Abstract
For high dimensional and complex tasks, quantum optimization algorithms suffer from the problem of high computational cost. Distributed computing is an efficient way to solve such problems. Therefore, distributed optimization algorithms have become a hotspot for large scale optimization problems with the increasing volume of the data. In this paper, a novel Spark-based distributed quantum-behaved particle swarm optimization algorithm (SDQPSO) was proposed. By submitting the task to a higher computing cluster in parallel, the SDQPSO algorithm can improve the convergence performance.
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