Abstract
Nowadays, wide-ranging meta-heuristic optimization methods have grown in popularity because of varied natural events. They have mostly considered biological systems like colonials’ behavior. A novel meta-heuristic optimization method, called synthetic raindrop algorithm (SRA), is presented for global numerical optimization in this study. As it comes from its name, it derives from the natural phenomenon of rainfall. This algorithm utilizes five operators to depict the changing process of a raindrop, including raindrop generation, raindrop descent, raindrop collision, raindrop flowing and raindrop updating. Numerical experiments are performed on all the CEC2005 contest benchmark functions to evaluate this algorithm performance. The achieved results indicate that SRA is competitive with eight other avant-grade original intelligent optimization algorithms and their developed ones.
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