An orthogonal electric fish optimization algorithm with quantization for global numerical optimization

In the past few decades, meta-heuristic algorithms have become a research hotspot in the field of evolutionary computing. The electric fish optimization algorithm (EFO) is a new meta-heuristic algorithm. Because of its simplicity and easy implementation, it has attracted the attention of researchers. However, it still faces premature convergence and poor balance between exploration and exploitation. To address this problems, an orthogonal electric fish optimization algorithm with quantization (QOXEFO) is proposed in this paper. In QOXEFO, orthogonal cross-design and quantification technique are employed to enhance the diversity of population and convergence precision of EFO. Secondly, the dynamic boundary mechanism is adopted to improve the convergence speed of EFO. At the same time, a sine-based update strategy of active electrolocation is used to change the direction of movement of individuals, thereby helping them jump out of the local optimum. Finally, the CEC2017 benchmark function and Speed reducer design problem are used to verify the performance of the proposed QOXEFO. Experimental results and statistical analysis show that compared with 9 famous evolutionary algorithms, QOXEFO is competitive in solution accuracy and convergence speed.