Over the past decade, Wireless Mesh Networks (WMNs) have seen significant advancements due to their simple deployment, cost-effectiveness, ease of implementation and reliable service coverage. However, despite these advantages, the placement of nodes in WMNs presents a critical challenge that significantly impacts their performance. This issue is recognized as an NP-hard problem, underscoring the necessity of development optimization algorithms, such as heuristic and metaheuristic approaches. This motivates us to develop the Maximum Entropy Genetic Algorithm (MEGA) to address the issue of mesh router node placement in WMNs. To assess the proposed method, we conducted experiments across various scenarios with different settings, focusing on key metrics such as network connectivity and user coverage. The simulation results show a comparison of MEGA with other prominent algorithms, such as the Coyote Optimization Algorithm (COA), Firefly Algorithm (FA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), revealing MEGA's effectiveness and usability in determining optimal locations for mesh routers.

翻译：在过去十年中，无线Mesh网络（WMNs）因其部署简单、成本效益高、易于实施和服务覆盖可靠而取得了显著进展。然而，尽管具有这些优势，WMNs中的节点部署仍是一个关键挑战，对其性能产生重大影响。该问题被公认为NP难问题，这凸显了开发优化算法（如启发式和元启发式方法）的必要性。这促使我们开发最大熵遗传算法（MEGA）来解决WMNs中网状路由器节点的部署问题。为评估所提方法，我们在不同设置的各种场景下进行了实验，重点关注网络连通性和用户覆盖率等关键指标。仿真结果将MEGA与郊狼优化算法（COA）、萤火虫算法（FA）、遗传算法（GA）和粒子群优化（PSO）等其他主流算法进行了比较，揭示了MEGA在确定网状路由器最优位置方面的有效性和实用性。