In order to solve the problems of the traditional whale optimization algorithm, such as slow convergence speed, low optimization precision and easy to fall into the local optimal solution, an improved algorithm combining elite disturbance opposition-based learning and dynamic spiral updating (OWOA) was proposed. Firstly, the whale population is initialized by opposition-based learning strategies to ensure the diversity of the population , and then elite whales are multiple chaos disturbed to avoid falling into local optimal solution; Secondly, the algorithm uses a dynamic spiral updating strategy, and dynamically adjusts the spiral shape with the iteration times, thus improving the optimization accuracy of the algorithm. Finally, using 12 classic reference functions, CEC2014 test set and CEC2017 test set to evaluate the effectiveness of OWOA. In addition, optimum power flow(OPF) is employed for estimating the efficacy of the OWOA in practical applications. The experimental results show that:compared with other algorithms, the algorithm in this paper has higher convergence speed and accuracy in unimodal function, multi-peak function and multi-dimensional function, and which is more competitive in providing optimal solutions for optimization problems.

The original Honey Badger Algorithm as one of the newest meta¬heuristic techniques has a better convergence speed. However, HBA has the potential disadvantages of poor convergence accuracy, insufficient balancing among exploration and exploitation, and the propensity to slip into local optimization. In this paper, a novel golden sinusoidal survival honey badger algorithm is proposed. Firstly, an opposing learning and chaos mechanism are applied to the initial individual generation so that they can be distributed throughout the entire search area, which improves the precision of initial populations. Secondly, in the position update phase, we use a nonlinear convergence strategy to balance the weight of prey in the next walk and to increase the global search ability. After that, evaluating the quality of honey badger by golden sinusoidal survival rate and updating precocious individuals by Lévy flight, through which the premature convergence of the algorithm can be avoided. Finally, 23 benchmark function, CEC2019 tests are employed to assess the effectivity of improved algorithm. Test results indicate that the algorithm's capabilities to evolve, to extricate the local optimal and to detect the global optimal placements are enhanced.

In order to solve the problems of the traditional whale optimization algorithm, such as slow convergence speed, low optimization precision and easy to fall into the local optimal solution, an improved algorithm combining elite disturbance opposition-based learning and dynamic spiral updating (OWOA) was proposed. Firstly, the whale population is initialized by opposition-based learning strategies to ensure the diversity of the population, and then elite whales are multiple chaos disturbed to avoid falling into local optimal solution; Secondly, the algorithm uses a dynamic spiral updating strategy, and dynamically adjusts the spiral shape with the iteration times, thus improving the optimization accuracy of the algorithm. Finally, using 12 classic reference functions, CEC2014 test set and CEC2017 test set to evaluate the effectiveness of OWOA. In addition, optimum power flow(OPF) is employed for estimating the efficacy of the OWOA in practical applications. The experimental results show that:compared with other algorithms, the algorithm in this paper has higher convergence speed and accuracy in unimodal function, multi-peak function and multi-dimensional function, and which is more competitive in providing optimal solutions for optimization problems.