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Efficient multiplayer battle game optimizer for numerical optimization and adversarial robust neural architecture search

delete2025-02-01
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PRE
AI
R
Rui Zhong
Y
Yuefeng Xu
C
Chao Zhang
J
Jun Yu *
DOI:10.1016/j.aej.2024.11.035delete
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Abstract

Abstract

En 中文
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 & eacute;vy 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. Asa 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 https: //github.com/RuiZhong961230/EMBGO.
Keywords:
Metaheuristic algorithm (MA)
Multiplayer battle game optimizer (MBGO)
Differential mutation
L & eacute
vy flight
Adversarial robust neural architecture search
(ARNAS)

Journal

Alexandria Engineering Journal cover
Alexandria Engineering Journal
IF:
6.8
Papers:
6.3K
Citations:
2.6W

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University of Toyama
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Niigata University
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Hokkaido University
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