Strategies for Parallelizing Swarm Intelligence Algorithms

Abstract

Swarm intelligence algorithms, based on multi-agent systems, are often used to solve complex problems that are not affordable through classical centralized/deterministic solutions. In many cases, to enhance the performance of such algorithms, the computation can be distributed to parallel/distributed nodes, in accordance with different strategies. Specifically, parallelization can be achieved either by partitioning the space in which agents operate among the nodes, or by assigning the entire space to each node but distributing input data through a sampling approach. Another choice is whether or not the management of conflicts is needed to prevent possible loss of data consistency. This paper discusses such issues, while referring to two well-known types of swarm intelligence algorithms -- ants and flocking -- and compares the mentioned strategies, evaluating the performance results in terms of speedup.