Chaos Gray Wolf global optimization algorithm based on Opposition-based Learning

Gray wolf optimizer (GWO) is a new heuristic algorithm. It has few parameters and strong optimization ability and is used in many fields. However, when solving complex and multimodal functions, it is also easy to trap into the local optimum and premature convergence. In order to boost the performance of GWO, a tent chaotic map and opposition-based learning Grey Wolf Optimizer (CO-GWO) is proposed. Firstly, some better values of the population in the current generation are retained to avoid deterioration in the next generation. Secondly, tent chaotic map and opposition-based (OBL)are introduced to generate values that can traverse the whole feasible region as much as possible, which is conducive to jumping out of local optimization and accelerating convergence. Then, the coefficient is dynamically adjusted by the polynomial attenuation function of the 2-decay method. Finally, the proposed algorithm is tested on 23 benchmark functions. The results show that the proposed algorithm is superior to the conventional heuristic algorithms, GWO and its variants in search-efficiency, solution accuracy and convergence rate.