A Hybrid Mechanism of Particle Swarm Optimization and Differential Evolution Algorithms based on Spark

Abstract

With the onset of the big data age, data is growing exponentially, and the issue of how to optimize large-scale data processing is especially significant. Large-scale global optimization (LSGO) is a research topic with great interest in academia and industry. Spark is a popular cloud computing framework that can cluster large-scale data, and it can effectively support the functions of iterative calculation through resilient distributed datasets (RDD). In this paper, we propose a hybrid mechanism of particle swarm optimization (PSO) and differential evolution (DE) algorithms based on Spark (SparkPSODE). The SparkPSODE algorithm is a parallel algorithm, in which the RDD and island models are employed. The island model is used to divide the global population into several subpopulations, which are applied to reduce the computational time by corresponding to RDD's partitions. To preserve population diversity and avoid premature convergence, the evolutionary strategy of DE is integrated into SparkPSODE. Finally, SparkPSODE is conducted on a set of benchmark problems on LSGO and show that, in comparison with several algorithms, the proposed SparkPSODE algorithm obtains better optimization performance through experimental results.

Tunnel displacement prediction under spatial effect based on Gaussian process regression optimized by differential evolution

“…First, based on the relevant research experience of DE strategy [28], fixed F as 0.6, and CR is 0.3 ∼ 0.9 in turn. As shown in Fig.…”

“…The Fig. 9 The iterative convergence curves under a variety of F、CR value changed conditions First, based on the relevant research experience of DE strategy [28], fixed F as 0.6, and CR is 0.3 ~0.9 in turn. As shown in Fig.…”