A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems

Bound‐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.