A Novel Meta-Heuristic Edge Server Placement Algorithm for Improving Service Quality

Edge server placement (ESP) is a critical determinant of service quality in edge–cloud computing systems, yet existing solutions often neglect the inherent collaboration between edge and cloud, leading to suboptimal performance under dynamic workloads. To address this gap, this paper proposes a novel meta-heuristic edge server placement algorithm based on the Coati Optimization Algorithm (COA). We first formulate the ESP problem as a constrained binary nonlinear programming model that explicitly incorporates edge–cloud collaboration, aiming to minimize the average request processing delay. The proposed COA-based solver features a compact one-dimensional encoding scheme that simultaneously represents server placement and request offloading decisions, a tailored boundary correction mechanism to enforce coverage and atomicity constraints, and a balanced exploration–exploitation strategy inspired by coatis’ natural hunting and escape behaviors. Extensive simulations are conducted, comparing the proposed algorithm against ten representative heuristic and meta-heuristic algorithms, including GA, PSO, DE, GWO, and their variants. The experimental results demonstrate that our algorithm significantly outperforms all compared methods in terms of the mean, minimum, and standard deviation of the overall average processing delay. Specifically, it achieves a 98.2% reduction in the mean delay relative to suboptimal algorithms while maintaining near-zero variance, confirming its effectiveness, efficiency, and robustness. The proposed algorithm provides a promising solution for service providers to enhance quality of service through optimal edge server deployment and request offloading under edge–cloud collaboration.