大逃杀优化与新的运动策略
Battle royale optimizer with a new movement strategy
摘要：
基于游戏的是一种新的随机元分析优化类别，其灵感来自传统或数字游戏类型。与基于SI的算法不同，内分区算法并不以击败其他个体并赢得比赛为目标。Battle royale optimizer（BRO）是最近提出的一种基于博弈的元启发式优化算法，用于求解连续问题。本文提出了一种改进的BRO（M-BRO），以改善勘探和开发之间的平衡。为此，在移动战略中使用了一个额外的移动算子。此外，所提出的方法不需要额外的参数。此外，这种改进的算法的复杂度是相同的，原来的。实验在一组19个（单峰和多峰）基准函数（CEC 2010）上进行。将所提出的方法与原始BRO以及六种众所周知/最近提出的优化算法进行了比较。结果表明，与原始的BRO和其他竞争对手相比，带有额外移动算子的BRO在解决复杂数值优化问题时表现得很好。
英文摘要：
Gamed-based is a new stochastic metaheuristics optimization category that is inspired by traditional or digital game genres. Unlike SI-based algorithms, in-dividuals do not work together with the goal of defeating other individuals and winning the game. Battle royale optimizer (BRO) is a Gamed-based me-taheuristic optimization algorithm that has been recently proposed for the task of continuous problems. This paper proposes a modified BRO (M-BRO) in order to improve balance between exploration and exploitation. For this matter, an additional movement operator has been used in the movement strategy. Moreover, no extra parameters are required for the proposed ap-proach. Furthermore, the complexity of this modified algorithm is the same as the original one. Experiments are performed on a set of 19 (unimodal and multimodal) benchmark functions (CEC 2010). The proposed method has been compared with the original BRO alongside six well-known/recently proposed optimization algorithms. The results show that BRO with additional movement operator performs well to solve complex numerical optimization problems compared to the original BRO and other competitors.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
