Multi-strategy sparrow search algorithm with non-uniform mutation

Sparrow search algorithm (SSA) suffers from a tendency to fall into local optima, as well as a preference for zero locations. Therefore, to improve this drawback, we propose a non-uniform mutation sparrow search algorithm (NMSSA). In the initialization stage of the population, we introduce a tent chaos map and a generalized opposition-based learning strategy to improve the diversity of the population; We introduce adaptive weight to dynamically adjust the search range of the discoverer to improve the search efficiency of the algorithm; To prevent the algorithm from deviating from the target in the early stage, we adopt a non-uniform mutation strategy to improve the flexibility of the follower search to improve the convergence accuracy of the algorithm. Finally, we use the somersault strategy to reduce the probability of the algorithm falling into local optimum. In the test experiments with 10 benchmark functions and CEC2017 functions, we compare the experimental results of NMSSA with those of other algorithms, and the experimental results verify the effectiveness of NMSSA. In addition, we also applied NMSSA to engineering problems optimization and K-means image segmentation, and the experimental results show that NMSSA has good performance in practical applications.

Automated summary

In this study, a non-uniform mutation sparrow search algorithm (NMSSA) is proposed to improve the issues of local optima and zero locations preference in the traditional sparrow search algorithm (SSA). By introducing different strategies, including the tent chaos map, generalized opposition-based learning, adaptive weight, and non-uniform mutation, experimental results show that NMSSA performs well in benchmark functions, engineering optimization, and image segmentation.