  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}.
