Abstract
Rainfall Optimization Algorithm (RFO) is a nature-inspired metaheuristic optimization algorithm. RFO mimics the movement of water drops generated during rainfall to optimize a function. The paper study new implementations for RFO to offer more reliable results. Moreover, it studies three restarting techniques that can be applied to the algorithm with multithreading. The different implementations for the RFO are benchmarked to test and verify the performance and accuracy of the solutions. The paper presents and compares the results using several multidimensional testing functions, as well as the visual behavior of the raindrops inside the benchmark functions. The results confirm that the movement of the artificial drops corresponds to the natural behavior of raindrops. The results also show the effectiveness of this behavior to minimize an optimization function and the advantages of parallel computing restarting techniques to improve the quality of the solutions.
Highlights
The optimal solution for a specific function can be obtained with different methods
Some application models are represented with functions that could take a lot of time to find their global optimums with the traditional methods, and that is why researches started to develop new optimization algorithms
These visual results show that the random distribution that set the initial positions of the drops, and how they move inside the search space towards the coordinates of the global minimum
Summary
The optimal solution for a specific function can be obtained with different methods. People started to research new methods to find global maximums and minimums without using traditional algorithms. Researchers have found that heuristicbased algorithms could find the global optimum for highly complex functions in less time than the traditional way. In the real world, the efficiency and precision of the algorithm are crucial. These algorithms are needed in many different fields, like physics, engineering, computer science, industrial processes, demography, and economics. Some application models are represented with functions that could take a lot of time to find their global optimums with the traditional methods, and that is why researches started to develop new optimization algorithms
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