Abstract
Particle Swarm Optimization (PSO) is a member of the nature inspired algorithms. Its ability to solve many complex search problems efficiently and accurately has made it an interesting research area. In this study, we model Distributed Database Query Optimization problem as a Bare Bones PSO and develop a set of canonical and hybrid PSO algorithms. To the best of our knowledge, this is the first time that Bare Bones PSO is being used for solving this problem. We explore and evaluate the capabilities of PSO against Iterative Dynamic Programming, and a Genetic Algorithm. We experimentally show that PSO algorithms are able to find near-optimal solutions efficiently.
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