National Natural Science Foundation of China (No.61463009);Science and Technology Top-notch Talents Support Plan of Colleges and Universities in Guizhou Province (黔教合KY字.[2017]070)
LONG Wen, CAI Shao-hong, JIAO Jian-jun, et al. An Improved Grey Wolf Optimization Algorithm[J]. Acta Electronica Sinica, 2019, 47(1): 169-175.
DOI:
LONG Wen, CAI Shao-hong, JIAO Jian-jun, et al. An Improved Grey Wolf Optimization Algorithm[J]. Acta Electronica Sinica, 2019, 47(1): 169-175. DOI: 10.3969/j.issn.0372-2112.2019.01.022.
Grey wolf optimization (GWO) algorithm is a relatively novel optimization technique which has been shown to be competitive to other population-based algorithms.However
there is still an insufficiency in canonical GWO regarding its position update equation
which is good at exploitation but poor at exploration.Inspired by differential evolution and particle swarm optimization
the personal best information and the random selected individual from population are used to construct a modified position update equation for enhancing the exploration.Inspired by particle swarm optimization
a random adjustment strategy of control parameterais proposed.In addition
to enhance the global convergence
when producing the initial population
the chaos method is employed.Simulation experiments were conducted on the 18 high-dimensional conventional test functions.The simulation results show that the proposed algorithm provides better performance than basic GWO algorithms in the same or less number of maximum fitness function evaluation in most cases.