Search

In recent years, significant attentions have been devoted to design of metaheuristic optimization algorithms in order to solve optimization problems. Metaheuristic optimizers are methods which are inspired by observing the phenomena occurring in nature. In this paper, a comprehensive and exhaustive review has been carried out on water cycle algorithm (WCA) and its applications in a wide variety of study fields. The WCA is one of the novel metaheuristic optimization algorithms which is inspired by water cycle process in nature and how streams and rivers flow into the sea. Good exploitation and exploration capabilities have made the WCA a good alternative for solving large-scale optimization problems. Due to its capabilities and strengths, the WCA has been utilized in many and various majors including mechanical engineering, electrical and electronic engineering, civil engineering, industrial engineering, water resources and hydropower engineering, computer engineering, mathematics, and so forth. A variety of articles based on WCA have been published in different international journals such as Elsevier, Springer, IEEE Transactions, Wiley, Taylor & Francis, and in the proceedings of international conferences as well, since 2012 to the present. Thus, it is highly believed that this paper can be appropriate, beneficial and practical for students, academic researchers, professionals, and engineers. Also, it can be an innovative and comprehensive reference for subsequent academic papers and books relevant to the WCA, optimization methods, and metaheuristic optimization algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the water cycle algorithm (WCA), a recently developed metaheuristic method is proposed for solving multi-objective optimization problems (MOPs). The fundamental concept of the WCA is inspired by the observation of water cycle process, and movement of rivers and streams to the sea in the real world. Several benchmark functions have been used to evaluate the performance of the WCA optimizer for the MOPs. The obtained optimization results based on the considered test functions and comparisons with other well-known methods illustrate and clarify the robustness and efficiency of the WCA and its exploratory capability for solving the MOPs. [ABSTRACT FROM AUTHOR]

Classification is achieved through the categorisation of objects into predefined categories or classes, where the categories or classes are created based on a similar set of attributes of the object. This is referred to as supervised learning. Numerous methodologies have been formulated by researchers in order to solve classification problems effectively. These methodologies exhibit an uncomplicated structure and fast training, and are based on artificial intelligence, such as the probabilistic neural network (PNN). In this study, techniques to improve the accurateness of the PNN in solving classification problems have been analysed with the help of the water cycle algorithm (WCA), which is a population-based metaheuristic that imitates the water cycle in the real world. In the recommended solution, near-optimal solutions are created in order to regulate the arbitrary parameter selection of the PNN. In this study, it has also been suggested that the enhanced WCA (E-WCA) can be used to attain a balance between exploitation and exploration, so that premature conjunction and immobility of the population can be avoided. With the help of 11 standard benchmark datasets, the recommended solutions were verified. The results of the experiment substantiated that the WCA and E-WCA are capable of improving the weight parameters of the PNN, thereby imparting improved performance with respect to convergence speed and classification accuracy, compared with the initial PNN classifier. [ABSTRACT FROM AUTHOR]