Battle royale optimizer with a new movement strategy

TL;DR

This paper introduces a modified battle royale optimizer (M-BRO) with an added movement operator to enhance exploration and exploitation balance, demonstrating improved performance on benchmark functions without increasing complexity.

Contribution

The paper proposes a new movement strategy for BRO that improves optimization performance without adding extra parameters or complexity.

Findings

M-BRO outperforms original BRO on benchmark functions.

M-BRO performs competitively against six other optimization algorithms.

No additional parameters are needed for the proposed modification.

Abstract

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.