This paper introduces a novel metaheuristic algorithm, known as the efficient multiplayer battle game optimizer (EMBGO), specifically designed for addressing complex numerical optimization tasks. The motivation behind this research stems from the need to rectify identified shortcomings in the original MBGO, particularly in search operators during the movement phase, as revealed through ablation experiments. EMBGO mitigates these limitations by integrating the movement and battle phases to simplify the original optimization framework and improve search efficiency. Besides, two efficient search operators: differential mutation and L\'evy flight are introduced to increase the diversity of the population. To evaluate the performance of EMBGO comprehensively and fairly, numerical experiments are conducted on benchmark functions such as CEC2017, CEC2020, and CEC2022, as well as engineering problems. Twelve well-established MA approaches serve as competitor algorithms for comparison. Furthermore, we apply the proposed EMBGO to the complex adversarial robust neural architecture search (ARNAS) tasks and explore its robustness and scalability. The experimental results and statistical analyses confirm the efficiency and effectiveness of EMBGO across various optimization tasks. As a potential optimization technique, EMBGO holds promise for diverse applications in real-world problems and deep learning scenarios. The source code of EMBGO is made available in \url{https://github.com/RuiZhong961230/EMBGO}.

翻译：本文提出了一种新颖的元启发式算法，即高效多人对战游戏优化器（EMBGO），专门用于解决复杂的数值优化任务。该研究的动机源于通过消融实验揭示的原版MBGO在移动阶段搜索算子中存在的缺陷。EMBGO通过整合移动和战斗阶段来简化原始优化框架并提高搜索效率，从而缓解了这些局限性。此外，引入了两种高效搜索算子：差分变异和Lévy飞行，以增加种群的多样性。为全面、公正地评估EMBGO的性能，在CEC2017、CEC2020和CEC2022等基准函数以及工程问题上进行了数值实验。选取12种成熟的元启发式算法作为对比算法。此外，我们将所提出的EMBGO应用于复杂的对抗鲁棒神经架构搜索（ARNAS）任务，并探索其鲁棒性和可扩展性。实验结果和统计分析证实了EMBGO在各种优化任务中的效率和有效性。作为一种潜在的优化技术，EMBGO在现实问题和深度学习场景中具有广泛应用的潜力。EMBGO的源代码已发布于\url{https://github.com/RuiZhong961230/EMBGO}。