Gaussian global-best harmony search algorithm for optimization problems

In this paper, a novel optimization approach is proposed using Gaussian distribution function to improve harmony search (HS) algorithm, namely “Gaussian global-best harmony search” (GGHS) algorithm. The harmony memory (HM) is adjusted using Gaussian random generation in GGHS. The GGHS algorithm consists of two adjustment steps; in the first stage, the harmony memory is adjusted by a dynamic bandwidth and the Gaussian distribution with a mean of 0 and a standard deviation of 1. In the second stage, the best memory is adjusted using a random Gaussian number with a mean of 0 and a dynamic standard deviation. The dynamic bandwidth and standard deviation are adaptively determined based on iteration number, the maximum and minimum of each design variable in the HM. Accuracy and efficiency of the GGHS algorithm have been evaluated using several benchmark mathematical examples. The numerical results illustrate that the GGHS algorithm is more accurate and efficient than other existing modified versions of harmony search algorithm. The capability of the dynamic bandwidth enhances the performance of this modified HS algorithm. The GGHS produces the better optimum results in comparison with other improved versions of harmony search algorithm.