An exponential function inflation size of multi-verse optimisation algorithm for global optimisation
Wei Li Pan, Yongquan Zhou, Zhiming Li · International Journal of Computing Science and Mathematics · 2017
This paper proposed an improved multi-verse optimisation (IMVO) algorithm based on exponential function inflation size. The main inspirations of IMVO are based on cosmic expansion, and inflation never ends. In the entire universe, we can think it is exponential growth. Exponential function inflation size is introduced to enhance accuracy and increase convergence rate of the multi-verse optimisation (MVO) algorithm. The numerical simulation experiments and comparisons are carried out based on a set of ten benchmark functions. The IMVO algorithm is compared with multi-verse optimisation (MVO), moth-flame optimisation (MFO), artificial bee colony (ABC) algorithm, bat algorithm (BA), differential evolution (DE) algorithm and dragonfly algorithm (DA). The experiment results show that the IMVO has not only the higher accuracy but also the faster convergence speed.