Application of a hybrid improved sparrow search algorithm for the prediction and control of dissolved oxygen in the aquaculture industry

Dissolved oxygen is a critical water quality factor, and its prediction and control have long been a research hotspot in the aquaculture industry. This study aims to address the limitations of poor population diversity and the ease of falling into local optima in the basic sparrow search algorithm (SSA). Herein, we improved this methodology in many aspects and proposed a hybrid improved SSA (HISSA) to enhance optimization performance. First, in the global search process of the sparrow producer, the position update strategy is modified to enhance the global search ability of the algorithm. Second, a butterfly algorithm is incorporated to replace the position update strategy in the escape stage of the producer to promote information exchange between individuals. Third, the Cauchy mutation strategy and greedy rule are used to perturb and update the current optimal solution to improve the algorithm’s ability to jump out of local optima. The performance of the proposed HISSA was verified on 10 benchmark functions. The experimental results illustrate that the proposed algorithm outperformed the four intelligent algorithms evaluated herein. In addition, the proposed HISSA was separately applied to the parameter optimization of the two most commonly used engineering methods: the back propagation neural network (BPNN) and proportional-integral-derivative (PID) controller. Compared with other intelligent algorithm-optimized methods, the error evaluation index values of the HISSA-BPNN and HISSA-PID controllers are small, indicating that the proposed HISSA is highly effective and practical for engineering parameter optimization, and can be competent for the prediction and control of dissolved oxygen.