Parallel algorithm of multiobjective optimization harmony search based on cloud computing

Abstract

In order to solve the problems of traditional harmony search in complex function multiobjective optimization, such as low precision, slow convergence, and easy to fall into local optimum, this article proposes a multiobjective optimization harmony search parallel algorithm based on cloud computing. First, according to the characteristics that the traditional harmony search algorithm uses a single harmony library for storing and processing the memory harmony, and it is divided into multiple harmony sublibraries according to different harmony. At the same time, the roulette selection and dynamic trade-off factor strategies are used for the dynamic setting of harmony memory library value-taking probability, pitch fine-tuning probability, pitch fine-tuning bandwidth, and other parameters which the traditional harmony search algorithm mainly relies on. Then, MapReduce programming model is used to establish Map and Reduce core parallel computing functions, to construct the parallel algorithm of dynamic parameter harmony search based on cloud computing. Finally, the algorithm optimization comparison test is conducted on Hadoop platform and compared with several existing optimal harmony search algorithms, the searching precision of this algorithm is improved by eight orders of magnitude, and the iteration number on the convergence speed is reduced by 6500 times, and the parallel achieves the linear acceleration ratio. Experimental results show that the optimization efficiency of this algorithm is higher than several existing optimal harmony search algorithms.