Similar to other swarm-based algorithms, the recently developed whale optimization algorithm (WOA) has the problems of low accuracy and slow convergence. It is also easy to fall into local optimum. Moreover, WOA and its variants cannot perform well enough in solving high-dimensional optimization problems. This paper puts forward a new improved WOA with joint search mechanisms called JSWOA for solving the above disadvantages. First, the improved algorithm uses tent chaotic map to maintain the diversity of the initial population for global search. Second, a new adaptive inertia weight is given to improve the convergence accuracy and speed, together with jump out from local optimum. Finally, to enhance the quality and diversity of the whale population, as well as increase the probability of obtaining global optimal solution, opposition-based learning mechanism is used to update the individuals of the whale population continuously during each iteration process. The performance of the proposed JSWOA is tested by twenty-three benchmark functions of various types and dimensions. Then, the results are compared with the basic WOA, several variants of WOA and other swarm-based intelligent algorithms. The experimental results show that the proposed JSWOA algorithm with multi-mechanisms is superior to WOA and the other state-of-the-art algorithms in the competition, exhibiting remarkable advantages in the solution accuracy and convergence speed. It is also suitable for dealing with high-dimensional global optimization problems.
Developing green hydrogen energy to power future societies has driven the progress of proton-exchange membrane water electrolyzers (PEMWE). However, due to the complex anode oxygen evolution reaction (OER) electron transfer process and the strong acidic environment, the most effective catalysts are still Ir-based nanomaterials. Therefore, exploiting low cost acidic OER catalysts to meet the needs of PEMWE remains a challenging and rewarding task. Herein, hexagonal-shaped and defect-rich MnOx/RuO2 heterojunction nanosheets (H/d-MnOx/RuO2) is designed. The oxygen vacancies and heterogeneous structure enable the H/d-MnOx/RuO2 catalyst to reach 10 mA cm−2 with only overpotential 178 mV in 0.5 m H2SO4. Density functional theory shows that the oxygen vacancies and heterogeneous interface facilitates the reduction of the adsorption energy of *OOH and the reduction of the energy level of Ru-Oads, thus suppressing the involvement of lattice oxygen and enhancing the durability. This study provides an effective way to design efficient catalysts for hydrogen production in PEMWE. 相似文献