Self-learning Sparrow Search Algorithm with Escape Mechanism
LIU Pengliang
LU Quan
Abstract：A self-learning sparrow search algorithm with escape mechanism is proposed to address the problem that the spar-row search algorithm is prone to search stagnation in the late iteration.First,the SPM chaotic mapping rule is used for population ini-tialization to improve the quality of population initialization.Then,the golden sine algorithm is used to improve the position update rule of the discoverer to solve the problem of smaller search dimension,and at the same time,individuals are instructed to self-learn so that the position information of the previous generation can be fully utilized to improve the solution speed.Finally,the escape mechanism is used to scatter the rest of the search space for the individuals that occur aggregation to improve the population diversity and avoid search stagnation in the algorithm.Simulation experiments on the benchmark test function show that the algo-rithm proposed in this paper solves the problem that the sparrow search algorithm is prone to search stagnation in the late iteration,which proves the effectiveness of this algorithm.
Keywords：sparrow search algorithmself-learning strategiesescape mechanismSPM chaos
Publication Date：2023-12-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 2769-2773 )