A Novel Sparrow Search Scheme Based on Enhanced Differential Evolution Operator

The sparrow search algorithm (SSA) represents a novel approach within swarm intelligence optimization, introduced in recent years. Renowned for its minimal control parameters and straightforward implementation, the SSA algorithm has swiftly emerged as one of the most proficient and attractive optimization methodologies. Despite its significant accuracy in solutions and rapid convergence, the SSA is hampered by its underperformance in tackling intricate optimization challenges and an imbalanced distribution between exploration and exploitation capabilities. Consequently, this paper proposes a dynamic two-factor sparrow search algorithm based on an enhanced differential evolution operator (DSSADE). Initially, to harmonize both the global and local search capabilities and enhance the convergence speed of the SSA algorithm, a dynamic two-factor mode is put forward. Subsequently, a tolerance mechanism is devised to gauge the algorithm's likelihood of gravitating towards local optima. Furthermore, an embedded enhanced differential evolution operator (EDE) fortifies the SSA's capacity to escape local optima. Notably, experimental results obtained from both classical and CEC2017 benchmark functions exhibit substantial confirmation and endorse the superior efficacy of DSSADE compared to various SSA variants and other cutting-edge swarm intelligent algorithms.