Recently, several metaheuristic optimization approaches have been developed for solving many complex problems in various areas. Most of these optimization algorithms are inspired by nature or the social behavior of some animals. However, there is no optimization algorithm which has been inspired by a game. In this paper, a novel metaheuristic optimization algorithm, named BRO (battle royale optimization), is proposed. The proposed method is inspired by a genre of digital games knowns as “battle royale.” BRO is a population-based algorithm in which each individual is represented by a soldier/player that would like to move toward the safest (best) place and ultimately survive. The proposed scheme has been compared with the well-known PSO algorithm and six recent proposed optimization algorithms on nineteen benchmark optimization functions. Moreover, to evaluate the performance of the proposed algorithm on real-world engineering problems, the inverse kinematics problem of the 6-DOF PUMA 560 robot arm is considered. The experimental results show that, according to both convergence and accuracy, the proposed algorithm is an efficient method and provides promising and competitive results.

最近，已经开发了几种元启发式优化方法来解决各个领域中的许多复杂问题。这些优化算法大多数是受自然界或某些动物的社交行为启发的。但是，没有受游戏启发的优化算法。本文提出了一种新颖的元启发式优化算法，称为BRO（皇家保卫战）。提出的方法是受一类称为“皇家大战”的数字游戏的启发。BRO是一种基于人口的算法，其中每个人都由一个士兵/玩家代表，他们希望朝最安全（最好）的地方移动并最终生存。该提议的方案已与著名的PSO算法和六个最新提议的优化算法进行了19种基准优化功能的比较。此外，为了评估所提出算法在实际工程问题上的性能，考虑了6自由度PUMA 560机械臂的逆运动学问题。实验结果表明，从收敛性和准确性两方面来看，该算法是一种有效的方法，具有很好的竞争效果。