Mashwani, Wali KhanRehman, Zia UrBakar, Maharani A.Kocak, IsmailFayaz, Muhammad2025-01-212025-01-2120211076-27871099-0526https://doi.org/10.1155/2021/5515701https://hdl.handle.net/20.500.12587/24046Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms (EAs) belong to nature-inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well-known 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-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity.eninfo:eu-repo/semantics/openAccessArticle202110.1155/2021/55157012-s2.0-85103768915Q1WOS:000641789800001Q2