Aiming at the disadvantages of Grey Wolf Optimization（GWO）, such as low solution accuracy, slow convergence speed and easy to fall into local optimization, an improved Grey Wolf Optimization（EGWO） is proposed. Two improvement strategies are introduced into the algorithm： the adjustment strategy of nonlinear convergence factor to balance global search and local development of the algorithm, and the elite re-election strategy to reduce the risk of falling into the local optimal. Through comparing experiments on 9 benchmark test functions with standard GWO algorithm, 5 improved GWO proposed by other literatures and 4 other algorithms, from algorithm optimization ability and robustness two aspects to demonstrate the effectiveness of two kinds of algorithm improvement strategies, the experimental results show that both strategies can improve algorithm performance, EGWO that comprehensive use of two strategies is superior to other comparison algorithms in convergence speed and solution accuracy.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2003-0121