Improved Egret Swarm Optimization Algorithm with mixed multi-strategy for system evaluation

To address the inherent limitations of the conventional Egret Swarm Optimization Algorithm (ESOA), this study proposes an enhanced variant, namely the improved egret swarm optimization algorithm (IESOA), which integrates a hybrid multi-strategy framework. Initially, the piecewise chaotic mapping is employed for population initialization. This approach enables a more flexible distribution of egret individuals within the initial solution space, thereby enhancing population diversity and mitigating the risk of the algorithm converging to local optima. Subsequently, an adaptive T-distribution method is introduced to update the hunting positions of egret squads. This modification strengthens the algorithms capability to escape local optima and improves its global search performance. Furthermore, a sine–cosine strategy is incorporated to dynamically adjust the search direction and control step size variations during the optimization process, which significantly accelerates the convergence speed. Experimental results demonstrate that IESOA exhibits robust performance in escaping local optima, achieves rapid convergence, and maintains relatively high optimization accuracy.