Grey wolf optimizer (GWO) is an efficient swarm intelligence algorithm for kinds of optimization problems. However, GWO tends to be trapped in local optimum when solving large-scale problems. Social hierarchy is one of the main characteristics of GWO which affect the searching efficiency. Thus, an improved algorithm called hierarchy strengthened GWO (HSGWO) is proposed in this paper. Firstly, the pack of wolves is roughly divided into two categories: dominant wolves and omega wolves. Secondly, the enhanced elite learning strategy is performed for dominant wolves to prevent the misguidance of low-ranking wolves and improve the collective efficiency. Then, the hybrid GWO and differential evolution (DE) strategy is executed for omega wolves to avoid falling into local optimum. In addition, a new hybrid one-dimensional and total-dimensional selection strategy is designed for omega wolves to balance the exploration and the exploitation during optimization. Finally, a perturbed operator is used to maintain the diversity of the population and further improve the exploration. To make a complete evaluation, the proposed HSGWO is first compared with six representative GWO variants for 50-dimensional problems based on CEC2014 benchmarks. The scalability of HSGWO is further tested by comparing it with eight state-of-the-art non-GWO algorithms for large-scale optimization problems with 100 decision variables. In addition, feature selection problem is used for testing the effectiveness of HSGWO on real-world applications. Experimental results demonstrated that the proposed algorithm outperforms other algorithms in terms of solution quality and convergence rate in most of the experiments.

Node localization is one of the basic requirements in various Internet of Things applications. Among a wide range of localization schemes, the range-free localization algorithm is promising as a cost-effective technique. However, the localization accuracy of this technique is susceptible to various anisotropy factors, such as the existence of holes, nonuniform node distribution, and dynamic radio propagation pattern. To this end, an accurate range-free localization model using extreme learning machine (ELM) and ring-shaped salp swarm algorithm (SSA) is proposed for anisotropic wireless sensor networks. First, the integer hop count between two adjacent nodes is quantized as a real number according to the Jaccard coefficient of their shared neighbor nodes. Second, exploiting the strong generalization and fast learning speed of ELM, a distance mapping model based on the modified real hop count is developed for solving anisotropic signal attenuation. Third, the coordinate calculation of normal nodes is formulated as a minimum problem by taking into account the weighted squared error of estimated distance, and the bounding box method is utilized to initialize the possible location boundary area of normal nodes. Finally, the SSA based on the ring-shaped topology is designed to compute the coordinates of normal nodes. Extensive simulations on several network topologies are conducted with the effect of multiple anisotropic factors. Experimental results show that the proposed algorithm is superior to other developed ones not only in localization accuracy but also in robustness against network anisotropy.