Abstract

Metaheuristic optimization algorithms have demonstrated strong performance when applied to complex nonlinear optimization tasks. However, their performance often degrades in high- dimensional and multimodal settings due to premature convergence and insufficient global search. To address these limitations, an Adversarial Game Optimization Algorithm (AGOA) is proposed, which constructs a metaheuristic optimization framework based on adversarial game mechanisms.

元启发式优化算法在求解复杂非线性优化任务时表现出强大性能，但在高维环境下性能往往下降。本文提出对抗博弈优化算法（AGOA），借鉴博弈论思想构建高效元启发式框架，并应用于多阈值图像分割、约束工程设计以及无人机三维路径规划等实际问题。

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