Abstract

Coyote Optimization Algorithm (COA) has demonstrated efficient performance by utilizing the multiple pack (subpopulation) mechanism. However, the fixed number of packs and a relatively singular evolutionary strategy limit its comprehensive optimization performance. Thus, this paper proposes a COA variant, referred to as the Optimization State-based Coyote Optimization Algorithm (OSCOA).

郊狼优化算法（COA）通过利用多包（子种群）机制展现高效性能，但固定的包数量与相对固定的机制存在局限。本文提出基于优化状态的郊狼优化算法（EOSCOA），改进种群状态估计机制，并应用于多阈值图像分割与无线传感器网络部署问题。

Keywords