Journal
NEURAL COMPUTING & APPLICATIONS
Volume 33, Issue 4, Pages 1139-1157Publisher
SPRINGER LONDON LTD
DOI: 10.1007/s00521-020-05004-4
Keywords
Battle royale; Optimization; Swarm intelligence; Metaheuristic
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This study introduces a new metaheuristic optimization algorithm BRO inspired by the battle royale game genre. Experimental results on 19 benchmark optimization functions and the inverse kinematics problem show that the proposed algorithm is competitive and efficient in terms of convergence and accuracy.
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.
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