论文标题
通过新机芯策略进行战斗皇家优化器
Battle royale optimizer with a new movement strategy
论文作者
论文摘要
基于GAMED的是一种新的随机元启发式优化类别，其灵感来自传统或数字游戏类型。与基于SI的算法不同，个人无法与击败其他人并赢得比赛的目标合作。 Battle Royale Optimizer（BRO）是一种基于同类的ME-Taheuristic优化算法，最近已提出了针对连续问题的任务。本文提出了一个修改后的BRO（M-BRO），以提高探索和剥削之间的平衡。为此，在运动策略中使用了另一个运动操作员。此外，提出的AP-frache不需要额外的参数。此外，该修改算法的复杂性与原始算法相同。实验是在一组（单峰和多模式）基准函数上进行的（CEC 2010）。已提出的方法已与原始BRO与六种著名/最近提出的优化算法进行了比较。结果表明，与原始BRO和其他竞争对手相比，与其他运动操作员相比，BRO效果很好，可以解决复杂的数值优化问题。
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.
