A Multi-Strategy Sparrow Search Algorithm with Selective Ensemble

Abstract

Aiming at the deficiencies of the sparrow search algorithm (SSA), such as being easily disturbed by the local optimal and deficient optimization accuracy, a multi-strategy sparrow search algorithm with selective ensemble (MSESSA) is proposed. Firstly, three novel strategies in the strategy pool are proposed: variable logarithmic spiral saltation learning enhances global search capability, neighborhood-guided learning accelerates local search convergence, and adaptive Gaussian random walk coordinates exploration and exploitation. Secondly, the idea of selective ensemble is adopted to select an appropriate strategy in the current stage with the aid of the priority roulette selection method. In addition, the modified boundary processing mechanism adjusts the transgressive sparrows’ locations. The random relocation method is for discoverers and alerters to conduct global search in a large range, and the relocation method based on the optimal and suboptimal of the population is for scroungers to conduct better local search. Finally, MSESSA is tested on CEC 2017 suites. The function test, Wilcoxon test, and ablation experiment results show that MSESSA achieves better comprehensive performance than 13 other advanced algorithms. In four engineering optimization problems, the stability, effectiveness, and superiority of MSESSA are systematically verified, which has significant advantages and can reduce the design cost.

DYNet: A Printed Book Detection Model Using Dual Kernel Neural Networks

“…In the target classification stage, the algorithm classifies these candidate regions and gives the location and category of the target. Generally, selective search algorithms [ 16 ] or some improved algorithms (such as SQR [ 16 ], RetinaRPN [ 17 ], and MCG [ 18 ]) are used to generate candidate regions that may contain targets. However, selective search algorithms require a significant amount of time and computational resources and generate a large number of candidate regions, and most candidate regions are redundant, which can affect the accuracy of target detection.…”