A new approach of the rain-fall optimization algorithm using parallelization
Guerrero Valadez, Juan Manuel
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This chapter introduces a new implementation of the Rain-Fall Optimization Algorithm (RFO) proposed by Kaboli, Sevbaraj, and Rahim in “Rain-Fall Optimization Algorithm. A Population-Based Algorithm for Solving Constrained Optimization Problems” by Kaboli et al. (J Comput Sci 19:31–42, 2017). RFO is a nature-inspired algorithm, which is based on the behavior of the water drops produced by a rainfall going down through a mountain to find the minimum values of specific functions. The algorithm was tested on four multidimensional benchmark functions: Ackley, Griewank, Rosenbrock, and Sphere functions. It was also tested in a four-dimensional function, the Kowalik function. The first step was to match the results of the rewritten algorithm with the results obtained by the original authors. Then the algorithm had to be modified to make some efficiency improvements and to get better results. The main modifications were a new equation to modify the step size for a function called explosion process and a parallel execution of the algorithm with two different restarting techniques: restart to the best and genetic restart to the best. © Springer Nature Switzerland AG 2020.
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