Nature is a wonderful source of inspiration for developing optimization techniques that can tackle difficult problems in science and engineering. Since the early 1970s, various nature-inspired optimization algorithms have emerged starting with the Genetic Algorithm (GA) have been proposed and successfully implemented in different applications. However, because each algorithm possesses strengths and weaknesses, there is no single method within the family of nature-inspired numerical optimization algorithms that stands out as the best for solving all types of problems. Therefore, hybrid algorithms have been presented to balance the overall exploration and exploitation ability to improve the convergence capability of the optimization techniques.
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