Chaotic dynamic weight grey wolf optimizer for numerical function optimization

The grey wolf optimizer (GWO) algorithm is a recently proposed optimization technique based on the social leadership and hunting behavior of grey wolves in nature. Due to its small number of control parameters, ease of implementation and high level of exploration and exploitation, the GWO has attracted the interest of researchers from different fields. However, the GWO has problems with its position-updated equation, which is good for exploitation but not conducive to exploration because it can prematurely convergence to local optima. To overcome this drawback, a chaotic dynamic weight grey wolf optimizer (CDGWO) is proposed. In the CDGWO algorithm, a new position-updated equation is presented by applying a chaotic map and dynamic weight to guide the search process for potential candidate solutions. In addition, a nonlinear control parameter strategy is designed to balance the exploration and exploitation and accelerate the convergence speed of the GWO algorithm. The search accuracy and performance of the modified position-updated equation and the nonlinear control parameter strategy are verified using 19 well-known classical benchmark functions. The experimental results show that, for almost all benchmark functions, the CDGWO algorithm gives competitive results in terms of convergence, solution quality and local optimal avoidance compared with other nature-inspired optimizations and GWO variants.