Need to find multiple solutions to a complex problem? This research presents a novel multimodal optimization algorithm, the Multi-modal Battle Royal Optimization (MBRO), designed to identify multiple local and global optima without requiring prior knowledge of the problem space. The algorithm offers a significant advancement in solving real-world optimization challenges. Building on the existing Battle Royal Optimization (BRO) algorithm, MBRO uses Coulomb's law to identify suitable neighbors and formulate multimodal solutions. This approach allows the algorithm to identify potential optima based on fitness values and establish optimal distances from solution candidates. Compared against seven well-known multimodal optimization algorithms, MBRO demonstrates superior proficiency in identifying most, if not all, local and global optima across 14 benchmark problems. Its ability to navigate complex problem spaces makes it a promising solution for a wide range of applications, and is well suited for mathematical modeling and computer science applications.
Cluster Computing focuses on parallel and distributed computing, including algorithms and applications. This paper fits the journal's scope by introducing a novel optimization algorithm designed for multimodal problems, a key area in cluster computing. The comparative study and experimental results presented contribute valuable insights to the field.