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In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound\u2010constrained benchmark functions and various real\u2010world problems. In general, the developed evolutionary algorithms (EAs) belong to nature\u2010inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well\u2010known EAs and has secured top ranks in most of the EA competitions in the special session of the IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty\u2010nine large\u2010scale bound\u2010constrained benchmark functions. The suggested C\u2010DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. 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