Various popular multiplayer battle royale games share a lot of common elements. Drawing from our observations, we summarized these shared characteristics and subsequently proposed a novel heuristic algorithm named multiplayer battle game-inspired optimizer (MBGO). The proposed MBGO streamlines mainstream multiplayer battle royale games into two discrete phases: movement and battle. Specifically, the movement phase incorporates the principles of commonly encountered “safe zones” to incentivize participants to relocate to areas with a higher survival potential. The battle phase simulates a range of strategies players adopt in various situations to enhance the diversity of the population. To evaluate and analyze the performance of the proposed MBGO, we executed it alongside ten other algorithms, including three classics and five latest ones, across multiple diverse dimensions within the CEC2017 and CEC2020 benchmark functions. In addition, we employed several industrial design problems to evaluate the scalability and practicality of the proposed MBGO. The statistical analysis results reveal that the novel MBGO demonstrates significant competitiveness, excelling in convergence speed and achieving high levels of convergence accuracy across both benchmark functions and real-world problems.

各种流行的多人大逃杀游戏有很多共同的元素。根据我们的观察，我们总结了这些共同特征，随后提出了一种新颖的启发式算法，称为多人战斗游戏启发优化器（MBGO）。拟议的 MBGO 将主流多人大逃杀游戏简化为两个独立的阶段：移动和战斗。具体来说，移动阶段结合了常见的“安全区”原则，以激励参与者迁移到具有更高生存潜力的区域。战斗阶段模拟玩家在各种情况下采取的一系列策略，以增强人口的多样性。为了评估和分析所提出的 MBGO 的性能，我们在 CEC2017 和 CEC2020 基准函数中的多个不同维度上将其与其他十种算法一起执行，包括三种经典算法和五种最新算法。此外，我们还采用了几个工业设计问题来评估所提出的 MBGO 的可扩展性和实用性。统计分析结果表明，新颖的MBGO表现出显着的竞争力，在基准函数和现实问题上都具有出色的收敛速度和高水平的收敛精度。