A novel chaotic grey wolf optimisation for high-dimensional and numerical optimisation. (12th March 2022) - Zhang, Meng Jian; Long, Dao Yin; Li, Dan Dan; Wang, Xiao; Qin, Tao; Yang, Jing
Aiming at the weakness of the current evolutionary algorithms for high-dimensional and numerical optimisation problems of global convergence, a novel chaotic grey wolf optimisation (NCGWO) is proposed for solving the high-dimensional optimisation problems. Firstly, the six chaotic one-dimensional maps are introduced and their mathematical models are improved with their mapping ranges being in the interval (0, 1). Secondly, the diversity experiments are conducted to test the results of the chaotic maps. The experiments show that the initial population by chaotic maps is superior to the GWO algorithm and the Sine map is best. Finally, the CSGWO algorithm is also proposed based on the NCGWO algorithm with the parameter C by Sine map. The simulations demonstrate that the performance of the GWO algorithm can be improved by the chaotic maps for high-dimensional and numerical optimisation problems, and the effectiveness of the CSGWO algorithm is superior to other evolutionary algorithms and achieves better accuracy and convergence speed.