  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.
