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Multi-agent systems; Optimization; Distributed systems.
Solving complex optimization problems using meta-heuristics and their combinations are, in general, a hard approach, since a great knowledge of the problem often is necessary. An alternative to the development of new algorithms, or their manual hybridization, is to use the collaboration and communication mechanisms inherent in the modeling of multi-agent systems (MMAS). The D-Optimas architecture is an MMAS based on the actor model, where each agent encapsulates a different meta-heuristic, and with a learning mechanism they are allowed to collaborate in finding the best solution to an optimization problem. The agents interact in the search space that is divided into regions, which have an independent behavior, being able to receive new solutions, spliting into three new regions or merge with another one. However, the execution of the D-Optimas is limited to a small number of nodes in a cluster. Extending it, allowing its execution in a cluster with an unfixed number of nodes is essential for solving large-scale problems and extracting reliable data to analyze the architecture’s performance. Therefore, this work aims to consolidate the D-Optimas architecture from the point of view of a distributed system, fault tolerant, with load balancing and location transparency, making it resilient and horizontally scalable. The results obtained by this research so far shows that the architecture scales efficiently in a cluster with up to six nodes without losing performance. It is expected, with the continuation of this work, to study the dynamic hybridization of architecture with a wide variety of agents.