The performance comparison of a new version of artificial raindrop algorithm on global numerical optimization

Abstract

Very recently, a new metaheuristic called artificial raindrop algorithm (ARA) was proposed. This search algorithm inspired from the phenomenon of natural rainfall, including the generation of raindrop, the descent of raindrop, the collision of raindrop, the flowing of raindrop and the updating of vapor. However, the original ARA only focused on the changing process of a raindrop. In this paper, we present an extension of ARA (ARAE) to more raindrops without any major conceptual change to its structure. In the proposed ARAE, all vapors are dynamically divided into several small-sized groups according to the relative distance of vapors in each generation. Each vapor has an associated weight proportion to the fitness value. The raindrop in the corresponding group is then generated based on weighted mean of the current vapors positions. But beyond that, some operators of ARA are further modified to enhance its exploration/exploitation capabilities. In order to thoroughly evaluate the performance of ARAE, a comprehensive comparative study has been carried on the CEC2005 contest benchmark functions. The obtained results indicate that ARAE has overall better performance than ARA, and is very competitive with respect to other twenty-four state-of-the-art original intelligent optimization algorithms and twenty-four improved metaheuristic algorithms. Finally, the proposed ARAE is also applied to artificial neural networks and the promising results on the nonlinear function approximation show the applicability of ARAE for problem solving.