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The HER requires a highly efficient, cost-effective, and stable catalyst to adapt to the large-scale hydrogen industry. Nickel has been confirmed to be useful to drive the water splitting reaction, but the intrinsic performance remains unsatisfactory. In this work, nickel (EG–Ni) with compressive strain was prepared through a one-step electrochemical deposition strategy. It shows an outstanding enhancement for the HER, and it achieves a current density of 10 mA cm−2 at a low overpotential of 85.9 mV. A long-term durability test proves that the EG–Ni can tolerate a large current density of 100 mA cm−2, and the overpotential remains steady without dramatically increasing. Such a low overpotential and superior stability are attributed to the optimized adsorption energy on the catalyst surface, as evidenced by the downshifted position of the d-band center. [ABSTRACT FROM AUTHOR]

Elephant herding optimization (EHO) has received widespread attention due to its few control parameters and simple operation but still suffers from slow convergence and low solution accuracy. In this paper, an improved algorithm to solve the above shortcomings, called Gaussian perturbation specular reflection learning and golden-sine-mechanism-based EHO (SRGS-EHO), is proposed. First, specular reflection learning is introduced into the algorithm to enhance the diversity and ergodicity of the initial population and improve the convergence speed. Meanwhile, Gaussian perturbation is used to further increase the diversity of the initial population. Second, the golden sine mechanism is introduced to improve the way of updating the position of the patriarch in each clan, which can make the best-positioned individual in each generation move toward the global optimum and enhance the global exploration and local exploitation ability of the algorithm. To evaluate the effectiveness of the proposed algorithm, tests are performed on 23 benchmark functions. In addition, Wilcoxon rank-sum tests and Friedman tests with 5% are invoked to compare it with other eight metaheuristic algorithms. In addition, sensitivity analysis to parameters and experiments of the different modifications are set up. To further validate the effectiveness of the enhanced algorithm, SRGS-EHO is also applied to solve two classic engineering problems with a constrained search space (pressure-vessel design problem and tension-/compression-string design problem). The results show that the algorithm can be applied to solve the problems encountered in real production. [ABSTRACT FROM AUTHOR]