perceived as a filtering agent triggered by a pursuit-based module. The resulting machine, referred to as the Pursuit OMA (POMA), has been rigorously tested in all the standard benchmark environments. Indeed, in certain extreme environments it is almost ten times faster than the original OMA reported in the legacy papers. 1 1In our improved implementation of these methods (explained in the body of the paper), our current scheme is nearly twice as fast. The application of the POMA to these application domains is extremely promising.

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Keywords Learning Automata, Object migration automaton, Object partitioning, Partitioning-based learning
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Journal Journal of Computational Science
Shirvani, A. (Abdolreza), & Oommen, J. (2017). On enhancing the object migration automaton using the Pursuit paradigm. Journal of Computational Science. doi:10.1016/j.jocs.2017.08.008